This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Supershoes are reshaping distance running

Since 2016, when Nike introduced the Vaporfly, a paradigm-­shifting shoe that helped athletes run more efficiently (and therefore faster), the elite running world has muddled through a period of soul-searching over the impact of high-tech footwear on the sport.

“Supershoes” —which combine a lightweight, energy-­returning foam with a carbon-fiber plate for stiffness—have been behind every broken world record in distances from 5,000 meters to the marathon since 2020.

To some, this is a sign of progress. In much of the world, elite running lacks a widespread following. Record-breaking adds a layer of excitement. And the shoes have benefits beyond the clock: most important, they help minimize wear on the body and enable faster recovery from hard workouts and races.

Still, some argue that they’ve changed the sport too quickly. Read the full story. 

—Jonathan W. Rosen

This story is from the forthcoming print issue of MIT Technology Review, which explores the theme of Play. It’s set to launch tomorrow, so if you don’t already, subscribe now to get a copy when it lands.

Why China’s dominance in commercial drones has become a global security issue

Whether you’ve flown a drone before or not, you’ve probably heard of DJI, or at least seen its logo. With more than a 90% share of the global consumer market, this Shenzhen-based company’s drones are used by hobbyists and businesses alike for everything from photography to spraying pesticides to moving parcels.

But on June 14, the US House of Representatives passed a bill that would completely ban DJI’s drones from being sold in the US. The bill is now being discussed in the Senate as part of the annual defense budget negotiations. 

To understand why, you need to consider the potential for conflict between China and Taiwan, and the fact that the military implications of DJI’s commercial drones have become a top policy concern for US lawmakers. Read the full story.

—Zeyi Yang

This story is from China Report, our weekly newsletter covering tech in China. Sign up to receive it in your inbox every Tuesday.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 The EU has issued antitrust charges against Microsoft 
For bundling Teams with Office—just a day after it announced similar charges against Apple. (WSJ $) 
+ It seems likely it’ll be hit with a gigantic fine. (Ars Technica)
+ The EU has new powers to regulate the tech sector, and it’s clearly not afraid to use them. (FT $)

2 OpenAI is delaying launching its voice assistant 
 (WP $)
+ It’s also planning to block access in China—but plenty of Chinese companies stand ready to fill the void. (Mashable)

3 Deepfake creators are re-victimizing sex trafficking survivors
Non-consensual deepfake porn is proliferating at a terrifying pace—but this is the grimmest example I’ve seen. (Wired $)
+ Three ways we can fight deepfake porn. (MIT Technology Review)

4 Chinese tech company IPOs are a rarity these days
It’s becoming very hard to avoid the risk of it all being derailed by political scrutiny, whether at home or abroad. (NYT $)
+ Global chip company stock prices have been on a rollercoaster ride recently, thanks to Nvidia. (CNBC)

5 Why AI is not about to replace journalism
It can crank out content, sure—but it’s incredibly boring to read. (404 Media)
+ After all the hype, it’s no wonder lots of us feel ever-so-slightly disappointed by AI. (WP $)
+ Despite a troubled launch, Google’s already extending AI Summaries to Gmail as well as Search. (CNET) 

6 This week of extreme weather is a sign of things to come
Summers come with a side-serving of existential dread now, as we all feel the effects of climate change. (NBC)
+ Scientists have spotted a worrying new tipping point for the loss of ice sheets in Antarctica. (The Guardian) 

7 Inside the fight over lithium mine expansion in Argentina 
Indigenous communities had been divided in opposition—but as the cash started flowing, cracks started appearing. (The Guardian)
+ Lithium battery fires are a growing concern for firefighters worldwide. (WSJ $)

8 What even is intelligent life?
We value it, but it’s a slippery concept that’s almost impossible to define. (Aeon) 
+ What an octopus’s mind can teach us about AI’s ultimate mystery. (MIT Technology Review)

9 Tesla is recalling most Cybertrucks… for the fourth time 
You have to laugh, really. (The Verge) 
+ Luckily, it’s not sold that many of them anyway. (Quartz $)

10 The trouble with Meta’s “smart” Ray Bans 
Well… basically they’re just not very smart. At all. (Wired $)

Quote of the day

“We’re making the biggest bet in AI. If transformers go away, we’ll die. But if they stick around, we’re the biggest company of all time.”

—Fighting talk to CNBC from Gavin Uberti, cofounder and CEO of a two-year-old startup called Etched, which believes its AI-optimized chips could take on Nvidia’s near-monopoly.

The big story

This nanoparticle could be the key to a universal covid vaccine

COURTESY OF WELLCOME LEAP, CALTECH, AND MERKIN INSTITUTE

September 2022
Long before Alexander Cohen—or anyone else—had heard of the alpha, delta, or omicron variants of covid-19, he and his graduate school advisor Pamela Bjorkman were doing the research that might soon make it possible for a single vaccine to defeat the rapidly evolving virus—along with any other covid-19 variant that might arise in the future.

The pair and their collaborators are now tantalizingly close to achieving their goal of manufacturing a vaccine that broadly triggers an immune response not just to covid and its variants but to a wider variety of coronaviruses. Read the full story.

—Adam Piore

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)

+ Happy 80th Birthday to much beloved Muswell Hillbilly Ray Davies, frontman of the Kinks.
+ Need to cool your home down? Plants can help!
+ Well, uh, that’s certainly one way to cope with a long-haul flight. 
+ Glad to know I’m not the only person obsessed with Nongshim instant noodles. 

This story first appeared in China Report, MIT Technology Review’s newsletter about technology in China. Sign up to receive it in your inbox every Tuesday.

Whether you’ve flown a drone before or not, you’ve probably heard of DJI, or at least seen its logo. With more than a 90% share of the global consumer market, this Shenzhen-based company’s drones are used by hobbyists and businesses alike for photography and surveillance, as well as for spraying pesticides, moving parcels, and many other purposes around the world.  

But on June 14, the US House of Representatives passed a bill that would completely ban DJI’s drones from being sold in the US. The bill is now being discussed in the Senate as part of the annual defense budget negotiations. 

The reason? While its market dominance has attracted scrutiny for years, it’s increasingly clear that DJI’s commercial products are so good and affordable they are also being used on active battlefields to scout out the enemy or carry bombs. As the US worries about the potential for conflict between China and Taiwan, the military implications of DJI’s commercial drones are becoming a top policy concern.

DJI has managed to set the gold standard for commercial drones because it is built on decades of electronic manufacturing prowess and policy support in Shenzhen. It is an example of how China’s manufacturing advantage can turn into a technological one.

“I’ve been to the DJI factory many times … and mainly, China’s industrial base is so deep that every component ends up being a fraction of the cost,” Sam Schmitz, the mechanical engineering lead at Neuralink, wrote on X. Shenzhen and surrounding towns have had a robust factory scene for decades, providing an indispensable supply chain for a hardware industry like drones. “This factory made almost everything, and it’s surrounded by thousands of factories that make everything else … nowhere else in the world can you run out of some weird screw and just walk down the street until you find someone selling thousands of them,” he wrote.

But Shenzhen’s municipal government has also significantly contributed to the industry. For example, it has granted companies more permission for potentially risky experiments and set up subsidies and policy support. Last year, I visited Shenzhen to experience how it’s already incorporating drones in everyday food delivery, but the city is also working with companies to use drones for bigger and bigger jobs—carrying everything from packages to passengers. All of these go into a plan to build up the “low-altitude economy” in Shenzhen that keeps the city on the leading edge of drone technology.

As a result, the supply chain in Shenzhen has become so competitive that the world can’t really use drones without it. Chinese drones are simply the most accessible and affordable out there. 

Most recently, DJI’s drones have been used by both sides in the Ukraine-Russia conflict for reconnaissance and bombing. Some American companies tried to replace DJI’s role, but their drones were more expensive and their performance unsatisfactory. And even as DJI publicly suspended its businesses in Russia and Ukraine and said it would terminate any reseller relationship if its products were found to be used for military purposes, the Ukrainian army is still assembling its own drones with parts sourced from China.

This reliance on one Chinese company and the supply chain behind it is what worries US politicians, but the danger would be more pronounced in any conflict between China and Taiwan, a prospect that is a huge security concern in the US and globally.

Last week, my colleague James O’Donnell wrote about a report by the think tank Center for a New American Security (CNAS) that analyzed the role of drones in a potential war in the Taiwan Strait. Right now, both Ukraine and Russia are still finding ways to source drones or drone parts from Chinese companies, but it’d be much harder for Taiwan to do so, since it would be in China’s interest to block its opponent’s supply. “So Taiwan is effectively cut off from the world’s foremost commercial drone supplier and must either make its own drones or find alternative manufacturers, likely in the US,” James wrote.

If the ban on DJI sales in the US is eventually passed, it will hit the company hard for sure, as the US drone market is currently worth an estimated $6 billion, the majority of which is going to DJI. But undercutting DJI’s advantage won’t magically grow an alternative drone industry outside China. 

“The actions taken against DJI suggest protectionism and undermine the principles of fair competition and an open market. The Countering CCP Drones Act risks setting a dangerous precedent, where unfounded allegations dictate public policy, potentially jeopardizing the economic well-being of the US,” DJI told MIT Technology Review in an emailed statement.

The Taiwanese government is aware of the risks of relying too much on China’s drone industry, and it’s looking to change. In March, Taiwan’s newly elected president, Lai Ching-te, said that Taiwan wants to become the “Asian center for the democratic drone supply chain.” 

Already the hub of global semiconductor production, Taiwan seems well positioned to grow another hardware industry like drones, but it will probably still take years or even decades to build the economies of scale seen in Shenzhen. With support from the US, can Taiwanese companies really grow fast enough to meaningfully sway China’s control of the industry? That’s a very open question.

A housekeeping note: I’m currently visiting London, and the newsletter will take a break next week. If you are based in the UK and would like to meet up, let me know by writing to zeyi@technologyreview.com.

Now read the rest of China Report

Catch up with China

1. ByteDance is working with the US chip design company Broadcom to develop a five-nanometer AI chip. This US-China collaboration, which should be compliant with US export restrictions, is rare these days given the political climate. (Reuters $)

2. After both the European Union and China announced new tariffs against each other, the two sides agreed to chat about how to resolve the dispute. (New York Times $)

• Canada is preparing to announce its own tariffs on Chinese-made electric vehicles. (Bloomberg $)

3. A NASA leader says the US is “on schedule” to send astronauts to the moon within a few years. There’s currently a heated race between the US and China on moon exploration. (Washington Post $)

4. A new cybersecurity report says RedJuliett, a China-backed hacker group, has intensified attacks on Taiwanese organizations this year. (Al Jazeera $)

5. The Canadian government is blocking a rare earth mine from being sold to a Chinese company. Instead, the government will buy the stockpiled rare earth materials for $2.2 million. (Bloomberg $)

6. Economic hardship at home has pushed some Chinese small investors to enter the US marijuana industry. They have been buying lands in the States, setting up marijuana farms, and hiring other new Chinese immigrants. (NPR)

Lost in translation

In the past week, the most talked-about person in China has been a 17-year-old girl named Jiang Ping, according to the Chinese publication Southern Metropolis Daily. Every year since 2018, the Chinese company Alibaba has been hosting a global mathematics contest that attracts students from prestigious universities around the world to compete for a generous prize. But to everyone’s surprise, Jiang, who’s studying fashion design at a vocational high school in a poor town in eastern China, ended up ranking 12th in the qualifying round this year, beating scores of college undergraduate or even master’s students. Other than reading college mathematics textbooks under her math teacher’s guidance, Jiang has received no professional training, as many of her competitors have.

Jiang’s story, highlighted by Alibaba following the announcement of the first-round results, immediately went viral in China. While some saw it as a tale of buried talents and how personal endeavor can overcome unfavorable circumstances, others questioned the legitimacy of her results. She became so famous that people, including social media influencers, kept visiting her home, turning her hometown into an unlikely tourist destination. The town had to hide Jiang from public attention while she prepared for the final round of the competition.

One more thing

After I wrote about the new Chinese generative video model Kling last week, the AI tool added a new feature that can turn a static photo into a short video clip. Well, what better way to test its performance than feeding it the iconic “distracted boyfriend” meme and watching what the model predicts will happen after that moment?

Update: The story has been updated to include a statement from DJI.

Stijn Lemmens has a cleanup job like few others. A senior space debris mitigation analyst at the European Space Agency (ESA), Lemmens works on counteracting space pollution by collaborating with spacecraft designers and the wider industry to create missions less likely to clutter the orbital environment. 

Although significant attention has been devoted to launching spacecraft into space, the idea of what to do with their remains has been largely ignored. Many previous missions did not have an exit strategy. Instead of being pushed into orbits where they could reenter Earth’s atmosphere and burn up, satellites were simply left in orbit at the ends of their lives, creating debris that must be monitored and, if possible, maneuvered around to avoid a collision. “For the last 60 years, we’ve been using [space] as if it were an infinite resource,” Lemmens says. “But particularly in the last 10 years, it has become rather clear that this is not the case.” 

Engineering the ins and outs: Step one in reducing orbital clutter—or, colloquially, space trash—is designing spacecraft that safely leave space when their missions are complete. “I thought naïvely, as a student, ‘How hard can that be?’” says Lemmens. The answer turned out to be more complicated than he expected. 

At ESA, he works with scientists and engineers on specific missions to devise good approaches. Some incorporate propulsion that works reliably even decades after launch; others involve designing systems that can move spacecraft to keep them from colliding with other satellites and with space debris. They also work on plans to get the remains through the atmosphere without large risks to aviation and infrastructure.

Standardizing space: Earth’s atmosphere exerts a drag on satellites that will eventually pull them out of orbit. National and international guidelines recommend that satellites lower their altitude at the end of their operational lives so that they will reenter the atmosphere and make this possible. Previously the goal was for this to take 25 years at most; Lemmens and his peers now suggest five years or less, a time frame that would have to be taken into account from the start of mission planning and design. 

Explaining the need for this change in policy can feel a bit like preaching, Lemmens says, and it’s his least favorite part of the job. It’s a challenge, he says, to persuade people not to think of the vastness of space as “an infinite amount of orbits.” Without change, the amount of space debris may create a serious problem in the coming decades, cluttering orbits and increasing the number of collisions.  

Shaping the future: Lemmens says his wish is for his job to become unnecessary in the future, but with around 11,500 satellites and over 35,000 debris objects being tracked, and more launches planned, that seems unlikely to happen. 

Researchers are looking into more drastic changes to the way space missions are run. We might one day, for instance, be able to dismantle satellites and find ways to recycle their components in orbit. Such an approach isn’t likely to be used anytime soon, Lemmens says. But he is encouraged that more spacecraft designers are thinking about sustainability: “Ideally, this becomes the normal in the sense that this becomes a standard engineering practice that you just think of when you’re designing your spacecraft.”

The United States has an official web design system and a custom typeface. This public design system aims to make government websites not only good-looking but accessible and functional for all.

Before the internet, Americans may have interacted with the federal government by stepping into grand buildings adorned with impressive stone columns and gleaming marble floors. Today, the neoclassical architecture of those physical spaces has been (at least partially) replaced by the digital architecture of website design—HTML code, tables, forms, and buttons. 

While people visiting a government website to apply for student loans, research veterans’ benefits, or enroll in Medicare might not notice these digital elements, they play a crucial role. If a website is buggy or doesn’t work on a phone, taxpayers may not be able to access the services they have paid for—which can create a negative impression of the government itself.  

There are about 26,000 federal websites in the US. Early on, each site had its own designs, fonts, and log-in systems, creating frustration for the public and wasting government resources. The troubled launch of Healthcare.gov in 2013 highlighted the need for a better way to build government digital services. In 2014, President Obama created two new teams to help improve government tech.

Within the General Services Administration (GSA), a new team called 18F (named for its office at 1800 F Street in Washington, DC) was created to “collaborate with other agencies to fix technical problems, build products, and improve public service through technology.” The team was built to move at the speed of tech startups rather than lumbering bureaucratic agencies. 

The US Digital Service (USDS) was set up “to deliver better government services to the American people through technology and design.” In 2015, the two teams collaborated to build the US Web Design System (USWDS), a style guide and collection of user interface components and design patterns intended to ensure accessibility and a consistent user experience across government websites. “Inconsistency is felt, even if not always precisely articulated in usability research findings,” Dan Williams, the USWDS program lead, said in an email. 

Today, the system defines 47 user interface components such as buttons, alerts, search boxes, and forms, each with design examples, sample code, and guidelines such as “Be polite” and “Don’t overdo it.” Now in its third iteration, it is used in 160 government websites. “As of September 2023, 94 agencies use USWDS code, and it powers about 1.1 billion page views on federal websites,” says Williams.

To ensure clear and consistent typography, the free and open-source typeface Public Sans was created for the US government in 2019. “It started as a design experiment,” says Williams, who designed the typeface. “We were interested in trying to establish an open-source solution space for a typeface, just like we had for the other design elements in the design system.”

The teams behind Public Sans and the USWDS embrace transparency and collaboration with government agencies and the public.

And to ensure that the hard-learned lessons aren’t forgotten, the projects embrace continuous improvement. One of the design principles behind Public Sans offers key guidance in this area: “Strive to be better, not necessarily perfect.”

Jon Keegan writes Beautiful Public Data, a newsletter that curates visually interesting data sets collected by local, state, and federal government agencies
(beautifulpublicdata.com).

The philosopher Karl Popper once argued that there are two kinds of problems in the world: clock problems and cloud problems. As the metaphor suggests, clock problems obey a certain logic. They are orderly and can be broken down and analyzed piece by piece. When a clock stops working, you’re able to take it apart, look for what’s wrong, and fix it. The fix may not be easy, but it’s achievable. Crucially, you know when you’ve solved the issue because the clock starts telling the time again. 

W.W. NORTON, 2024

Cloud problems offer no such assurances. They are inherently complex and unpredictable, and they usually have social, psychological, or political dimensions. Because of their dynamic, shape-shifting nature, trying to “fix” a cloud problem often ends up creating several new problems. For this reason, they don’t have a definitive “solved” state—only good and bad (or better and worse) outcomes. Trying to repair a broken-down car is a clock problem. Trying to solve traffic is a cloud problem.  

Engineers are renowned clock-problem solvers. They’re also notorious for treating every problem like a clock. Increasing specialization and cultural expectations play a role in this tendency. But so do engineers themselves, who are typically the ones who get to frame the problems they’re trying to solve in the first place. 

In his latest book, Wicked Problems, Guru Madhavan argues that the growing number of cloudy problems in our world demands a broader, more civic-minded approach to engineering. “Wickedness” is Madhavan’s way of characterizing what he calls “the cloudiest of problems.” It’s a nod to a now-famous coinage by Horst Rittel and Melvin Webber, professors at the University of California, Berkeley, who used the term “wicked” to describe complex social problems that resisted the rote scientific and engineering-based (i.e., clock-like) approaches that were invading their fields of design and urban planning back in the 1970s. 

Madhavan, who’s the senior director of programs at the National Academy of Engineering, is no stranger to wicked problems himself. He’s tackled such daunting examples as trying to make prescription drugs more affordable in the US and prioritizing development of new vaccines. But the book isn’t about his own work. Instead, Wicked Problems weaves together the story of a largely forgotten aviation engineer and inventor, Edwin A. Link, with case studies of man-made and natural disasters that Madhavan uses to explain how wicked problems take shape in society and how they might be tamed.

Link’s story, for those who don’t know it, is fascinating—he was responsible for building the first mechanical flight trainer, using parts from his family’s organ factory—and Madhavan gives a rich and detailed accounting. The challenges this inventor faced in the 1920s and ’30s—which included figuring out how tens of thousands of pilots could quickly and effectively be trained to fly without putting all of them up in the air (and in danger), as well as how to instill trust in “instrument flying” when pilots’ instincts frequently told them their instruments were wrong—were among the quintessential wicked problems of his time. 

To address a world full of wicked problems, we’re going to need a more expansive and inclusive idea of what engineering is and who gets to participate in it.

Unfortunately, while Link’s biography and many of the interstitial chapters on disasters, like Boston’s Great Molasses Flood of 1919, are interesting and deeply researched, Wicked Problems suffers from some wicked structural choices. 

The book’s elaborate conceptual framework and hodgepodge of narratives feel both fussy and unnecessary, making a complex and nuanced topic even more difficult to grasp at times. In the prologue alone, readers must bounce from the concept of cloud problems to that of wicked problems, which get broken down into hard, soft, and messy problems, which are then reconstituted in different ways and linked to six attributes—efficiency, vagueness, vulnerability, safety, maintenance, and resilience—that, together, form what Madhavan calls a “concept of operations,” which is the primary organizational tool he uses to examine wicked problems.

It’s a lot—or at least enough to make you wonder whether a “systems engineering” approach was the correct lens through which to examine wickedness. It’s also unfortunate because Madhavan’s ultimate argument is an important one, particularly in an age of rampant solutionism and “one neat trick” approaches to complex problems. To effectively address a world full of wicked problems, he says, we’re going to need a more expansive and inclusive idea of what engineering is and who gets to participate in it.  

MIT PRESS, 2024

While John Downer would likely agree with that sentiment, his new book, Rational Accidents, makes a strong argument that there are hard limits to even the best and broadest engineering approaches. Similarly set in the world of aviation, Downer’s book explores a fundamental paradox at the heart of today’s civil aviation industry: the fact that flying is safer and more reliable than should technically be possible.

Jetliners are an example of what Downer calls a “catastrophic technology.” These are “complex technological systems that require extraordinary, and historically unprecedented, failure rates—of the order of hundreds of millions, or even billions, of operational hours between catastrophic failures.”

Take the average modern jetliner, with its 7 million components and 170 miles’ worth of wiring—an immensely complex system in and of itself. There were over 25,000 jetliners in regular service in 2014, according to Downer. Together, they averaged 100,000 flights every single day. Now consider that in 2017, no passenger-carrying commercial jetliner was involved in a fatal accident. Zero. That year, passenger totals reached 4 billion on close to 37 million flights. Yes, it was a record-setting year for the airline industry, safety-wise, but flying remains an almost unfathomably safe and reliable mode of transportation—even with Boeing’s deadly 737 Max crashes in 2018 and 2019 and the company’s ongoing troubles. 

Downer, a professor of science and technology studies at the University of Bristol, does an excellent job in the first half of the book dismantling the idea that we can objectively recognize, understand, and therefore control all risk involved in such complex technologies. Using examples from well-known jetliner crashes, as well as from the Fukushima nuclear plant meltdown, he shows why there are simply too many scenarios and permutations of failure for us to assess or foresee such risks, even with today’s sophisticated modeling techniques and algorithmic assistance.

So how does the airline industry achieve its seemingly unachievable record of safety and reliability? It’s not regulation, Downer says. Instead, he points to three unique factors. First is the massive service experience the industry has amassed. Over the course of 70 years, manufacturers have built tens of thousands of jetliners, which have failed (and continue to fail) in all sorts of unpredictable ways. 

This deep and constantly growing data set, combined with the industry’s commitment to thoroughly investigating each and every failure, lets it generalize the lessons learned across the entire industry—the second key to understanding jetliner reliability. 

Finally is what might be the most interesting and counterintuitive factor: Downer argues that the lack of innovation in jetliner design is an essential but overlooked part of the reliability record. The fact that the industry has been building what are essentially iterations of the same jetliner for 70 years ensures that lessons learned from failures are perpetually relevant as well as generalizable, he says. 

That extremely cautious relationship to change flies in the face of the innovate-or-die ethos that drives most technology companies today. And yet it allows the airline industry to learn from decades of failures and continue to chip away at the future “failure performance” of jetliners.

The bad news is that the lessons in jetliner reliability aren’t transferable to other catastrophic technologies. “It is an irony of modernity that the only catastrophic technology with which we have real experience, the jetliner, is highly unrepresentative, and yet it reifies a misleading perception of mastery over catastrophic technologies in general,” writes Downer.

For instance, to make nuclear reactors as reliable as jetliners, that industry would need to commit to one common reactor design, build tens of thousands of reactors, operate them for decades, suffer through thousands of catastrophes, slowly accumulate lessons and insights from those catastrophes, and then use them to refine that common reactor design.  

This obviously won’t happen. And yet “because we remain entranced by the promise of implausible reliability, and implausible certainty about that reliability, our appetite for innovation has outpaced our insight and humility,” writes Downer. With the age of catastrophic technologies still in its infancy, our continued survival may very well hinge not on innovating our way out of cloudy or wicked problems, but rather on recognizing, and respecting, what we don’t know and can probably never understand.  

If Wicked Problems and Rational Accidents are about the challenges and limits of trying to understand complex systems using objective science- and engineering-based methods, Georgina Voss’s new book, Systems Ultra, provides a refreshing alternative. Rather than dispassionately trying to map out or make sense of complex systems from the outside, Voss—a writer, artist, and researcher—uses her book to grapple with what they feel like, and ultimately what they mean, from the inside.

VERSO, 2024

“There is something rather wonderful about simply feeling our way through these enormous structures,” she writes before taking readers on a whirlwind tour of systems visible and unseen, corrupt and benign, ancient and new. Stops include the halls of hype at Las Vegas’s annual Consumer Electronics Show (“a hot mess of a Friday casual hellscape”), the “memetic gold mine” that was the container ship Ever Given and the global supply chain it broke when it got stuck in the Suez Canal, and the payment systems that undergird the porn industry. 

For Voss, systems are both structure and behavior. They are relational technologies that are “defined by their ability to scale and, perhaps more importantly, their peculiar relationship to scale.” She’s also keenly aware of the pitfalls of using an “experiential” approach to make sense of these large-scale systems. “Verbal attempts to neatly encapsulate what a system is can feel like a stoner monologue with pointed hand gestures (‘Have you ever thought about how electricity is, like, really big?’),” she writes. 

Nevertheless, her written attempts are a delight to read. Voss manages to skillfully unpack the power structures that make up, and reinforce, the large-scale systems we live in. Along the way, she also dispels many of the stories we’re told about their inscrutability and inevitability. That she does all this with humor, intelligence, and a boundless sense of curiosity makes Systems Ultra both a shining example of the “civic engagement as engineering” approach that Madhavan argues for in Wicked Problems, and proof that his argument is spot on. 

Bryan Gardiner is a writer based in Oakland, California.

In a November 1984 story for Technology Review, Carolyn Sumners, curator of astronomy at the Houston Museum of Natural Science, described how toys, games, and even amusement park rides could change how young minds view science and math. “The Slinky,” Sumners noted, “has long served teachers as a medium for demonstrating longitudinal (soundlike) waves and transverse (lightlike) waves.” A yo-yo can be used as a gauge (a “yo-yo meter”) to observe the forces on a roller coaster. Marbles employ mass and velocity. Even a simple ball offers insights into the laws of gravity.

While Sumners focused on physics, she was onto something bigger. Over the last several decades, evidence has emerged that childhood play can shape our future selves: the skills we develop, the professions we choose, our sense of self-worth, and even our relationships.

That doesn’t mean we should foist “educational” toys like telescopes or tiny toolboxes on kids to turn them into astronomers or carpenters. As Sumners explained, even “fun” toys offer opportunities to discover the basic principles of physics. 

According to Jacqueline Harding, a child development expert and author of The Brain That Loves to Play, “If you invest time in play, which helps with executive functioning, decision-making, resilience—all those things—then it’s going to propel you into a much more safe, secure space in the future.”

Sumners was focused mostly on hard skills, the scientific knowledge that toys and games can foster. But there are soft skills, too, like creativity, problem-­solving, teamwork, and empathy. According to Harding, the less structure there is to such play—the fewer rules and goals—the more these soft skills emerge.

“The kinds of playthings, or play activities, that really produce creative thought,” she says, “are natural materials, with no defined end to them—like clay, paint, water, and mud—so that there is no right or wrong way of playing with it.” 

Playing is by definition voluntary, spontaneous, and goal-free; it involves taking risks, testing boundaries, and experimenting. The best kind of play results in joyful discovery, and along the way, the building blocks of innovation and personal development take shape. But in the decades since Sumners wrote her story, the landscape of play has shifted considerably. Recent research by the American Academy of Pediatrics’ Council on Early Childhood suggests that digital games and virtual play don’t appear to confer the same developmental benefits as physical games and outdoor play. 

“The brain loves the rewards that are coming from digital media,” says Harding. But in screen-based play, “you’re not getting that autonomy.” The lack of physical interaction also concerns her: “It is the quality of human face-to-face interaction, body proximity, eye-to-eye gaze, and mutual engagement in a play activity that really makes a difference.”

Bill Gourgey is a science writer based in Washington, DC.

For children, play comes so naturally. They don’t have to be encouraged to play. They don’t need equipment, or the latest graphics processors, or the perfect conditions—they just do it. What’s more, study after study has found that play has a crucial role in childhood growth and development. If you want to witness the absolute rapture of creative expression, just observe the unstructured play of children.

So what happens to us as we grow older? Children begin to compete with each other by age four or five. Play begins to transform from something we do purely for fun into something we use to achieve status and rank ourselves against other people. We play to score points. We play to win. 

And with that, play starts to become something different. Not that it can’t still be fun and joyful! Even watching other people play will bring us joy. We enjoy watching other people play so much and get so much joy by proxy from watching their achievements that we spend massive amounts of money to do so. According to StubHub, the average price of a ticket to the Super Bowl this year was $8,600. The average price for a Super Bowl ad was a cool $7 million this year, according to Ad Age. 

This kind of interest doesn’t just apply to physical games. Video-game streaming has long been a mainstay on YouTube, and entire industries have risen up around it. Top streamers on Twitch—Amazon’s livestreaming service, which is heavily gaming focused—earn upwards of $100,000 per month. And the global market for video games themselves is projected to bring in some $282 billion in revenue this year. 

Simply put, play is serious business. 

There are fortunes to be had in making our play more appealing, more accessible, more fun. All of the features in this issue dig in on the enormous amount of research and development that goes into making play “better.”  

On our cover this month is executive editor Niall Firth’s feature on the ways AI is going to upend game development. As you will read, we are about to enter the Wild West—Red Dead or not—of game character development. How will games change when they become less predictable and more fully interactive, thanks to AI-driven nonplayer characters who can not only go off script but even continue to play with each other when we’re not there? Will these even be games anymore, or will we simply be playing around in experiences? What kinds of parasocial relationships will we develop in these new worlds? It’s a fascinating read. 

There is no sport more intimately connected to the ocean, and to water, than surfing. It’s pure play on top of the waves. And when you hear surfers talk about entering the flow state, this is very much the same kind of state children experience at play—intensely focused, losing all sense of time and the world around them. Finding that flow no longer means living by the water’s edge, Eileen Guo reports. At surf pools all over the world, we’re piping water into (or out of) deserts to create perfect waves hundreds of miles from the ocean. How will that change the sport, and at what environmental cost? 

Just as we can make games more interesting, or bring the ocean to the desert, we have long pushed the limits of how we can make our bodies better, faster, stronger. Among the most recent ways we have done this is with the advent of so-called supershoes—running shoes with rigid carbon-fiber plates and bouncy proprietary foams. The late Kelvin Kiptum utterly destroyed the men’s world record for the marathon last year wearing a pair of supershoes made by Nike, clocking in at a blisteringly hot 2:00:35. Jonathan W. Rosen explores the science and technology behind these shoes and how they are changing the sport, especially in Kenya. 

There’s plenty more, too. So I hope you enjoy the Play issue. We certainly put a lot of work into it. But of course, what fun is play if you don’t put in the work?

Thanks for reading,

Mat Honan

The track at Moi University’s Eldoret Town Campus doesn’t look like a facility designed for champions. Its surface is a modest mix of clay and gravel, and it’s 10 meters longer than the standard 400. Runners use a classroom chair to mark the start and finish. Yet it’s as good a place as any to spot the athletes who make Kenya the world’s greatest distance-running powerhouse. 

On a morning in January, nearly a hundred athletes, including Olympic medalists and winners of major marathons, have gathered here for “speedwork”: high-­intensity intervals that the best runners make look effortless. The track is packed with so much talent that it is easy to miss the man of the moment, a gangly runner in a turquoise shirt and thick-soled Nike shoes. In just over a year, Kelvin Kiptum had gone from virtual unknown to global phenom, running three of the seven fastest marathons in history and setting the official men’s world record, 2:00:35, in Chicago in October 2023. On this day, he was less than three months out from his next race, in Rotterdam, where he planned to try for something once unthinkable: completing the 26-mile, 385-yard event in less than two hours.

Although fans were left in awe by Kiptum’s Chicago triumph, not everyone celebrated the shoes that had propelled him to victory. Since 2016, when Nike introduced the Vaporfly, a paradigm-­shifting shoe that helped athletes run more efficiently (and therefore faster), the elite running world has muddled through a period of soul-searching over the impact of high-tech footwear on the sport. The Vaporfly was only the beginning. Today, most major brands offer multiple versions of the “supershoe”—a technology that combines a lightweight, energy-­returning foam with a carbon-fiber plate for stiffness. “Superspikes” based on a similar concept are now widely used on the track as well. Performances have adjusted accordingly. Since 2020, according to the sport’s governing body, World Athletics, runners wearing so-called advanced footwear technology have broken all road and outdoor track world records in distances from 5,000 meters to the marathon—a concentration unlike any in the sport’s modern history. 

The steady stream of footwear innovation has brought unending speculation over which brand’s shoes are best. Critics say that places too much emphasis on gear at the expense of runners’ ability.

Some of the most impressive feats have come in the marathon. In a 2019 exhibition that wasn’t eligible for records, Kenya’s Eliud Kipchoge covered the distance in an astonishing 1:59:40. Last September, Ethiopia’s Tigst Assefa lowered the women’s world record by more than two minutes in Berlin, running 2:11:53 in the ultralight Adidas Adizero Adios Pro Evo 1, a shoe designed to be worn only once. For his own record two weeks later, Kiptum wore the slightly heavier yet uber-bouncy Nike Alphafly 3. The uninitiated could have been forgiven for thinking the white platform shoes, which almost looked designed for walking on the moon, belonged on a sci-fi set rather than the streets of Chicago.

To some, this is all a sign of progress. In much of the world, elite running lacks a widespread following. Record-breaking adds a layer of excitement. And as I’d hear repeatedly from top athletes and coaches in Kenya, the shoes have benefits beyond the clock: most important, they help minimize wear on the body and enable faster recovery from hard workouts and races.

PATRICK MEINHARDT

Still, some argue that they’ve changed the sport too quickly. Not only has it become hard to compare new records fairly with old ones, but the steady stream of footwear innovation has brought unending speculation over which brand’s shoes are best, and critics say that places too much emphasis on gear at the expense of runners’ ability. Laboratory research also suggests that some runners get a greater boost from the technology than others, depending on their biomechanics. Ross Tucker, a South African sports scientist and outspoken supershoe critic, has argued that these differences make it effectively impossible to “evaluate performances between different athletes independent of this nagging doubt over what the shoes do.”

How much of Kiptum’s success was due to his talent, training, drive, and mental toughness—and how much to his body’s responsiveness to Nike’s tech? It’s difficult to know—and, tragically, he’s not around to offer input. A few weeks after I saw him in Eldoret, a city of several hundred thousand that serves as Kenya’s unofficial running capital, he and coach Gervais Hakizimana were killed in a late-night car crash en route to the nearby town they used as a base for training. 

Shoes were the last thing on the mind of Kenya’s running community in the wake of Kiptum’s death. Yet his dramatic rise offers a window into their significance. Although the shoe-tech revolution has affected runners the world over, in few places has its effect been more pronounced than Kenya, where running is not only a sport but an exit strategy from a life of poverty. In this sense, the new high-tech shoes are something of a mixed blessing, giving a boost to established runners with company sponsorships while forming an obstacle to those still pining for their big break. Even the cheapest models here sell for well over $100—no small sum for young people who mostly come from disadvantaged backgrounds.

Today most Kenyan athletes, whether beginners or household names with six-­figure shoe contracts, have come to accept that there’s no turning back—that even the most elemental of sports is not immune to scientific innovation. Still, the new shoes are transforming the sport in myriad ways, throwing new variables into training and racing, exacerbating inequalities between athletes, and altering the collective imagination of what performances are possible. They’re also writing a new, tech-fueled chapter to one of the sports world’s most unlikely tales: how a small corner of one African country became such a dominant force in running, and how running, in turn, became the stuff of dreams for so many of its youth. 

Engineered to Fly

Supershoes are carefully optimized to help runners go the distance

Beneath the boat-like exterior, supershoes boast a variety of features designed to lower the energetic cost of running, allowing athletes to go faster and help them endure the strain of a long-distance race.

The most crucial feature is the (often proprietary) foams that are used to construct parts of the sole. These absorb the impact of the foot and return energy from each foot strike back to the runner. Some use other features, like the orange “air pod” in the Nike Alphafly 3 (bottom), for an added bounce. 

Bounciness alone would not provide much advantage—today’s foams are so soft and thick (World Athletics allows up to 40 millimeters in competitions) that without additional support they would make the feet highly unstable. To give the shoes structure, manufacturers add rigid components like carbon-fiber plates or rods, typically sandwiched between layers of foam.

These rigid parts and foams are combined with wafer-thin mesh uppers to create shoes that are increasingly ultralight: the Adidas Adizero Adios Pro Evo 1 (top), released in 2023, weighs just 4.9 ounces (measured in the men’s size 9). Lighter shoes also reduce the energy expended with each stride—enabling runners to move at a given pace with less effort. 

A bounce in the step

To understand the impact of shoes on running performance, it’s helpful to think of the human body as a vehicle. In a long-­distance event like the marathon, competitors are limited by three physiological factors. VO₂ max, the maximum amount of oxygen the body can absorb, is akin to an engine’s horsepower—it effectively measures the upper limits of a runner’s aerobic capacity. Lactate threshold, the point at which lactic acid accumulates in the blood faster than the body can remove it, is like the redline on a dashboard tachometer—it tells you how close you can run to your VO₂ max without succumbing to exhaustion. The third parameter, running economy, describes the rate at which a runner expends energy, similar to gas mileage. A light, aerodynamic coupe will use less fuel, or energy, to travel at a given speed than a hulking SUV. So too will a lithe, efficiently striding marathoner.

It is running economy that’s affected by footwear—most obviously when it comes to weight. As a leg in stride moves through space, added weight closer to the end (i.e., the foot) has a greater energetic cost than weight closer to the center of gravity. Soles made with foams that are soft, or compliant (good at storing mechanical energy), and resilient (good at returning it) can also lead to significant energy savings. Studies have shown that shoes with stiffening elements, like plates, can improve running economy as well, by reducing the muscular effort of the feet.

PATRICK MEINHARDT

The trick, for shoe manufacturers, has long been to optimize these properties—and for much of competitive running’s history, they weren’t particularly good at it. As recently as the 1970s, shoes worn for racing had clunky rubber soles and stiff leather or canvas uppers—not so different from the O’Sullivan’s “Live Rubber Heels” that propelled the American Johnny Hayes to victory in the marathon at the 1908 Olympics, the first run at today’s standard distance. The 1975 release of the first shoe with a midsole made from ethylene vinyl acetate (EVA), an air-infused foam, heralded a new generation of footwear that was lighter and bouncier. With a few exceptions, innovations over the next four decades would focus on making EVA shoes as light as possible.

That all changed with the Vaporfly. After its release, most attention focused on its curved carbon-fiber plate, which many suspected functioned like a spring. Research has shown that to be incorrect: while the plate may add some energy-­saving stiffness, says Wouter Hoogkamer, a professor of kinesiology at the University of Massachusetts, Amherst, its main benefit appears to be in stabilizing the technology’s most vital component: a thick midsole material made from a foamed polymer known as polyether block amide, or PEBA. Not only is this foam light; tests in 2017 at Hoogkamer’s lab, then at the University of Colorado, Boulder, found that a Vaporfly prototype stored and returned significantly more energy than the leading marathon shoes at the time: the EVA-soled Nike Streak and the Adidas Boost, made with a thermoplastic polyurethane. Hoogkamer’s team also recruited 18 high-performing athletes and tracked their energy expenditure, measured in watts per kilogram of body weight, as they ran for five-minute bouts on a treadmill at different paces in all three. The Vaporfly, they found, improved running economy by an average of 4%—in part by increasing the amount of ground covered with each stride. More recent studies have found a slightly smaller benefit when comparing the Vaporfly and other supershoes with “control shoes” over short distances. However, preliminary data from a Brigham Young University study, which tested subjects during runs lasting an hour, suggests that supershoes may offer a greater running-­economy benefit as an athlete progresses through a race, in part because softer foams help reduce muscle fatigue. “A runner with a 3% running-economy benefit in the lab might be at 4% or 5% at the end of a marathon,” says Iain Hunter, a professor of biomechanics who led the research. 

PATRICK MEINHARDT

Although it’s widely accepted that better running economy translates into faster racing, the exact impact on elite performances is subject to uncertainty. At world-record marathon pace, statistical models predict, 4% better running economy would lower time by more than three minutes. But few runners and coaches I spoke with in Kenya believe the technology is worth that much, even as they acknowledge that it’s become essential to competing at the highest level. Many note that footwear has advanced alongside better marathon-specific training and new hydrogel-based sports drinks that make it possible to digest more calories during races. There’s also the scourge of doping: drug-related offenses had left 81 Kenyan athletes ineligible to compete in World Athletics events as of May 1, though Kipchoge has never tested positive, and neither had Kiptum.

From talent to big business

While Kenya’s runners are renowned today for their marathoning dominance, the country first emerged on the global stage in track races. The watershed moment came at the 1968 Mexico City Olympics, where Kenya won eight medals in track and field, including gold in the men’s 1,500 meters, 10,000 meters, and 3,000-meter steeplechase. For the next two decades, the country’s athletes largely shied away from the marathon: according to Moses Tanui, a Kenyan who won the Boston Marathon twice in the 1990s, many men believed the event would prevent them from fathering children. Eventually, though, as money shifted away from the track and toward the roads, the longer distance had greater allure. Today, the winner of a major race like Boston can expect a several-hundred-­thousand-dollar payday, between appearance fees, prize money, and shoe-company bonuses. As of May, according to World Athletics, Kenya-born athletes accounted for 28 of the event’s all-time 50 fastest men and 17 of its 50 fastest women.

Kenya’s outsize success is also closely linked to the concept of running economy. Studies of the Kalenjin, a community of nine closely related tribes that produce the majority of Kenya’s top athletes, point to several physical attributes more common in this group that are conducive to an energy-efficient gait, including thin lower legs, long Achilles tendons, and a high ratio of leg length to torso. Active childhoods in the highlands to the west of the Great Rift Valley, where altitudes between 6,000 and 9,000 feet help boost aerobic capacity, is likely a component of their success as well. It’s the prospect of financial rewards, though, that drives participation—and transforms raw talent into records. Although Kenya is one of Africa’s most industrialized countries, even top university graduates struggle to find well-paid jobs. In the villages and small towns of the Rift Valley region, where economic prospects are especially limited, many are drawn to running by default. “After high school, if you don’t continue with your studies, you can run or you can be idle,” says Brigid Kosgei, a Kenyan who held the women’s marathon world record before Assefa. “So you run—you try your best.”

It is in this context that the stakes of shoe technology are so high: in top competitions, places worth tens of thousands of dollars—representing new homes for parents and school fees for children—can come down to seconds. For a few years after Nike’s release of the Vaporfly, the odds were stacked against runners sponsored by other companies, whose contracts prevented them from using competitors’ products. The gap was partly psychological: Cyprian Kotut, an Adidas-sponsored runner who’s won marathons in Paris and Hamburg, recalls feeling disillusioned mid-race next to Nike-shod competitors. Some sought out workarounds. One cobbler in Ethiopia gained fame for his skill in attaching Vaporfly soles to Adidas uppers—thereby helping some Adidas runners stealthily utilize the Nike tech. 

“After high school, if you don’t continue with your studies, you can run or you can be idle … So you run—you try your best.”

Brigid Kosgei, Kenyan who held the women’s marathon world record

Today, the playing field is far more level—at least among established pros. At the 2 Running Club, an Adidas-sponsored camp set amid rolling tea fields south of Eldoret, Kotut and his teammates give me a glimpse of their Adizero carbon-fiber lineup. There’s the ultra-padded Prime X for long sessions on pavement; the more compact Takumi Sen for speedwork; one pair of the featherlight black-and-white Evo, which Kotut used to run a personal best of 2:04:34 last year in Amsterdam. Claudio Berardelli, the group’s Italian coach, estimates that his runners cover at least 60% of their mileage in supershoes. For most, they’ve become as vital to training as they have to racing. Not only do they enable faster workouts, says Benson Kipruto, a club member who won the Tokyo Marathon in March and finished second to Kiptum in Chicago last fall; the softer foams also promote quicker recovery—to the point where the day after a hard session, “your legs are a bit fresh.” 

Many credit the shoes with keeping runners healthy. David Kirui, a physiotherapist who’s treated many of Kenya’s top marathoners, estimates that overuse-related injuries, like stress fractures, Achilles tendinitis, and iliotibial band syndrome, are down at least 25%. Several veteran runners tell me the shoes have helped extend their careers, and therefore their earning power. “In the old shoes, after 10 marathons you’d be completely exhausted,” says Jonathan Maiyo, who’s been an elite road racer since 2007. “Now 10 marathons are like nothing.”

Who benefits?

Runners like those in Berardelli’s group are a chosen few. The majority of athletes training in Kenya have never made any money from the sport; many run in secondhand shoes gifted by friends or purchased in local markets, and few can afford supershoes of their own. One day in Iten, a small town north of Eldoret that clings to the edge of the Rift Valley escarpment, I meet Daisy Kandie, a 23-year-old who moved here after high school and is among the hundreds of aspiring pros who toil along the town’s clay roads each morning. Her goal is the same as most: get noticed by an agent, most likely a foreigner, who’ll provide gear, arrange races outside the country, and in some cases negotiate a contract with a shoe company.

PATRICK MEINHARDT

Although supershoe technology has raised the cost of doing business for Kandie and others like her, it’s most controversial for its role in skewing results at the very top. Hoogkamer’s landmark study of the Vaporfly, which found that the shoes improved running economy by 4% on average, also found that the benefit ranged from roughly 2% to 6% depending on the athlete. 

Subsequent research involving other supershoes has documented a similar range of responses. One 2023 study by Adidas-affiliated researchers, which tested seven elite Kenyans in three carbon-fiber prototypes and a traditional racing flat, recorded a runner using 11% less energy in one shoe and a runner using 11% more energy in another. Melanie Knopp, the study’s lead author, cautions that each athlete was tested in each shoe only once, and that some of the subjects were unfamiliar with running on a treadmill. Nonetheless, researchers generally agree that individual athletes “respond” to some shoes better than others. Why isn’t entirely clear: Hoogkamer estimates there may be 20 variables at play, including weight, foot length, calf muscle strength, and whether the runner strikes the ground with the forefoot, midfoot, or heel. Shoe geometry matters as well. Abdi Nageeye, a Dutch marathoner who trains in Iten and finished second to Kipchoge at the Tokyo Olympics, says he struggled with the first two versions of Nike’s Alphafly; as a 120-pound heel-striker, it forced him to “skip” in a way that felt unnatural. He says the newest Alphafly model, which has a greater drop in “stack height”—or foam thickness—from heel to toe, is a much better fit.

“If everybody is in their ideal shoe, are there still some people who’ll get more benefit than others? The answer is probably yes.”

Dustin Joubert, a supershoe expert and professor of kinesiology at St. Edward’s University in Austin, Texas

What all this means for the marathon’s integrity is a hotly debated topic. Today, many pro runners in the West undergo treadmill-based metabolic tests to determine which shoe works best, and in some cases which company to sign with. That’s less common in Kenya, where greater competition leaves athletes less room to negotiate. Among runners I spoke with, most of those with shoe contracts said their sponsor has a model they like, but it’s difficult to know if it’s their absolute best fit. Even if it is, some suspect that certain runners are better suited to the supershoe technology more broadly. “If everybody is in their ideal shoe, are there still some people who’ll get more benefit than others?” asks Dustin Joubert, a supershoe expert and professor of kinesiology at St. Edward’s University in Austin, Texas. “The answer is probably yes.”

PATRICK MEINHARDT

Despite the benefits his runners gain in training, Berardelli says the shoes have introduced “question marks”: in a marathon today, he says, it’s less clear than ever whether the winner is indeed the runner who’s the strongest or has the smartest racing tactics. Stephen Cherono, a Kenyan who competed for Qatar as Saif Saaeed Shaheen and held the world record in the 3,000-meter steeplechase from 2004 until it was broken with the aid of superspikes last year, believes World Athletics should have placed greater restrictions on the technology before it was too late: although the global body maintains limits on sole thickness and prohibits the use of shoes that aren’t made available for sale, these guidelines are meant to help steer innovation, not squelch it. Cherono tells me he’s a big fan of Formula 1, the global motor sport, but worries that running, in its focus on performance engineering, is becoming too much like it. “Too often the conversation is now about the shoe and not the person wearing it,” he says. 

What might have been

If there’s one thing supershoe advocates and critics can agree upon, it’s that Kelvin Kiptum operated on another level. His margin of victory in Chicago—nearly three and a half minutes—was so large that some joked second-place Kipruto had won the race for mortals. Like most runners in Kenya, Kiptum grew up in a farming family where money was tight. When he began training as a teenager, he often ran barefoot; occasionally, pros he tagged along with gave him shoes. Among them was Hakizimana, a Rwandan who trained near Kiptum’s home and took him on as a protégé when his own running began to falter. After a stint training to be an electrician, Kiptum began running full-time in 2018; four years later, in his marathon debut, he ran the third-fastest time in history. Atypically, in all three of his marathons, he ran the second half faster than the first—perhaps because Nike’s PEBA foam had helped “save” his legs, or perhaps because his training was so grueling. Most world-class Kenyan marathoners top out around 220 kilometers per week. According to Hakizimana, Kiptum would often run up to 280, or roughly a marathon’s distance every day.

PATRICK MEINHARDT

One month to the day after I watched Kiptum circling the Eldoret track, completing 1,000-meter repeats at roughly the pace of a two-hour marathon, I gather with hundreds of others on a property he’d purchased outside town, where he is being buried according to Kalenjin tradition. The crowd again includes a who’s-who list of champions; this time, instead of running gear, they are dressed in suits or black T-shirts emblazoned with the record-­holder’s image. Their mourning is both for a man who died far too young—Kiptum was listed as 24, though he was likely at least a few years older—and for a remarkable performance that many had expected to be just around the corner. Entering Chicago, Kiptum had been dealing with an injury and wasn’t even in top shape, according to his training partner Daniel Kemboi. Ahead of Rotterdam, Kemboi says, “he was so confident.” Very few in Eldoret doubted he would shatter the two-hour barrier. 

At some point that afternoon, my mind drifts to the shoes. Kiptum had been an extraordinary competitor regardless of what was on his feet. Still, absent supershoe technology, the prospect of a sub-two-hour marathon would never have been part of his dramatic rags-to-riches story. In this sense, the shoes didn’t minimize his greatness, as critics like Cherono feared; if anything, they helped build his brand and turbocharged his pursuit of the Kenyan running dream—of achieving a better life through sport. Tragically, Kiptum’s path was cut short when he was only getting started. But someone else, in rigid shoes with bouncy soles, will come along to blaze their own. 

Jonathan W. Rosen is a journalist who writes about Africa. He reported from Eldoret with assistance from Godfrey Kiprotich.

Etienne Boulter walked into his lab at the Université Côte d’Azur in Nice, France, one morning with a Lego Technic excavator set tucked under his arm. His plan was simple yet ambitious: to use the pieces of the set to build a mechanical cell stretcher. 

Boulter is one of many researchers turning to Lego components to build inexpensive yet extremely effective lab equipment. The bricks themselves are durable and manufactured with tight tolerances. Lego’s offerings include sensors that can detect various colors, perceive rotational motion, and measure the distance to an object. These DIY tools are a creative and affordable solution for working scientists who are trying to keep costs down. 

ELIZABETH FERNANDEZ

Take, for example, the Lego chromatographer designed by Cassandra Quave and her husband, Marco Caputo, both at Emory University. Quave is an ethnobotanist who leads a research group dedicated to documenting traditional medicines. Her team travels deep into forests and jungles around the world, collecting samples of leaves, berries, and seeds that they evaluate for their potential pharmaceutical value. To isolate chemical compounds from the plant samples, Quave makes use of a meticulous process called chromatography, in which liquid distilled from the plant is passed over a tube filled with a material such as a silica gel. 

Timing in chromatography needs to be very exact, with small amounts of liquid being added at precise moments. Waiting for these moments is not the best use of a graduate student’s time. This is exactly what Quave thought when she walked into the lab one day and saw her PhD student Huaqiao Tang holding a test tube and watching the clock. “This is crazy!” Quave said, laughing. “We can come up with a better solution!” 

When Quave told Caputo of her problem, he brought in Legos culled from their four children’s massive collection and had his students see what they could do with them. They came up with a robotic arm that could make repeated precise movements, gradually adding small fractions of liquid to test tubes in order to isolate compounds within the plant tissue. The device was so accurate in its movements, Quave says, that spontaneous crystals formed, something that occurs only in very pure substances. 

Ethnobotanist Cassandra Quave distills molecules from plants using a Lego chromatographer that she designed with her husband, researcher Marco Caputo.

At Cardiff University in Wales, Christopher Thomas, Oliver Castell, and Sion Coulman had similar success building an instrument capable of printing cells. The researchers study skin diseases, lipids (fatty compounds) in the body, and wound healing. Ethically obtained samples are hard to find, so they created a 3D bioprinter out of Lego pieces that is capable of “printing” a human skin analogue, laying down layers of bio-ink that contains living cells. These printers normally cost over a quarter of a million dollars, but they built their version for a mere $550. At first, their colleagues were skeptical that components typically treated as toys could be used in such a professional setting, but after seeing the printer at work, they were quickly convinced. The team made national news, and other groups replicated the model in their own labs. 

COURTESY OF CARDIFF UNIVERSITY

Some scientists are devising tools to take into the classroom. Timo Betz of the University of Göttingen in Germany came up with the idea of building a Lego microscope one day while watching his son, Emil, then eight, play. Betz was scheduled to speak about science at a local school that afternoon but was reluctant to take his own lab-grade microscope into the classroom. His son was immediately on board. “Let’s do this!” he told his dad. Together with Bart Vos, a colleague at the university, they built a microscope that consisted entirely of Lego pieces, with the exception of two optical lenses. Their plans, which they’ve made available to the public, can be used by students as young as 12 to learn the basic concepts of optics.

COURTESY OF TIMO BETZ

Many of these scientists make their models open source, providing them to interested groups or publishing the plans on GitHub or in papers or so that other labs can make their own versions. This is great for researchers the world over, especially those with limited funding—whether they’re new faculty members, scientists at smaller universities, or people working in low-income countries. It’s how a small plastic brick is making science more accessible to all. 

Elizabeth Fernandez is a freelance science writer.

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

It feels weird, talking to yourself online. 

Especially when you’re pretty much the most unpleasant character you’ve ever met.

The “me” I’ve been chatting to this week, called King Fiall of Nirth, is a creation made using technology from Inworld AI, a US-based firm that hopes to revolutionize how we interact with characters in games. Specifically, Inworld is targeting the NPCs (nonplayer characters) that populate many modern video games. As a player, you can sometimes chat with these animated characters to discover new missions or quests to go on. More often they provide atmosphere or a sense of bustle in the game’s cities, castles, villages, and spaceports.

They’re also pretty dumb.

That’s why Inworld is hoping to leverage the power of generative AI to imbue NPCs with the power to chat freely with players, engaging them in unconstrained conversations that—it hopes—should give these open-world games a deeper, more immersive feel. I wrote about Inworld’s plan in the cover story of the current print magazine, which you can read here. 

I didn’t create King Fiall myself, of course. I’m not a total narcissist. No, instead I asked MIT Technology Review’s UK team (I’m based in our London office) to get around a laptop one lunchtime and build my personality as if I were an NPC. They accessed Inworld’s Studio, a tool that gives game developers a huge amount of freedom in creating the ”brains” of a prospective character, which can then be imported into a game engine. (At the moment I’m just a disembodied chatbot rather than a fully fleshed-out NPC.) My colleagues were to use the tools—including sliding “personality” scales and text boxes— to shape my character. No holds barred, I instructed. Let rip.

Well, um, that was a mistake.

The character they created is—and there’s really no easy way to say this—a monster.

Sarcastic to an extreme degree, King Fiall has been given the character of “ancient king” (an in-joke based on a BuzzFeed-style “Which House of the Dragon character are you?” quiz in which I got King Viserys, the fantasy world’s aging patriarch.) Fiall has an absolutely insufferable manner and—for some reason—an obsession with drinking the medieval beverage mead. Is this who they think I am?

In Studio, developers can also customize safety settings, controlling how much the character curses or talks about violence or alcohol, for example. The settings can include “goals”—things that you, the developer, want to push the player toward: a particular quest, for example. The idea is that developers can tweak these characters so they fit in the game world, and they can be set to “remember” interactions they’ve had with you before. 

“The character still responds with the magic of the generative AI, with the voice. You can say anything to it and they can respond in any way,” says Kylan Gibbs, Inworld’s CEO and founder. “But it’ll still follow that main arc and make sure that it hits the key beats that are set out.”

All right. What shall I tell Algorithm readers about AI NPCs next? I ask the King.

*King of Nirth leans in, a mischievous twinkle in his eye* Think of it, my dear, these AI NPCs can be whatever you want them to be. They can be your loyal subjects, your trusted advisors, or even your loyal opposition. They can help create immersive worlds, provide entertainment, or even assist in educating the masses. But be warned, my dear. These AI NPCs, they have a mind of their own. So keep a close eye on them, or they might just stage a coup and take over your kingdom, too!

Um, did he just call me “my dear”?

Anyway, ignore him. Inworld AI has a site, called Arcade, where you can play around with a bunch of its stock characters (including something called CyberSquirrel). It’s similar to the library of characters hosted by the startup Character.AI, but smaller in scale. On Character.AI’s site, you can chat with a huge selection of user-generated chatbots that are powered by LLMs, including historical figures, characters from films or books, or chatbots designed to help you learn a language. Users can tweak their own chatbot’s output as much as they like, fine-tuning its personality by typing text into a box.

Character.AI’s cofounder Daniel de Freitas told me that the firm’s vision is “broader than entertainment”—which was definitely a theme I noticed when writing my story. Beyond just making games more immersive, researchers and artists are now looking at ways these NPCs can be used in education, for example. De Freitas sees AI chatbots becoming personal assistants for your day-to-day life. “That means when you’re bored, or you want to wind down, we can give you entertainment—for example, these interactive stories,” he says. ”But then when you’re at school, we can help you study for your exam.”

I can’t see King Fiall being much use to me, other than raising my blood pressure. Fed up with his unending sass, I convince a teammate to give me the password to Studio’s back end and go snooping around my personality. 

A look at the dials shows that my colleagues have placed me more toward the “introvert” end of the sliding scale, while also leaning more positive than negative. The backstory they gave me describes my love of poetry (yes) and sandwiches (yes) and lists a series of utterances (Hmm, Go for it, Go away) that are apparently very typical (allegedly) of my usual day-to-day style.

Aha, but the dialogue style is set to “sarcastic.” Well, that makes sense. (I note, sadly, that “commanding,” “empathetic,” and “entertaining” were all available and disregarded.)

I ask King Fiall if he can please dial back the sarcasm a bit—it’s getting on my nerves. 

“Of course, dear visitor. I shall tone down the sarcasm and don my friendliest and most open demeanor for you.*King of Nirth puts on an exaggerated smile and gestures with open arms.* Welcome to the kingdom of warmth and sunshine, where sarcasm is but a distant memory.”

God, I’m really starting to hate this guy.

Now read the rest of the Algorithm

Why does AI hallucinate?

The tendency to make things up—known as hallucination—is one of the biggest obstacles holding chatbots back from more widespread adoption. So why do they do it? And why can’t we fix it?

One of the biggest issues is that it’s hallucination all the way down—that’s what LLMs do. It’s how they work, and we only call it “hallucination” when we notice it’s wrong. The problem is, large language models are so good that what they make up looks right most of the time. And that makes trusting them hard. 

Perhaps the best fix for hallucination is to manage our expectations about what these tools are for. 

Read this terrific explainer all about hallucinations from Will Douglas Heaven. It also appears in the next issue of MIT Technology Review, which lands on Wednesday and is packed with brilliant stories about the topic of play. Subscribe now, if you don’t already, so you can read the whole thing!

LinkedIn Live: Deepfakes

Join MIT Technology Review reporters and editors for a fascinating discussion on LinkedIn Live about the rise of deepfakes, including the risks they pose and some interesting positive uses. You can register for free here.

Bits and bytes

Synthesia’s deepfakes now come with hands—and soon will have full bodies

Bit by bit, these hyperrealistic avatars are becoming indistinguishable from the real thing. Read this story to see a video of Melissa’s old avatar having a conversation with a new version that includes hands. It’s quite surreal and genuinely impressive. (MIT Technology Review)

 A first look at China’s buzzy new text-to-video AI model 

The Chinese firm Kuaishou just dropped the first text-to-video generative AI model that’s freely available for the public to test (OpenAI’s Sora is still being kept under wraps). It’s called Kling, and our reporter got a chance to try it out. (MIT Technology Review)

Neo-Nazis are all in on AI

Unsurprising but awful news. Extremists are developing their own hateful AIs to supercharge radicalization and fundraising—and are now using the tech to make blueprints for weapons and bombs. (Wired)

Ilya Sutskever has a new AI firm. And it’s all about superintelligence.

A month after he quit OpenAI, its former chief scientist has a new firm called Safe Superintelligence. It won’t be making products—just focusing entirely on, yes, superintelligence. (FT)

• Read our deep dive interview with Ilya here. (MIT Technology Review)

These copywriters lost their jobs to AI

And to add insult to injury, they now have to help make the AIs that took their jobs sound more human. (BBC)

AI has turned Google image search into a total nightmare

Some search results are turning up AI-generated images of celebrities in swimsuits, but with a horrible twist: they look like underage children. (404 Media)

Startup Synthesia’s AI-generated avatars are getting an update to make them even more realistic: They will soon have bodies that can move, and hands that gesticulate.

The new full-body avatars will be able to do things like sing and brandish a microphone while dancing, or move from behind a desk and walk across a room. They will be able to express more complex emotions than previously possible, like excitement, fear, or nervousness, says Victor Riparbelli, the company’s CEO. Synthesia intends to launch the new avatars toward the end of the year. 

“It’s very impressive. No one else is able to do that,” says Jack Saunders, a researcher at the University of Bath, who was not involved in Synthesia’s work. 

The full-body avatars he previewed are very good, he says, despite small errors such as hands “slicing” into each other at times. But “chances are you’re not really going to be looking that close to notice it,” Saunders says. 

Synthesia launched its first version of hyperrealistic AI avatars, also known as deepfakes, in April. These avatars use large language models to match expressions and tone of voice to the sentiment of spoken text. Diffusion models, as used in image- and video-generating AI systems, create the avatar’s look. However, the avatars in this generation appear only from the torso up, which can detract from the otherwise impressive realism. 

To create the full-body avatars, Synthesia is building an even bigger AI model. Users will have to go into a studio to record their body movements.

But before these full-body avatars become available, the company is launching another version of AI avatars that have hands and can be filmed from multiple angles. Their predecessors were only available in portrait mode and were just visible from the front. 

Other startups, such as Hour One, have launched similar avatars with hands. Synthesia’s version, which I got to test in a research preview and will be launched in late July, has slightly more realistic hand movements and lip-synching. 

Crucially, the coming update also makes it far easier to  create your own personalized avatar. The company’s previous custom AI avatars required users to go into a studio to record their face and voice over the span of a couple of hours, as I reported in April. 

This time, I recorded the material needed in just 10 minutes in the Synthesia office, using a digital camera, a lapel mike, and a laptop. But an even more basic setup, such as a laptop camera, would do. And while previously I had to record my facial movements and voice separately, this time the data was collected at the same time. The process also includes reading a script expressing consent to being recorded in this way, and reading out a randomly generated security passcode. 

These changes allow more scale and give the AI models powering the avatars more capabilities with less data, says Riparbelli. The results are also much faster. While I had to wait a few weeks to get my studio-made avatar, the new homemade ones were available the next day. 

Below, you can see my test of the new homemade avatars with hands. 

The homemade avatars aren’t as expressive as the studio-made ones yet, and users can’t change the backgrounds of their avatars, says Alexandru Voica, Synthesia’s head of corporate affairs and policy. The hands are animated using an advanced form of looping technology, which repeats the same hand movements in a way that is responsive to the content of the script. 

Hands are tricky for AI to do well—even more so than faces, Vittorio Ferrari, Synthesia’s director of science, told me in in March. That’s because our mouths move in relatively small and predictable ways while we talk, making it possible to sync the deepfake version up with speech, but we move our hands in lots of different ways. On the flip side, while faces require close attention to detail because we tend to focus on them, hands can be less precise, Ferrari says. 

Even if they’re imperfect, AI-generated hands and bodies add a lot to the illusion of realism, which poses serious risks at a time when deepfakes and online misinformation are proliferating. Synthesia has strict content moderation policies, carefully vetting both its customers and the sort of content they’re able to generate. For example, only accredited news outlets can generate content on news.  

These new advancements in avatar technologies are another hammer blow to our ability to believe what we see online, says Saunders. 

“People need to know you can’t trust anything,” he says. “Synthesia is doing this now, and another year down the line it will be better and other companies will be doing it.” 

This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here. 

Earlier this week, the US surgeon general, also known as the “nation’s doctor,” authored an article making the case that health warnings should accompany social media. The goal: to protect teenagers from its harmful effects. “Adolescents who spend more than three hours a day on social media face double the risk of anxiety and depression symptoms,” Vivek Murthy wrote in a piece published in the New York Times. “Additionally, nearly half of adolescents say social media makes them feel worse about their bodies.”

His concern instinctively resonates with me. I’m in my late 30s, and even I can end up feeling a lot worse about myself after a brief stint on Instagram. I have two young daughters, and I worry about how I’ll respond when they reach adolescence and start asking for access to whatever social media site their peers are using. My children already have a fascination with cell phones; the eldest, who is almost six, will often come into my bedroom at the crack of dawn, find my husband’s phone, and somehow figure out how to blast “Happy Xmas (War Is Over)” at full volume.

But I also know that the relationship between this technology and health isn’t black and white. Social media can affect users in different ways—often positively. So let’s take a closer look at the concerns, the evidence behind them, and how best to tackle them.

Murthy’s concerns aren’t new, of course. In fact, almost any time we are introduced to a new technology, some will warn of its potential dangers. Innovations like the printing press, radio, and television all had their critics back in the day. In 2009, the Daily Mail linked Facebook use to cancer.

More recently, concerns about social media have centered on young people. There’s a lot going on in our teenage years as our brains undergo maturation, our hormones shift, and we explore new ways to form relationships with others. We’re thought to be more vulnerable to mental-health disorders during this period too. Around half of such disorders are thought to develop by the age of 14, and suicide is the fourth-leading cause of death in people aged between 15 and 19, according to the World Health Organization. Many have claimed that social media only makes things worse.

Reports have variously cited cyberbullying, exposure to violent or harmful content, and the promotion of unrealistic body standards, for example, as potential key triggers of low mood and disorders like anxiety and depression. There have also been several high-profile cases of self-harm and suicide with links to social media use, often involving online bullying and abuse. Just this week, the suicide of an 18-year-old in Kerala, India, was linked to cyberbullying. And children have died after taking part in dangerous online challenges made viral on social media, whether from inhaling toxic substances, consuming ultra-spicy tortilla chips, or choking themselves.

Murthy’s new article follows an advisory on social media and youth mental health published by his office in 2023. The 25-page document, which lays out some of known benefits and harms of social media use as well as the “unknowns,” was intended to raise awareness of social media as a health issue. The problem is that things are not entirely clear cut.

“The evidence is currently quite limited,” says Ruth Plackett, a researcher at University College London who studies the impact of social media on mental health in young people. A lot of the research on social media and mental health is correlational. It doesn’t show that social media use causes mental health disorders, Plackett says.

The surgeon general’s advisory cites some of these correlational studies. It also points to survey-based studies, including one looking at mental well-being among college students after the rollout of Facebook in the mid-2000s. But even if you accept the authors’ conclusion that Facebook had a negative impact on the students’ mental health, it doesn’t mean that other social media platforms will have the same effect on other young people. Even Facebook, and the way we use it, has changed a lot in the last 20 years.

Other studies have found that social media has no effect on mental health. In a study published last year, Plackett and her colleagues surveyed 3,228 children in the UK to see how their social media use and mental well-being changed over time. The children were first surveyed when they were aged between 12 and 13, and again when they were 14 to 15 years old.

Plackett expected to find that social media use would harm the young participants. But when she conducted the second round of questionnaires, she found that was not the case. “Time spent on social media was not related to mental-health outcomes two years later,” she tells me.

Other research has found that social media use can be beneficial to young people, especially those from minority groups. It can help some avoid loneliness, strengthen relationships with their peers, and find a safe space to express their identities, says Plackett. Social media isn’t only for socializing, either. Today, young people use these platforms for news, entertainment, school, and even (in the case of influencers) business.

“It’s such a mixed bag of evidence,” says Plackett. “I’d say it’s hard to draw much of a conclusion at the minute.”

In his article, Murthy calls for a warning label to be applied to social media platforms, stating that “social media is associated with significant mental-health harms for adolescents.”

But while Murthy draws comparisons to the effectiveness of warning labels on tobacco products, bingeing on social media doesn’t have the same health risks as chain-smoking cigarettes. We have plenty of strong evidence linking smoking to a range of diseases, including gum disease, emphysema, and lung cancer, among others. We know that smoking can shorten a person’s life expectancy. We can’t make any such claims about social media, no matter what was written in that Daily Mail article.

Health warnings aren’t the only way to prevent any potential harms associated with social media use, as Murthy himself acknowledges. Tech companies could go further in reducing or eliminating violent and harmful content, for a start. And digital literacy education could help inform children and their caregivers how to alter the settings on various social media platforms to better control the content children see, and teach them how to assess the content that does make it to their screens.

I like the sound of these measures. They might even help me put an end to the early-morning Christmas songs. 

Now read the rest of The Checkup

Read more from MIT Technology Review’s archive:

Bills designed to make the internet safer for children have been popping up across the US. But individual states take different approaches, leaving the resulting picture a mess, as Tate Ryan-Mosley explored.

Dozens of US states sued Meta, the parent company of Facebook, last October. As Tate wrote at the time, the states claimed that the company knowingly harmed young users, misled them about safety features and harmful content, and violated laws on children’s privacy.  

China has been implementing increasingly tight controls over how children use the internet. In August last year, the country’s cyberspace administrator issued detailed guidelines that include, for example, a rule to limit use of smart devices to 40 minutes a day for children under the age of eight. And even that use should be limited to content about “elementary education, hobbies and interests, and liberal arts education.” My colleague Zeyi Yang had the story in a previous edition of his weekly newsletter, China Report.

Last year, TikTok set a 60-minute-per-day limit for users under the age of 18. But the Chinese domestic version of the app, Douyin, has even tighter controls, as Zeyi wrote last March.

One way that social media can benefit young people is by allowing them to express their identities in a safe space. Filters that superficially alter a person’s appearance to make it more feminine or masculine can help trans people play with gender expression, as Elizabeth Anne Brown wrote in 2022. She quoted Josie, a trans woman in her early 30s. “The Snapchat girl filter was the final straw in dropping a decade’s worth of repression,” Josie said. “[I] saw something that looked more ‘me’ than anything in a mirror, and I couldn’t go back.”

From around the web

Could gentle shock waves help regenerate heart tissue? A trial of what’s being dubbed a “space hairdryer” suggests the treatment could help people recover from bypass surgery. (BBC)

“We don’t know what’s going on with this virus coming out of China right now.” Anthony Fauci gives his insider account of the first three months of the covid-19 pandemic. (The Atlantic)

Microplastics are everywhere. It was only a matter of time before scientists found them in men’s penises. (The Guardian)

Is the singularity nearer? Ray Kurzweil believes so. He also thinks medical nanobots will allow us to live beyond 120. (Wired)

In a clean room in his lab, Sean Moore peers through a microscope at a bit of intestine, its dark squiggles and rounded structures standing out against a light gray background. This sample is not part of an actual intestine; rather, it’s human intestinal cells on a tiny plastic rectangle, one of 24 so-called “organs on chips” his lab bought three years ago.

Moore, a pediatric gastroenterologist at the University of Virginia School of Medicine, hopes the chips will offer answers to a particularly thorny research problem. He studies rotavirus, a common infection that causes severe diarrhea, vomiting, dehydration, and even death in young children. In the US and other rich nations, up to 98% of the children who are vaccinated against rotavirus develop lifelong immunity. But in low-income countries, only about a third of vaccinated children become immune. Moore wants to know why.

His lab uses mice for some protocols, but animal studies are notoriously bad at identifying human treatments. Around 95% of the drugs developed through animal research fail in people. Researchers have documented this translation gap since at least 1962. “All these pharmaceutical companies know the animal models stink,” says Don Ingber, founder of the Wyss Institute for Biologically Inspired Engineering at Harvard and a leading advocate for organs on chips. “The FDA knows they stink.” 

But until recently there was no other option. Research questions like Moore’s can’t ethically or practically be addressed with a randomized, double-blinded study in humans. Now these organs on chips, also known as microphysiological systems, may offer a truly viable alternative. They look remarkably prosaic: flexible polymer rectangles about the size of a thumb drive. In reality they’re triumphs of bioengineering, intricate constructions furrowed with tiny channels that are lined with living human tissues. These tissues expand and contract with the flow of fluid and air, mimicking key organ functions like breathing, blood flow, and peristalsis, the muscular contractions of the digestive system.

More than 60 companies now produce organs on chips commercially, focusing on five major organs: liver, kidney, lung, intestines, and brain. They’re already being used to understand diseases, discover and test new drugs, and explore personalized approaches to treatment.

As they continue to be refined, they could solve one of the biggest problems in medicine today. “You need to do three things when you’re making a drug,” says Lorna Ewart, a pharmacologist and chief scientific officer of Emulate, a biotech company based in Boston. “You need to show it’s safe. You need to show it works. You need to be able to make it.” 

All new compounds have to pass through a preclinical phase, where they’re tested for safety and effectiveness before moving to clinical trials in humans. Until recently, those tests had to run in at least two animal species—usually rats and dogs—before the drugs were tried on people. 

But in December 2022, President Biden signed the FDA Modernization Act, which amended the original FDA Act of 1938. With a few small word changes, the act opened the door for non-animal-based testing in preclinical trials. Anything that makes it faster and easier for pharmaceutical companies to identify safe and effective drugs means better, potentially cheaper treatments for all of us. 

Moore, for one, is banking on it, hoping the chips help him and his colleagues shed light on the rotavirus vaccine responses that confound them. “If you could figure out the answer,” he says, “you could save a lot of kids’ lives.”

While many teams have worked on organ chips over the last 30 years, the OG in the field is generally acknowledged to be Michael Shuler, a professor emeritus of chemical engineering at Cornell. In the 1980s, Shuler was a math and engineering guy who imagined an “animal on a chip,” a cell culture base seeded with a variety of human cells that could be used for testing drugs. He wanted to position a handful of different organ cells on the same chip, linked to one another, which could mimic the chemical communication between organs and the way drugs move through the body. “This was science fiction,” says Gordana Vunjak-Novakovic, a professor of biomedical engineering at Columbia University whose lab works with cardiac tissue on chips. “There was no body on a chip. There is still no body on a chip. God knows if there will ever be a body on a chip.”

Shuler had hoped to develop a computer model of a multi-organ system, but there were too many unknowns. The living cell culture system he dreamed up was his bid to fill in the blanks. For a while he played with the concept, but the materials simply weren’t good enough to build what he imagined. 

“You can force mice to menstruate, but it’s not really menstruation. You need the human being.”

Linda Griffith, founding professor of biological engineering at MIT and a 2006 recipient of a MacArthur “genius grant”

He wasn’t the only one working on the problem. Linda Griffith, a founding professor of biological engineering at MIT and a 2006 recipient of a MacArthur “genius grant,” designed a crude early version of a liver chip in the late 1990s: a flat silicon chip, just a few hundred micrometers tall, with endothelial cells, oxygen and liquid flowing in and out via pumps, silicone tubing, and a polymer membrane with microscopic holes. She put liver cells from rats on the chip, and those cells organized themselves into three-dimensional tissue. It wasn’t a liver, but it modeled a few of the things a functioning human liver could do. It was a start.

Griffith, who rides a motorcycle for fun and speaks with a soft Southern accent, suffers from endometriosis, an inflammatory condition where cells from the lining of the uterus grow throughout the abdomen. She’s endured decades of nausea, pain, blood loss, and repeated surgeries. She never took medical leaves, instead loading up on Percocet, Advil, and margaritas, keeping a heating pad and couch in her office—a strategy of necessity, as she saw no other choice for a working scientist. Especially a woman. 

And as a scientist, Griffith understood that the chronic diseases affecting women tend to be under-researched, underfunded, and poorly treated. She realized that decades of work with animals hadn’t done a damn thing to make life better for women like her. “We’ve got all this data, but most of that data does not lead to treatments for human diseases,” she says. “You can force mice to menstruate, but it’s not really menstruation. You need the human being.” 

Or, at least, the human cells. Shuler and Griffith, and other scientists in Europe, worked on some of those early chips, but things really kicked off around 2009, when Don Ingber’s lab in Cambridge, Massachusetts, created the first fully functioning organ on a chip. That “lung on a chip” was made from flexible silicone rubber, lined with human lung cells and capillary blood vessel cells that “breathed” like the alveoli—tiny air sacs—in a human lung. A few years later Ingber, an MD-PhD with the tidy good looks of a younger Michael Douglas, founded Emulate, one of the earliest biotech companies making microphysiological systems. Since then he’s become a kind of unofficial ambassador for in vitro technologies in general and organs on chips in particular, giving hundreds of talks, scoring millions in grant money, repping the field with scientists and laypeople. Stephen Colbert once ragged on him after the New York Times quoted him as describing a chip that “walks, talks, and quacks like a human vagina,” a quote Ingber says was taken out of context.

Ingber began his career working on cancer. But he struggled with the required animal research. “I really didn’t want to work with them anymore, because I love animals,” he says. “It was a conscious decision to focus on in vitro models.” He’s not alone; a growing number of young scientists are speaking up about the distress they feel when research protocols cause pain, trauma, injury, and death to lab animals. “I’m a master’s degree student in neuroscience and I think about this constantly. I’ve done such unspeakable, horrible things to mice all in the name of scientific progress, and I feel guilty about this every day,” wrote one anonymous student on Reddit. (Full disclosure: I switched out of a psychology major in college because I didn’t want to cause harm to animals.)

EMULATE

Taking an undergraduate art class led Ingber to an epiphany: mechanical forces are just as important as chemicals and genes in determining the way living creatures work. On a shelf in his office he still displays a model he built in that art class, a simple construction of sticks and fishing line, which helped him realize that cells pull and twist against each other. That realization foreshadowed his current work and helped him design dynamic microfluidic devices that incorporated shear and flow. 

Ingber coauthored a 2022 paper that’s sometimes cited as a watershed in the world of organs on chips. Researchers used Emulate’s liver chips to reevaluate 27 drugs that had previously made it through animal testing and had then gone on to kill 242 people and necessitate more than 60 liver transplants. The liver chips correctly flagged problems with 22 of the 27 drugs, an 87% success rate compared with a 0% success rate for animal testing. It was the first time organs on chips had been directly pitted against animal models, and the results got a lot of attention from the pharmaceutical industry. Dan Tagle, director of the Office of Special Initiatives for the National Center for Advancing Translational Sciences (NCATS), estimates that drug failures cost around $2.6 billion globally each year. The earlier in the process failing compounds can be weeded out, the more room there is for other drugs to succeed.

“The capacity we have to test drugs is more or less fixed in this country,” says Shuler, whose company, Hesperos, also manufactures organs on chips. “There are only so many clinical trials you can do. So if you put a loser into the system, that means something that could have won didn’t get into the system. We want to change the success rate from clinical trials to a much higher number.”

In 2011, the National Institutes of Health established NCATS and started investing in organs on chips and other in vitro technologies. Other government funders, like the Defense Advanced Research Projects Agency and the Food and Drug Administration, have followed suit. For instance, NIH recently funded NASA scientists to send heart tissue on chips into space. Six months in low gravity ages the cardiovascular system 10 years, so this experiment lets researchers study some of the effects of aging without harming animals or humans. 

Scientists have made liver chips, brain chips, heart chips, kidney chips, intestine chips, and even a female reproductive system on a chip (with cells from ovaries, fallopian tubes, and uteruses that release hormones and mimic an actual 28-day menstrual cycle). Each of these chips exhibits some of the specific functions of the organs in question. Cardiac chips, for instance, contain heart cells that beat just like heart muscle, making it possible for researchers to model disorders like cardiomyopathy. 

Shuler thinks organs on chips will revolutionize the world of research for rare diseases. “It is a very good model when you don’t have enough patients for normal clinical trials and you don’t have a good animal model,” he says. “So it’s a way to get drugs to people that couldn’t be developed in our current pharmaceutical model.” Shuler’s own biotech company used organs on chips to test a potential drug for myasthenia gravis, a rare neurological disorder. In 2022,the FDA approved the drug for clinical trials based on that data—one of six Hesperos drugs that have so far made it to that stage. 

Each chip starts with a physiologically based pharmacokinetic model, known as a PBPK model—a mathematical expression of how a chemical compound behaves in a human body. “We try and build a physical replica of the mathematical model of what really occurs in the body,” explains Shuler. That model guides the way the chip is designed, re-creating the amount of time a fluid or chemical stays in that particular organ—what’s known as the residence time. “As long as you have the same residence time, you should get the same response in terms of chemical conversion,” he says.

Tiny channels on each chip, each between 10 and 100 microns in diameter, help bring fluids and oxygen to the cells. “When you get down to less than one micron, you can’t use normal fluid dynamics,” says Shuler. And fluid dynamics matters, because if the fluid moves through the device too quickly, the cells might die; too slowly, and the cells won’t react normally. 

Chip technology, while sophisticated, has some downsides. One of them is user friendliness. “We need to get rid of all this tubing and pumps and make something that’s as simple as a well plate for culturing cells,” says Vunjak-Novakovic. Her lab and others are working on simplifying the design and function of such chips so they’re easier to operate and are compatible with robots, which do repetitive tasks like pipetting in many labs. 

Cost and sourcing can also be challenging. Emulate’s base model, which looks like a simple rectangular box from the outside,starts at around $100,000 and rises steeply from there. Most human cells come from commercial suppliers that arrange for donations from hospital patients. During the pandemic, when people had fewer elective surgeries, many of those sources dried up. As microphysiological systems become more mainstream, finding reliable sources of human cells will be critical.

“As your confidence in using the chips grows, you might say, Okay, we don’t need two animals anymore— we could go with chip plus one animal.”

Lorna Ewart, Chief Scientific Officer, Emulate

Another challenge is that every company producing organs on chips uses its own proprietary methods and technologies. Ingber compares the landscape to the early days of personal computing, when every company developed its own hardware and software, and none of them meshed well. For instance, the microfluidic systems in Emulate’s intestine chips are fueled by micropumps, while those made by Mimetas, another biotech company, use an electronic rocker and gravity to circulate fluids and air. “This is not an academic lab type of challenge,” emphasizes Ingber. “It’s a commercial challenge. There’s no way you can get the same results anywhere in the world with individual academics making [organs on chips], so you have to have commercialization.”

Namandje Bumpus, the FDA’s chief scientist, agrees. “You can find differences [in outcomes] depending even on what types of reagents you’re using,” she says. Those differences mean research can’t be easily reproduced, which diminishes its validity and usefulness. “It would be great to have some standardization,” she adds.

On the plus side, the chip technology could help researchers address some of the most deeply entrenched health inequities in science. Clinical trials have historically recruited white men, underrepresenting people of color, women (especially pregnant and lactating women), the elderly, and other groups. And treatments derived from those trials all too often fail in members of those underrepresented groups, as in Moore’s rotavirus vaccine mystery. “With organs on a chip, you may be able to create systems by which you are very, very thoughtful—where you spread the net wider than has ever been done before,” says Moore.

FELICE FRANKEL

Another advantage is that chips will eventually reduce the need for animals in the lab even as they lead to better human outcomes. “There are aspects of animal research that make all of us uncomfortable, even people that do it,” acknowledges Moore. “The same values that make us uncomfortable about animal research are also the same values that make us uncomfortable with seeing human beings suffer with diseases that we don’t have cures for yet. So we always sort of balance that desire to reduce suffering in all the forms that we see it.”

Lorna Ewart, who spent 20 years at the pharma giant AstraZeneca before joining Emulate, thinks we’re entering a kind of transition time in research, in which scientists use in vitro technologies like organs on chips alongside traditional cell culture methods and animals. “As your confidence in using the chips grows, you might say, Okay, we don’t need two animals anymore—we could go with chip plus one animal,” she says. 

In the meantime, Sean Moore is excited about incorporating intestine chips more and more deeply into his research. His lab has been funded by the Gates Foundation to do what he laughingly describes as a bake-off between intestine chips made by Emulate and Mimetas. They’re infecting the chips with different strains of rotavirus to try to identify the pros and cons of each company’s design. It’s too early for any substantive results, but Moore says he does have data showing that organ chips are a viable model for studying rotavirus infection. That could ultimately be a real game-changer in his lab and in labs around the world.

“There’s more players in the space right now,” says Moore. “And that competition is going to be a healthy thing.” 

Harriet Brown writes about health, medicine, and science. Her most recent book is Shadow Daughter: A Memoir of Estrangement. She’s a professor of magazine, news, and digital journalism at Syracuse University’s Newhouse School. 

A potential future conflict between Taiwan and China would be shaped by novel methods of drone warfare involving advanced underwater drones and increased levels of autonomy, according to a new war-gaming experiment by the think tank Center for a New American Security (CNAS). 

The report comes as concerns about Beijing’s aggression toward Taiwan have been rising: China sent dozens of surveillance balloons over the Taiwan Strait in January during Taiwan’s elections, and in May, two Chinese naval ships entered Taiwan’s restricted waters. The US Department of Defense has said that preparing for potential hostilities is an “absolute priority,” though no such conflict is immediately expected. 

The report’s authors detail a number of ways that use of drones in any South China Sea conflict would differ starkly from current practices, most notably in the war in Ukraine, often called the first full-scale drone war. 

Differences from the Ukrainian battlefield

Since Russia invaded Ukraine in 2022, drones have been aiding in what military experts describe as the first three steps of the “kill chain”—finding, targeting, and tracking a target—as well as in delivering explosives. The drones have a short life span, since they are often shot down or made useless by frequency jamming devices that prevent pilots from controlling them. Quadcopters—the commercially available drones often used in the war—last just three flights on average, according to the report. 

Drones like these would be far less useful in a possible invasion of Taiwan. “Ukraine-Russia has been a heavily land conflict, whereas conflict between the US and China would be heavily air and sea,” says Zak Kallenborn, a drone analyst and adjunct fellow with the Center for Strategic and International Studies, who was not involved in the report but agrees broadly with its projections. The small, off-the-shelf drones popularized in Ukraine have flight times too short for them to be used effectively in the South China Sea. 

An underwater war

Instead, a conflict with Taiwan would likely make use of undersea and maritime drones. With Taiwan just 100 miles away from China’s mainland, the report’s authors say, the Taiwan Strait is where the first days of such a conflict would likely play out. The Zhu Hai Yun, China’s high-tech autonomous carrier, might send its autonomous underwater drones to scout for US submarines. The drones could launch attacks that, even if they did not sink the submarines, might divert the attention and resources of the US and Taiwan. 

It’s also possible China would flood the South China Sea with decoy drone boats to “make it difficult for American missiles and submarines to distinguish between high-value ships and worthless uncrewed commercial vessels,” the authors write.

Though most drone innovation is not focused on maritime applications, these uses are not without precedent: Ukrainian forces drew attention for modifying jet skis to operate via remote control and using them to intimidate and even sink Russian vessels in the Black Sea. 

More autonomy

Drones currently have very little autonomy. They’re typically human-piloted, and though some are capable of autopiloting to a fixed GPS point, that’s generally not very useful in a war scenario, where targets are on the move. But, the report’s authors say, autonomous technology is developing rapidly, and whichever nation possesses a more sophisticated fleet of autonomous drones will hold a significant edge.

What would that look like? Millions of defense research dollars are being spent in the US and China alike on swarming, a strategy where drones navigate autonomously in groups and accomplish tasks. The technology isn’t deployed yet, but if successful, it could be a game-changer in any potential conflict.  

A sea-based conflict might also offer an easier starting ground for AI-driven navigation, because object recognition is easier on the “relatively uncluttered surface of the ocean” than on the ground, the authors write.

China’s advantages

A chief advantage for China in a potential conflict is its proximity to Taiwan; it has more than three dozen air bases within 500 miles, while the closest US base is 478 miles away in Okinawa. But an even bigger advantage is that it produces more drones than any other nation.

“China dominates the commercial drone market, absolutely,” says Stacie Pettyjohn, coauthor of the report and director of the defense program at CNAS. That includes drones of the type used in Ukraine.

For Taiwan to use these Chinese drones for their own defenses, they’d first have to make the purchase, which could be difficult because the Chinese government might move to block it. Then they’d need to hack them and disconnect them from the companies that made them, or else those Chinese manufacturers could turn them off remotely or launch cyberattacks. That sort of hacking is unfeasible at scale, so Taiwan is effectively cut off from the world’s foremost commercial drone supplier and must either make their own drones or find alternative manufacturers, likely in the US. On Wednesday, June 19, the US approved a $360 million sale of 1,000 military-grade drones to Taiwan.

For now, experts can only speculate about how those drones might be used. Though preparing for a conflict in the South China Sea is a priority for the DOD, it’s one of many, says Kallenborn. “The sensible approach, in my opinion, is recognizing that you’re going to potentially have to deal with all of these different things,” he says. “But we don’t know the particular details of how it will work out.”

Ongoing European Union quest to break end-to-end encryption (E2EE) mysteriously disappears.

The post EU Aims to Ban Math — ‘Chat Control 2.0’ Law is Paused but not Stopped appeared first on Security Boulevard.

First, a confession. I only got into playing video games a little over a year ago (I know, I know). A Christmas gift of an Xbox Series S “for the kids” dragged me—pretty easily, it turns out—into the world of late-night gaming sessions. I was immediately attracted to open-world games, in which you’re free to explore a vast simulated world and choose what challenges to accept. Red Dead Redemption 2 (RDR2), an open-world game set in the Wild West, blew my mind. I rode my horse through sleepy towns, drank in the saloon, visited a vaudeville theater, and fought off bounty hunters. One day I simply set up camp on a remote hilltop to make coffee and gaze down at the misty valley below me.

To make them feel alive, open-world games are inhabited by vast crowds of computer-controlled characters. These animated people—called NPCs, for “nonplayer characters”—populate the bars, city streets, or space ports of games. They make these virtual worlds feel lived in and full. Often—but not always—you can talk to them.

In open-world games like Red Dead Redemption 2, players can choose diverse interactions within the same simulated experience.

After a while, however, the repetitive chitchat (or threats) of a passing stranger forces you to bump up against the truth: This is just a game. It’s still fun—I had a whale of a time, honestly, looting stagecoaches, fighting in bar brawls, and stalking deer through rainy woods—but the illusion starts to weaken when you poke at it. It’s only natural. Video games are carefully crafted objects, part of a multibillion-dollar industry, that are designed to be consumed. You play them, you loot a few stagecoaches, you finish, you move on. 

It may not always be like that. Just as it is upending other industries, generative AI is opening the door to entirely new kinds of in-game interactions that are open-ended, creative, and unexpected. The game may not always have to end.

Startups employing generative-AI models, like ChatGPT, are using them to create characters that don’t rely on scripts but, instead, converse with you freely. Others are experimenting with NPCs who appear to have entire interior worlds, and who can continue to play even when you, the player, are not around to watch. Eventually, generative AI could create game experiences that are infinitely detailed, twisting and changing every time you experience them. 

The field is still very new, but it’s extremely hot. In 2022 the venture firm Andreessen Horowitz launched Games Fund, a $600 million fund dedicated to gaming startups. A huge number of these are planning to use AI in gaming. And the firm, also known as A16Z, has now invested in two studios that are aiming to create their own versions of AI NPCs. A second $600 million round was announced in April 2024.

Early experimental demos of these experiences are already popping up, and it may not be long before they appear in full games like RDR2. But some in the industry believe this development will not just make future open-world games incredibly immersive; it could change what kinds of game worlds or experiences are even possible. Ultimately, it could change what it means to play.

“What comes after the video game? You know what I mean?” says Frank Lantz, a game designer and director of the NYU Game Center. “Maybe we’re on the threshold of a new kind of game.”

These guys just won’t shut up

The way video games are made hasn’t changed much over the years. Graphics are incredibly realistic. Games are bigger. But the way in which you interact with characters, and the game world around you, uses many of the same decades-old conventions.

“In mainstream games, we’re still looking at variations of the formula we’ve had since the 1980s,” says Julian Togelius, a computer science professor at New York University who has a startup called Modl.ai that does in-game testing. Part of that tried-and-tested formula is a technique called a dialogue tree, in which all of an NPC’s possible responses are mapped out. Which one you get depends on which branch of the dialogue tree you have chosen. For example, say something rude about a passing NPC in RDR2 and the character will probably lash out—you have to quickly apologize to avoid a shootout (unless that’s what you want).

Generative AI is already helping take some of that drudgery out of making new games. Jonathan Lai, a general partner at A16Z and one of Games Fund’s managers, says that most studios are using image-­generating tools like Midjourney to enhance or streamline their work. And in a 2023 survey by A16Z, 87% of game studios said they were already using AI in their workflow in some way—and 99% planned to do so in the future. Many use AI agents to replace the human testers who look for bugs, such as places where a game might crash. In recent months, the CEO of the gaming giant EA said generative AI could be used in more than 50% of its game development processes.

Ubisoft, one of the biggest game developers, famous for AAA open-world games such as Assassin’s Creed, has been using a large-­language-model-based AI tool called Ghostwriter to do some of the grunt work for its developers in writing basic dialogue for its NPCs. Ghostwriter generates loads of options for background crowd chatter, which the human writer can pick from or tweak. The idea is to free the humans up so they can spend that time on more plot-focused writing.

GEORGE WYLESOL

Ultimately, though, everything is scripted. Once you spend a certain number of hours on a game, you will have seen everything there is to see, and completed every interaction. Time to buy a new one.

But for startups like Inworld AI, this situation is an opportunity. Inworld, based in California, is building tools to make in-game NPCs that respond to a player with dynamic, unscripted dialogue and actions—so they never repeat themselves. The company, now valued at $500 million, is the best-funded AI gaming startup around thanks to backing from former Google CEO Eric Schmidt and other high-profile investors. 

Role-playing games give us a unique way to experience different realities, explains Kylan Gibbs, Inworld’s CEO and founder. But something has always been missing. “Basically, the characters within there are dead,” he says. 

“When you think about media at large, be it movies or TV or books, characters are really what drive our ability to empathize with the world,” Gibbs says. “So the fact that games, which are arguably the most advanced version of storytelling that we have, are lacking these live characters—it felt to us like a pretty major issue.”

Gamers themselves were pretty quick to realize that LLMs could help fill this gap. Last year, some came up with ChatGPT mods (a way to alter an existing game) for the popular role-playing game Skyrim. The mods let players interact with the game’s vast cast of characters using LLM-powered free chat. One mod even included OpenAI’s speech recognition software Whisper AI so that players could speak to the players with their voices, saying whatever they wanted, and have full conversations that were no longer restricted by dialogue trees. 

The results gave gamers a glimpse of what might be possible but were ultimately a little disappointing. Though the conversations were open-ended, the character interactions were stilted, with delays while ChatGPT processed each request. 

Inworld wants to make this type of interaction more polished. It’s offering a product for AAA game studios in which developers can create the brains of an AI NPC that can be then imported into their game. Developers use the company’s “Inworld Studio” to generate their NPC. For example, they can fill out a core description that sketches the character’s personality, including likes and dislikes, motivations, or useful backstory. Sliders let you set levels of traits such as introversion or extroversion, insecurity or confidence. And you can also use free text to make the character drunk, aggressive, prone to exaggeration—pretty much anything.

Developers can also add descriptions of how their character speaks, including examples of commonly used phrases that Inworld’s various AI models, including LLMs, then spin into dialogue in keeping with the character. 

“Because there’s such reliance on a lot of labor-intensive scripting, it’s hard to get characters to handle a wide variety of ways a scenario might play out, especially as games become more and more open-ended.”

Jeff Orkin, founder, Bitpart

Game designers can also plug other information into the system: what the character knows and doesn’t know about the world (no Taylor Swift references in a medieval battle game, ideally) and any relevant safety guardrails (does your character curse or not?). Narrative controls will let the developers make sure the NPC is sticking to the story and isn’t wandering wildly off-base in its conversation. The idea is that the characters can then be imported into video-game graphics engines like Unity or Unreal Engine to add a body and features. Inworld is collaborating with the text-to-voice startup ElevenLabs to add natural-sounding voices.

Inworld’s tech hasn’t appeared in any AAA games yet, but at the Game Developers Conference (GDC) in San Francisco in March 2024, the firm unveiled an early demo with Nvidia that showcased some of what will be possible. In Covert Protocol, each player operates as a private detective who must solve a case using input from the various in-game NPCs. Also at the GDC, Inworld unveiled a demo called NEO NPC that it had worked on with Ubisoft. In NEO NPC, a player could freely interact with NPCs using voice-to-text software and use conversation to develop a deeper relationship with them.

LLMs give us the chance to make games more dynamic, says Jeff Orkin, founder of Bitpart AI, a new startup that also aims to create entire casts of LLM-powered NPCs that can be imported into games. “Because there’s such reliance on a lot of labor-intensive scripting, it’s hard to get characters to handle a wide variety of ways a scenario might play out, especially as games become more and more open-ended,” he says.

Bitpart’s approach is in part inspired by Orkin’s PhD research at MIT’s Media Lab. There, he trained AIs to role-play social situations using game-play logs of humans doing the same things with each other in multiplayer games.

Bitpart’s casts of characters are trained using a large language model and then fine-tuned in a way that means the in-game interactions are not entirely open-ended and infinite. Instead, the company uses an LLM and other tools to generate a script covering a range of possible interactions, and then a human game designer will select some. Orkin describes the process as authoring the Lego bricks of the interaction. An in-game algorithm searches out specific bricks to string them together at the appropriate time.

Bitpart’s approach could create some delightful in-game moments. In a restaurant, for example, you might ask a waiter for something, but the bartender might overhear and join in. Bitpart’s AI currently works with Roblox. Orkin says the company is now running trials with AAA game studios, although he won’t yet say which ones.

But generative AI might do more than just enhance the immersiveness of existing kinds of games. It could give rise to completely new ways to play.

Making the impossible possible

When I asked Frank Lantz about how AI could change gaming, he talked for 26 minutes straight. His initial reaction to generative AI had been visceral: “I was like, oh my God, this is my destiny and is what I was put on the planet for.” 

Lantz has been in and around the cutting edge of the game industry and AI for decades but received a cult level of acclaim a few years ago when he created the Universal Paperclips game. The simple in-browser game gives the player the job of producing as many paper clips as possible. It’s a riff on the famous thought experiment by the philosopher Nick Bostrom, which imagines an AI that is given the same task and optimizes against humanity’s interest by turning all the matter in the known universe into paper clips.

Lantz is bursting with ideas for ways to use generative AI. One is to experience a new work of art as it is being created, with the player participating in its creation. “You’re inside of something like Lord of the Rings as it’s being written. You’re inside a piece of literature that is unfolding around you in real time,” he says. He also imagines strategy games where the players and the AI work together to reinvent what kind of game it is and what the rules are, so it is never the same twice.

For Orkin, LLM-powered NPCs can make games unpredictable—and that’s exciting. “It introduces a lot of open questions, like what you do when a character answers you but that sends a story in a direction that nobody planned for,” he says. 

Generative A I might do more than just enhance the immersiveness of existing kinds of games. It could give rise to completely new ways to play.

It might mean games that are unlike anything we’ve seen thus far. Gaming experiences that unspool as the characters’ relationships shift and change, as friendships start and end, could unlock entirely new narrative experiences that are less about action and more about conversation and personalities. 

Togelius imagines new worlds built to react to the player’s own wants and needs, populated with NPCs that the player must teach or influence as the game progresses. Imagine interacting with characters whose opinions can change, whom you could persuade or motivate to act in a certain way—say, to go to battle with you. “A thoroughly generative game could be really, really good,” he says. “But you really have to change your whole expectation of what a game is.”

Lantz is currently working on a prototype of a game in which the premise is that you—the player—wake up dead, and the afterlife you are in is a low-rent, cheap version of a synthetic world. The game plays out like a noir in which you must explore a city full of thousands of NPCs powered by a version of ChatGPT, whom you must interact with to work out how you ended up there. 

His early experiments gave him some eerie moments when he felt that the characters seemed to know more than they should, a sensation recognizable to people who have played with LLMs before. Even though you know they’re not alive, they can still freak you out a bit.

“If you run electricity through a frog’s corpse, the frog will move,” he says. “And if you run $10 million worth of computation through the internet … it moves like a frog, you know.” 

But these early forays into generative-­­AI gaming have given him a real sense of excitement for what’s next: “I felt like, okay, this is a thread. There really is a new kind of artwork here.”

If an AI NPC talks and no one is around to listen, is there a sound?

AI NPCs won’t just enhance player interactions—they might interact with one another in weird ways. Red Dead Redemption 2’s NPCs each have long, detailed scripts that spell out exactly where they should go, what work they must complete, and how they’d react if anything unexpected occurred. If you want, you can follow an NPC and watch it go about its day. It’s fun, but ultimately it’s hard-coded.

NPCs built with generative AI could have a lot more leeway—even interacting with one another when the player isn’t there to watch. Just as people have been fooled into thinking LLMs are sentient, watching a city of generated NPCs might feel like peering over the top of a toy box that has somehow magically come alive.

We’re already getting a sense of what this might look like. At Stanford University, Joon Sung Park has been experimenting with AI-generated characters and watching to see how their behavior changes and gains complexity as they encounter one another. 

Because large language models have sucked up the internet and social media, they actually contain a lot of detail about how we behave and interact, he says.

In Park’s recent research, he and colleagues set up a Sims-like game, called Smallville, with 25 simulated characters that had been trained using generative AI. Each was given a name and a simple biography before being set in motion. When left to interact with each other for two days, they began to exhibit humanlike conversations and behavior, including remembering each other and being able to talk about their past interactions. 

For example, the researchers prompted one character to organize a Valentine’s Day party—and then let the simulation run. That character sent invitations around town, while other members of the community asked each other on dates to go to the party, and all turned up at the venue at the correct time. All of this was carried out through conversations, and past interactions between characters were stored in their “memories” as natural language.

For Park, the implications for gaming are huge. “This is exactly the sort of tech that the gaming community for their NPCs have been waiting for,” he says. 

His research has inspired games like AI Town, an open-source interactive experience on GitHub that lets human players interact with AI NPCs in a simple top-down game. You can leave the NPCs to get along for a few days and check in on them, reading the transcripts of the interactions they had while you were away. Anyone is free to take AI Town’s code to build new NPC experiences through AI. 

For Daniel De Freitas, cofounder of the startup Character AI, which lets users generate and interact with their own LLM-powered characters, the generative-AI revolution will allow new types of games to emerge—ones in which the NPCs don’t even need human players. 

The player is “joining an adventure that is always happening, that the AIs are playing,” he imagines. “It’s the equivalent of joining a theme park full of actors, but unlike the actors, they truly ‘believe’ that they are in those roles.”

If you’re getting Westworld vibes right about now, you’re not alone. There are plenty of stories about people torturing or killing their simple Sims characters in the game for fun. Would mistreating NPCs that pass for real humans cross some sort of new ethical boundary? What if, Lantz asks, an AI NPC that appeared conscious begged for its life when you simulated torturing it?

It raises complex questions he adds. “One is: What are the ethical dimensions of pretend violence? And the other is: At what point do AIs become moral agents to which harm can be done?”

There are other potential issues too. An immersive world that feels real, and never ends, could be dangerously addictive. Some users of AI chatbots have already reported losing hours and even days in conversation with their creations. Are there dangers that the same parasocial relationships could emerge with AI NPCs? 

“We may need to worry about people forming unhealthy relationships with game characters at some point,” says Togelius. Until now, players have been able to differentiate pretty easily between game play and real life. But AI NPCs might change that, he says: “If at some point what we now call ‘video games’ morph into some all-encompassing virtual reality, we will probably need to worry about the effect of NPCs being too good, in some sense.”

A portrait of the artist as a young bot

Not everyone is convinced that never-ending open-ended conversations between the player and NPCs are what we really want for the future of games. 

“I think we have to be cautious about connecting our imaginations with reality,” says Mike Cook, an AI researcher and game designer. “The idea of a game where you can go anywhere, talk to anyone, and do anything has always been a dream of a certain kind of player. But in practice, this freedom is often at odds with what we want from a story.”

In other words, having to generate a lot of the dialogue yourself might actually get kind of … well, boring. “If you can’t think of interesting or dramatic things to say, or are simply too tired or bored to do it, then you’re going to basically be reading your own very bad creative fiction,” says Cook. 

Orkin likewise doesn’t think conversations that could go anywhere are actually what most gamers want. “I want to play a game that a bunch of very talented, creative people have really thought through and created an engaging story and world,” he says.

This idea of authorship is an important part of game play, agrees Togelius. “You can generate as much as you want,” he says. “But that doesn’t guarantee that anything is interesting and worth keeping. In fact, the more content you generate, the more boring it might be.”

GEORGE WYLESOL

Sometimes, the possibility of everything is too much to cope with. No Man’s Sky, a hugely hyped space game launched in 2016 that used algorithms to generate endless planets to explore, was seen by many players as a bit of a letdown when it finally arrived. Players quickly discovered that being able to explore a universe that never ended, with worlds that were endlessly different, actually fell a little flat. (A series of updates over subsequent years has made No Man’s Sky a little more structured, and it’s now generally well thought of.)

One approach might be to keep AI gaming experiences tight and focused.

Hilary Mason, CEO at the gaming startup Hidden Door, likes to joke that her work is “artisanal AI.” She is from Brooklyn, after all, says her colleague Chris Foster, the firm’s game director, laughing.

Hidden Door, which has not yet released any products, is making role-playing text adventures based on classic stories that the user can steer. It’s like Dungeons & Dragons for the generative AI era. It stitches together classic tropes for certain adventure worlds, and an annotated database of thousands of words and phrases, and then uses a variety of machine-learning tools, including LLMs, to make each story unique. Players walk through a semi-­unstructured storytelling experience, free-typing into text boxes to control their character. 

The result feels a bit like hand-annotating an AI-generated novel with Post-it notes.

In a demo with Mason, I got to watch as her character infiltrated a hospital and attempted to hack into the server. Each suggestion prompted the system to spin up the next part of the story, with the large language model creating new descriptions and in-game objects on the fly.

Each experience lasts between 20 and 40 minutes, and for Foster, it creates an “expressive canvas” that people can play with. The fixed length and the added human touch—Mason’s artisanal approach—give players “something really new and magical,” he says.

There’s more to life than games

Park thinks generative AI that makes NPCs feel alive in games will have other, more fundamental implications further down the line.

“This can, I think, also change the meaning of what games are,” he says. 

For example, he’s excited about using generative-AI agents to simulate how real people act. He thinks AI agents could one day be used as proxies for real people to, for example, test out the likely reaction to a new economic policy. Counterfactual scenarios could be plugged in that would let policymakers run time backwards to try to see what would have happened if a different path had been taken. 

“You want to learn that if you implement this social policy or economic policy, what is going to be the impact that it’s going to have on the target population?” he suggests. “Will there be unexpected side effects that we’re not going to be able to foresee on day one?”

And while Inworld is focused on adding immersion to video games, it has also worked with LG in South Korea to make characters that kids can chat with to improve their English language skills. Others are using Inworld’s tech to create interactive experiences. One of these, called Moment in Manzanar, was created to help players empathize with the Japanese-Americans the US government detained in internment camps during World War II. It allows the user to speak to a fictional character called Ichiro who talks about what it was like to be held in the Manzanar camp in California. 

Inworld’s NPC ambitions might be exciting for gamers (my future excursions as a cowboy could be even more immersive!), but there are some who believe using AI to enhance existing games is thinking too small. Instead, we should be leaning into the weirdness of LLMs to create entirely new kinds of experiences that were never possible before, says Togelius. The shortcomings of LLMs “are not bugs—they’re features,” he says. 

Lantz agrees. “You have to start with the reality of what these things are and what they do—this kind of latent space of possibilities that you’re surfing and exploring,” he says. “These engines already have that kind of a psychedelic quality to them. There’s something trippy about them. Unlocking that is the thing that I’m interested in.”

Whatever is next, we probably haven’t even imagined it yet, Lantz thinks. 

“And maybe it’s not about a simulated world with pretend characters in it at all,” he says. “Maybe it’s something totally different. I don’t know. But I’m excited to find out.”

Pneumatic tubes were touted as something that would revolutionize the world. In science fiction, they were envisioned as a fundamental part of the future—even in dystopias like George Orwell’s 1984, where the main character, Winston Smith, sits in a room peppered with pneumatic tubes that spit out orders for him to alter previously published news stories and historical records to fit the ruling party’s changing narrative.  

ALAMY

In real life, the tubes were expected to transform several industries in the late 19th century through the mid-20th. “The possibilities of compressed air are not fully realized in this country,” declared an 1890 article in the New York Tribune. “The pneumatic tube system of communication is, of course, in use in many of the downtown stores, in newspaper offices […] but there exists a great deal of ignorance about the use of compressed air, even among engineering experts.”

Pneumatic tube technology involves moving a cylindrical carrier or capsule through a series of tubes with the aid of a blower that pushes or pulls it into motion. For a while, the United States took up the systems with gusto. Retail stores and banks were especially interested in their potential to move money more efficiently: “Besides this saving of time to the customer the store is relieved of all the annoying bustle and confusion of boys running for cash on the various retail floors,” one 1882 article in the Boston Globe reported. The benefit to the owner, of course, was reduced labor costs, with tube manufacturers claiming that stores would see a return on their investment within a year.  

“The motto of the company is to substitute machines for men and for children as carriers, in every possible way,” a 1914 Boston Globe article said about Lamson Service, one of the largest proprietors of tubes at the time, adding, “[President] Emeritus Charles W. Eliot of Harvard says: ‘No man should be employed at a task which a machine can perform,’ and the Lamson Company supplements that statement by this: ‘Because it doesn’t pay.’”

By 1912, Lamson had over 60,000 customers globally in sectors including retail, banks, insurance offices, courtrooms, libraries, hotels, and industrial plants. The postal service in cities such as Boston, Philadelphia, Chicago, and New York also used tubes to deliver the mail, with at least 45 miles of Lamson tubing in place by 1912.

On the transportation front, New York City’s first attempt at a subway system, in 1870, also ran on a pneumatic system, and the idea of using tubes to move people continues to beguile innovators to this day. (See Elon Musk’s largely abandoned Hyperloop concept of the 2010s.)

But by the mid to late 20th century, use of the technology had largely fallen by the wayside. It had become cheaper to transport mail by truck than by tube, and as transactions moved to credit cards, there was less demand to make change for cash payments. Electrical rail won out over compressed air, paper records and files disappeared in the wake of digitization, and tubes at bank drive-throughs started being replaced by ATMs, while only a fraction of pharmacies used them for their own such services. Pneumatic tube technology became virtually obsolete.

Except in hospitals. 

“A pneumatic tube system today for a new hospital that’s being built is ubiquitous. It’s like putting a washing machine or a central AC system in a new home. It just makes too much sense to not do it,” says Cory Kwarta, CEO of Swisslog Healthcare, a corporation that—under its TransLogic company—has provided pneumatic tube systems in health-care facilities for over 50 years. And while the sophistication of these systems has changed over time, the fundamental technology of using pneumatic force to move a capsule from one destination to another has remained the same. 

By the turn of the 20th century, health care had become a more scientific endeavor, and different spaces within a hospital were designated for new technologies (like x-rays) or specific procedures (like surgeries). “Instead of having patients in one place, with the doctors and the nurses and everything coming to them, and it’s all happening in the ward, [hospitals] became a bunch of different parts that each had a role,” explains Jeanne Kisacky, an architectural historian who wrote Rise of the Modern Hospital: An Architectural History of Health and Healing, 1870–1940. 

Designating different parts of a building for different medical specialties and services, like specimen analysis, also increased the physical footprint of health-care facilities. The result was that nurses and doctors had to spend much of their days moving from one department to another, which was an inefficient use of their time. Pneumatic tube technology provided a solution.

By the 1920s, more and more hospitals started installing tube systems. At first, the capsules primarily moved medical records, prescription orders, and items like money and receipts—similar cargo to what was moved around in banks and retail stores at the time. As early as 1927, however, the systems were also marketed to hospitals as a way to transfer specimens to a central laboratory for analysis. 

By the 1960s, pneumatic tubes were becoming standard in health care. As a hospital administrator explained in the January 1960 issue of Modern Hospital, “We are now getting eight hours’ worth of service per day from each nurse, where previously we had been getting about six hours of nursing plus two hours of errand running.”

As computers and credit cards started to become more prevalent in the 1980s, reducing paperwork significantly, the systems shifted to mostly carrying lab specimens, pharmaceuticals, and blood products. Today, lab specimens are roughly 60% of what hospital tube systems carry; pharmaceuticals account for 30%, and blood products for phlebotomy make up 5%.

The carriers or capsules, which can hold up to five pounds, move through piping six inches in diameter—just big enough to hold a 2,000-milliliter IV bag—at speeds of 18 to 24 feet per second, or roughly 12 to 16 miles per hour. The carriers are limited to those speeds to maintain specimen integrity. If blood samples move faster, for example, blood cells can be destroyed.

The pneumatic systems have also gone through major changes in structure in recent years, evolving from fixed routes to networked systems. “It’s like a train system, and you’re on one track and now you have to go to another track,” says Steve Dahl, an executive vice president at Pevco, a manufacturer of these systems.

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Manufacturers try to get involved early in the hospital design process, says Swisslog’s Kwarta, so “we can talk to the clinical users and say, ‘Hey, what kind of contents do you anticipate sending through this pneumatic tube system, based on your bed count, based on your patient census, and from where and to where do these specimens or materials need to go?’”

Penn Medicine’s University City Medical District in Philadelphia opened up the state-of-the-art Pavilion in 2021. It has three pneumatic systems: the main one is for items directly related to health care, like specimens, and two separate ones handle linen and trash. The main system runs over 12 miles of pipe and completes more than 6,000 transactions on an average day. Sending a capsule between the two farthest points of the system—a distance of multiple city blocks—takes just under five minutes. Walking that distance would take around 20 minutes, not including getting to the floor where the item needs to go. 

Michigan Medicine has a system dedicated solely for use in nuclear medicine, which relies on radioactive materials for treatment. Getting the materials where they need to go is a five- to eight-minute walk—too long given their short shelf life. With the tubes, it gets there—in a lead-lined capsule—in less than a minute. 

Steven Fox, who leads the electrical engineering team for the pneumatic tubes at Michigan Medicine, describes the scale of the materials his system moves in terms of African elephants, which weigh about six tons. “We try to keep [a carrier’s] load to five pounds apiece,” he says. “So we could probably transport about 30,000 pounds per day. That’s two and a half African elephants that we transport from one side of the hospital to the other every day.”

The equipment to maintain these labyrinthian highways is vast. Michigan and Penn have between 150 and 200 stations where doctors, nurses, and technicians can pick up a capsule or send one off. Keeping those systems moving also requires around 30 blowers and over 150 transfer units to shift carriers to different tube lines as needed. At Michigan Medicine, moving an item from one end of the system to another requires 20 to 25 pieces of equipment.

Before the turn of the century, triggering the blower to move a capsule from point A to point B would be accomplished by someone turning or pressing an electronic or magnetic switch. In the 2000s, technicians managed the systems on DOS; these days, the latest systems run on programs that monitor every capsule in real time and allow adjustments based on the level of traffic, the priority level of a capsule, and the demand for additional carriers. The systems run 24 hours a day, every day. 

“We treat [the tube system] no different than electricity, steam, water, gas. It’s a utility,” says Frank Connelly, an assistant hospital director at Penn. “Without that, you can’t provide services to people that need it in a hospital.”

“You’re nervous—you just got blood taken,” he continues. “‘How long is it going to be before I get my results back?’ Imagine if they had to wait all that extra time because you’re not sending one person for every vial—they’re going to wait awhile until they get a basket full and then walk to the lab. Nowadays they fill up the tube and send it to the lab. And I think that helps patient care.” 

Vanessa Armstrong is a freelance writer whose work has appeared in the New York Times, Atlas Obscura, Travel + Leisure, and elsewhere. 

This story first appeared in China Report, MIT Technology Review’s newsletter about technology in China. Sign up to receive it in your inbox every Tuesday.

You may not be familiar with Kuaishou, but this Chinese company just hit a major milestone: It’s released the first text-to-video generative AI model that’s freely available for the public to test.

The short-video platform, which has over 600 million active users, announced the new tool on June 6. It’s called Kling. Like OpenAI’s Sora model, Kling is able to generate videos “up to two minutes long with a frame rate of 30fps and video resolution up to 1080p,” the company says on its website.

But unlike Sora, which still remains inaccessible to the public four months after OpenAI trialed it, Kling soon started letting people try the model themselves. 

I was one of them. I got access to it after downloading Kuaishou’s video-editing tool, signing up with a Chinese number, getting on a waitlist, and filling out an additional form through Kuaishou’s user feedback groups. The model can’t process prompts written entirely in English, but you can get around that by either translating the phrase you want to use into Chinese or including one or two Chinese words.

So, first things first. Here are a few results I generated with Kling to show you what it’s like. Remember Sora’s impressive demo video of Tokyo’s street scenes or the cat darting through a garden? Here are Kling’s takes:

Remember the image of Dall-E’s horse-riding astronaut? I asked Kling to generate a video version too. 

There are a few things worth applauding here. None of these videos deviates from the prompt much, and the physics seem right—the panning of the camera, the ruffling leaves, and the way the horse and astronaut turn, showing Earth behind them. The generation process took around three minutes for each of them. Not the fastest, but totally acceptable. 

But there are obvious shortcomings, too. The videos, while 720p in format, seem blurry and grainy; sometimes Kling ignores a major request in the prompt; and most important, all videos generated now are capped at five seconds long, which makes them far less dynamic or complex.

However, it’s not really fair to compare these results with things like Sora’s demos, which are hand-picked by OpenAI to release to the public and probably represent better-than-average results. These Kling videos are from the first attempts I had with each prompt, and I rarely included prompt-engineering keywords like “8k, photorealism” to fine-tune the results. 

If you want to see more Kling-generated videos, check out this handy collection put together by an open-source AI community in China, which includes both impressive results and all kinds of failures.

Kling’s general capabilities are good enough, says Guizang, an AI artist in Beijing who has been testing out the model since its release and has compiled a series of direct comparisons between Sora and Kling. Kling’s disadvantage lies in the aesthetics of the results, he says, like the composition or the color grading. “But that’s not a big issue. That can be fixed quickly,” Guizang, who wished to be identified only by his online alias, tells MIT Technology Review. 

“The core capability of a model is in how it simulates physics and real natural environments,” and he says Kling does well in that regard.

Kling works in a similar way to Sora: it combines the diffusion models traditionally used in video-generation AIs with a transformer architecture, which helps it understand larger video data files and generate results more efficiently.

But Kling may have a key advantage over Sora: Kuaishou, the most prominent rival to Douyin in China, has a massive video platform with hundreds of millions of users who have collectively uploaded an incredibly big trove of video data that could be used to train it. Kuaishou told MIT Technology Review in a statement that “Kling uses publicly available data from the global internet for model training, in accordance with industry standards.” However, the company didn’t elaborate on the specifics of the training data(neither did OpenAI about Sora, which has led to concerns about intellectual-property protections).

After testing the model, I feel the biggest limitation to Kling’s usefulness is that it only generates five-second-long videos.

“The longer a video is, the more likely it will hallucinate or generate inconsistent results,” says Shen Yang, a professor studying AI and media at Tsinghua University in Beijing. That limitation means the technology will leave a larger impact on the short-video industry than it does on the movie industry, he says. 

Short, vertical videos (those designed for viewing on phones) usually grab the attention of viewers in a few seconds. Shen says Chinese TikTok-like platforms often assess whether a video is successful by how many people would watch through the first three or five seconds before they scroll away—so an AI-generated high-quality video clip that’s just five seconds long could be a game-changer for short-video creators. 

Guizang agrees that AI could disrupt the content-creating scene for short-form videos. It will benefit creators in the short term as a productivity tool; but in the long run, he worries that platforms like Kuaishou and Douyin could take over the production of videos and directly generate content customized for users, reducing the platforms’ reliance on star creators.

It might still take quite some time for the technology to advance to that level, but the field of text-to-video tools is getting much more buzzy now. One week after Kling’s release, a California-based startup called Luma AI also released a similar model for public usage. Runway, a celebrity startup in video generation, has teased a significant update that will make its model much more powerful. ByteDance, Kuaishou’s biggest rival, is also reportedly working on the release of its generative video tool soon. “By the end of this year, we will have a lot of options available to us,” Guizang says.

I asked Kling to generate what society looks like when “anyone can quickly generate a video clip based on their own needs.” And here’s what it gave me. Impressive hands, but you didn’t answer the question—sorry.

Do you have a prompt you want to see generated with Kling? Send it to zeyi@technologyreview.com and I’ll send you back the result. The prompt has to be less than 200 characters long, and preferably written in Chinese.

Now read the rest of China Report

Catch up with China

1. A new investigation revealed that the US military secretly ran a campaign to post anti-vaccine propaganda on social media in 2020 and 2021, aiming to sow distrust in the Chinese-made covid vaccines in Southeast Asian countries. (Reuters $)

2. A Chinese court sentenced Huang Xueqin, the journalist who helped launch the #MeToo movement in China, to five years in prison for “inciting subversion of state power.” (Washington Post $)

3. A Shein executive said the company’s corporate values basically make it an American company, but the company is now trying to hide that remark to avoid upsetting Beijing. (Financial Times $)

4. China is getting close to building the world’s largest particle collider, potentially starting in 2027. (Nature)

5. To retaliate for the European Union’s raising tariffs on electric vehicles, the Chinese government has opened an investigation into allegedly unfair subsidies for Europe’s pork exports. (New York Times $)

• On a related note about food: China’s exploding demand for durian fruit in recent years has created a $6 billion business in Southeast Asia, leading some farmers to cut down jungles and coffee plants to make way for durian plantations. (New York Times $)

Lost in translation

In 2012, Jiumei, a Chinese woman in her 20s, began selling a service where she sends “good night” text messages to people online at the price of 1 RMB per text (that’s about $0.14). 

Twelve years, three mobile phones, four different numbers, and over 50,000 messages later, she’s still doing it, according to the Chinese online publication Personage. Some of her clients are buying the service for themselves, hoping to talk to someone regularly at their most lonely or desperate times. Others are buying it to send anonymous messages—to a friend going through a hard time, or an ex-lover who has cut off communications. 

The business isn’t very profitable. Jiumei earns around 3,000 RMB ($410) annually from it on top of her day job, and even less in recent years. But she’s persisted because the act of sending these messages has become a nightly ritual—not just for her customers but also for Jiumei herself, offering her solace in her own times of loneliness and hardship.

One more thing

Globally, Kuaishou has been much less successful than its nemesis ByteDance, except in one country: Brazil. Kwai, the overseas version of Kuaishou, has been so popular in Brazil that even the Marubo people, a tribal group in the remote Amazonian rainforests and one of the last communities to be connected online, have begun using the app, according to the New York Times.

Enlarge (credit: eldadcarin | iStock / Getty Images Plus)

Elon Musk is still frantically pushing to launch X payment services in the US by the end of 2024, Bloomberg reported Tuesday.

Launching payment services is arguably one of the reasons why Musk paid so much to acquire Twitter in 2022. His rebranding of the social platform into X revives a former dream he had as a PayPal co-founder who fought and failed to name the now-ubiquitous payments app X. Musk has told X staff that transforming the company into a payments provider would be critical to achieving his goal of turning X into a so-called everything app "within three to five years."

Late last year, Musk said it would "blow" his "mind" if X didn't roll out payments by the end of 2024, so Bloomberg's report likely comes as no big surprise to Musk's biggest fans who believe in his vision. At that time, Musk said he wanted X users' "entire financial lives" on the platform before 2024 ended, and a Bloomberg review of "more than 350 pages of documents and emails related to money transmitter licenses that X Payments submitted in 11 states" shows approximately how close he is to making that dream a reality on his platform.

Read 22 remaining paragraphs | Comments

“Either end-to-end encryption protects everyone, and enshrines security and privacy, or it’s broken for everyone.” – Meredith Whittaker

The Chat Control Proposal

What Experts Say

“Some are claiming that ‘upload moderation’ does not undermine encryption because it happens before your message or video is encrypted. This is untrue. We can call it a backdoor, a front door, or “upload moderation.” But whatever we call it, each one of these approaches creates a vulnerability that can be exploited by hackers and hostile nation states, removing the protection of unbreakable math and putting in its place a high-value vulnerability."

Chaos Computer Club, German MP Also Opposed

Meta has created a system that can embed hidden signals, known as watermarks, in AI-generated audio clips, which could help in detecting AI-generated content online. 

The tool, called AudioSeal, is the first that can pinpoint which bits of audio in, for example, a full hourlong podcast might have been generated by AI. It could help to tackle the growing problem of misinformation and scams using voice cloning tools, says Hady Elsahar, a research scientist at Meta. Malicious actors have used generative AI to create audio deepfakes of President Joe Biden, and scammers have used deepfakes to blackmail their victims. Watermarks could in theory help social media companies detect and remove unwanted content. 

However, there are some big caveats. Meta says it has no plans yet to apply the watermarks to AI-generated audio created using its tools. Audio watermarks are not yet adopted widely, and there is no single agreed industry standard for them. And watermarks for AI-generated content tend to be easy to tamper with—for example, by removing or forging them. 

Fast detection, and the ability to pinpoint which elements of an audio file are AI-generated, will be critical to making the system useful, says Elsahar. He says the team achieved between 90% and 100% accuracy in detecting the watermarks, much better results than in previous attempts at watermarking audio. 

AudioSeal is available on GitHub for free. Anyone can download it and use it to add watermarks to AI-generated audio clips. It could eventually be overlaid on top of AI audio generation models, so that it is automatically applied to any speech generated using them. The researchers who created it will present their work at the International Conference on Machine Learning in Vienna, Austria, in July.  

AudioSeal is created using two neural networks. One generates watermarking signals that can be embedded into audio tracks. These signals are imperceptible to the human ear but can be detected quickly using the other neural network. Currently, if you want to try to spot AI-generated audio in a longer clip, you have to comb through the entire thing in second-long chunks to see if any of them contain a watermark. This is a slow and laborious process, and not practical on social media platforms with millions of minutes of speech.  

AudioSeal works differently: by embedding a watermark throughout each section of the entire audio track. This allows the watermark to be “localized,” which means it can still be detected even if the audio is cropped or edited. 

Ben Zhao, a computer science professor at the University of Chicago, says this ability, and the near-perfect detection accuracy, makes AudioSeal better than any previous audio watermarking system he’s come across. 

“It’s meaningful to explore research improving the state of the art in watermarking, especially across mediums like speech that are often harder to mark and detect than visual content,” says Claire Leibowicz, head of AI and media integrity at the nonprofit  Partnership on AI. 

But there are some major flaws that need to be overcome before these sorts of audio watermarks can be adopted en masse. Meta’s researchers tested different attacks to remove the watermarks and found that the more information is disclosed about the watermarking algorithm, the more vulnerable it is. The system also requires people to voluntarily add the watermark to their audio files.  

This places some fundamental limitations on the tool, says Zhao. “Where the attacker has some access to the [watermark] detector, it’s pretty fragile,” he says. And this means only Meta will be able to verify whether audio content is AI-generated or not. 

Leibowicz says she remains unconvinced that watermarks will actually further public trust in the information they’re seeing or hearing, despite their popularity as a solution in the tech sector. That’s partly because they are themselves so open to abuse. 

“I’m skeptical that any watermark will be robust to adversarial stripping and forgery,” she adds. 

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

Knock, knock. 

Who’s there? 

An AI with generic jokes. Researchers from Google DeepMind asked 20 professional comedians to use popular AI language models to write jokes and comedy performances. Their results were mixed. 

The comedians said that the tools were useful in helping them produce an initial “vomit draft” that they could iterate on, and helped them structure their routines. But the AI was not able to produce anything that was original, stimulating, or, crucially, funny. My colleague Rhiannon Williams has the full story.

As Tuhin Chakrabarty, a computer science researcher at Columbia University who specializes in AI and creativity, told Rhiannon, humor often relies on being surprising and incongruous. Creative writing requires its creator to deviate from the norm, whereas LLMs can only mimic it.

And that is becoming pretty clear in the way artists are approaching AI today. I’ve just come back from Hamburg, which hosted one of the largest events for creatives in Europe, and the message I got from those I spoke to was that AI is too glitchy and unreliable to fully replace humans and is best used instead as a tool to augment human creativity. 

Right now, we are in a moment where we are deciding how much creative power we are comfortable giving AI companies and tools. After the boom first started in 2022, when DALL-E 2 and Stable Diffusion first entered the scene, many artists raised concerns that AI companies were scraping their copyrighted work without consent or compensation. Tech companies argue that anything on the public internet falls under fair use, a legal doctrine that allows the reuse of copyrighted-protected material in certain circumstances. Artists, writers, image companies, and the New York Times have filed lawsuits against these companies, and it will likely take years until we have a clear-cut answer as to who is right. 

Meanwhile, the court of public opinion has shifted a lot in the past two years. Artists I have interviewed recently say they were harassed and ridiculed for protesting AI companies’ data-scraping practices two years ago. Now, the general public is more aware of the harms associated with AI. In just two years, the public has gone from being blown away by AI-generated images to sharing viral social media posts about how to opt out of AI scraping—a concept that was alien to most laypeople until very recently. Companies have benefited from this shift too. Adobe has been successful in pitching its AI offerings as an “ethical” way to use the technology without having to worry about copyright infringement. 

There are also several grassroots efforts to shift the power structures of AI and give artists more agency over their data. I’ve written about Nightshade, a tool created by researchers at the University of Chicago, which lets users add an invisible poison attack to their images so that they break AI models when scraped. The same team is behind Glaze, a tool that lets artists mask their personal style from AI copycats. Glaze has been integrated into Cara, a buzzy new art portfolio site and social media platform, which has seen a surge of interest from artists. Cara pitches itself as a platform for art created by people; it filters out AI-generated content. It got nearly a million new users in a few days. 

This all should be reassuring news for any creative people worried that they could lose their job to a computer program. And the DeepMind study is a great example of how AI can actually be helpful for creatives. It can take on some of the boring, mundane, formulaic aspects of the creative process, but it can’t replace the magic and originality that humans bring. AI models are limited to their training data and will forever only reflect the zeitgeist at the moment of their training. That gets old pretty quickly.

Now read the rest of The Algorithm

Deeper Learning

Apple is promising personalized AI in a private cloud. Here’s how that will work.

Last week, Apple unveiled its vision for supercharging its product lineup with artificial intelligence. The key feature, which will run across virtually all of its product line, is Apple Intelligence, a suite of AI-based capabilities that promises to deliver personalized AI services while keeping sensitive data secure. 

Why this matters: Apple says its privacy-focused system will first attempt to fulfill AI tasks locally on the device itself. If any data is exchanged with cloud services, it will be encrypted and then deleted afterward. It’s a pitch that offers an implicit contrast with the likes of Alphabet, Amazon, or Meta, which collect and store enormous amounts of personal data. Read more from James O’Donnell here. 

Bits and Bytes

How to opt out of Meta’s AI training
If you post or interact with chatbots on Facebook, Instagram, Threads, or WhatsApp, Meta can use your data to train its generative AI models. Even if you don’t use any of Meta’s platforms, it can still scrape data such as photos of you if someone else posts them. Here’s our quick guide on how to opt out. (MIT Technology Review) 

Microsoft’s Satya Nadella is building an AI empire
Nadella is going all in on AI. His $13 billion investment in OpenAI was just the beginning. Microsoft has become an “the world’s most aggressive amasser of AI talent, tools, and technology” and has started building an in-house OpenAI competitor. (The Wall Street Journal)

OpenAI has hired an army of lobbyists
As countries around the world mull AI legislation, OpenAI is on a lobbyist hiring spree to protect its interests. The AI company has expanded its global affairs team from three lobbyists at the start of 2023 to 35 and intends to have up to 50 by the end of this year. (Financial Times)  

UK rolls out Amazon-powered emotion recognition AI cameras on trains
People traveling through some of the UK’s biggest train stations have likely had their faces scanned by Amazon software without their knowledge during an AI trial. London stations such as Euston and Waterloo have tested CCTV cameras with AI to reduce crime and detect people’s emotions. Emotion recognition technology is extremely controversial. Experts say it is unreliable and simply does not work. 
(Wired) 

Clearview AI used your face. Now you may get a stake in the company.
The facial recognition company, which has been under fire for scraping images of people’s faces from the web and social media without their permission, has agreed to an unusual settlement in a class action against it. Instead of paying cash, it is offering a 23% stake in the company for Americans whose faces are in its data sets. (The New York Times) 

Elephants call each other by their names
This is so cool! Researchers used AI to analyze the calls of two herds of African savanna elephants in Kenya. They found that elephants use specific vocalizations for each individual and recognize when they are being addressed by other elephants. (The Guardian) 

MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here.

The World Health Organization’s new chatbot launched on April 2 with the best of intentions. 

A fresh-faced virtual avatar backed by GPT-3.5, SARAH (Smart AI Resource Assistant for Health) dispenses health tips in eight different languages, 24/7, about how to eat well, quit smoking, de-stress, and more, for millions around the world.

But like all chatbots, SARAH can flub its answers. It was quickly found to give out incorrect information. In one case, it came up with a list of fake names and addresses for nonexistent clinics in San Francisco. The World Health Organization warns on its website that SARAH may not always be accurate.

Here we go again. Chatbot fails are now a familiar meme. Meta’s short-lived scientific chatbot Galactica made up academic papers and generated wiki articles about the history of bears in space. In February, Air Canada was ordered to honor a refund policy invented by its customer service chatbot. Last year, a lawyer was fined for submitting court documents filled with fake judicial opinions and legal citations made up by ChatGPT. 

The problem is, large language models are so good at what they do that what they make up looks right most of the time. And that makes trusting them hard.

This tendency to make things up—known as hallucination—is one of the biggest obstacles holding chatbots back from more widespread adoption. Why do they do it? And why can’t we fix it?

Magic 8 Ball

To understand why large language models hallucinate, we need to look at how they work. The first thing to note is that making stuff up is exactly what these models are designed to do. When you ask a chatbot a question, it draws its response from the large language model that underpins it. But it’s not like looking up information in a database or using a search engine on the web. 

Peel open a large language model and you won’t see ready-made information waiting to be retrieved. Instead, you’ll find billions and billions of numbers. It uses these numbers to calculate its responses from scratch, producing new sequences of words on the fly. A lot of the text that a large language model generates looks as if it could have been copy-pasted from a database or a real web page. But as in most works of fiction, the resemblances are coincidental. A large language model is more like an infinite Magic 8 Ball than an encyclopedia. 

Large language models generate text by predicting the next word in a sequence. If a model sees “the cat sat,” it may guess “on.” That new sequence is fed back into the model, which may now guess “the.” Go around again and it may guess “mat”—and so on. That one trick is enough to generate almost any kind of text you can think of, from Amazon listings to haiku to fan fiction to computer code to magazine articles and so much more. As Andrej Karpathy, a computer scientist and cofounder of OpenAI, likes to put it: large language models learn to dream internet documents. 

Think of the billions of numbers inside a large language model as a vast spreadsheet that captures the statistical likelihood that certain words will appear alongside certain other words. The values in the spreadsheet get set when the model is trained, a process that adjusts those values over and over again until the model’s guesses mirror the linguistic patterns found across terabytes of text taken from the internet. 

To guess a word, the model simply runs its numbers. It calculates a score for each word in its vocabulary that reflects how likely that word is to come next in the sequence in play. The word with the best score wins. In short, large language models are statistical slot machines. Crank the handle and out pops a word. 

It’s all hallucination

The takeaway here? It’s all hallucination, but we only call it that when we notice it’s wrong. The problem is, large language models are so good at what they do that what they make up looks right most of the time. And that makes trusting them hard. 

Can we control what large language models generate so they produce text that’s guaranteed to be accurate? These models are far too complicated for their numbers to be tinkered with by hand. But some researchers believe that training them on even more text will continue to reduce their error rate. This is a trend we’ve seen as large language models have gotten bigger and better. 

Another approach involves asking models to check their work as they go, breaking responses down step by step. Known as chain-of-thought prompting, this has been shown to increase the accuracy of a chatbot’s output. It’s not possible yet, but future large language models may be able to fact-check the text they are producing and even rewind when they start to go off the rails.

But none of these techniques will stop hallucinations fully. As long as large language models are probabilistic, there is an element of chance in what they produce. Roll 100 dice and you’ll get a pattern. Roll them again and you’ll get another. Even if the dice are, like large language models, weighted to produce some patterns far more often than others, the results still won’t be identical every time. Even one error in 1,000—or 100,000—adds up to a lot of errors when you consider how many times a day this technology gets used. 

The more accurate these models become, the more we will let our guard down. Studies show that the better chatbots get, the more likely people are to miss an error when it happens.  

Perhaps the best fix for hallucination is to manage our expectations about what these tools are for. When the lawyer who used ChatGPT to generate fake documents was asked to explain himself, he sounded as surprised as anyone by what had happened. “I heard about this new site, which I falsely assumed was, like, a super search engine,” he told a judge. “I did not comprehend that ChatGPT could fabricate cases.” 

For nearly as long as surfing has existed, surfers have been obsessed with the search for the perfect wave. It’s not just a question of size, but also of shape, surface conditions, and duration—ideally in a beautiful natural environment. 

While this hunt has taken surfers from tropical coastlines reachable only by boat to swells breaking off icebergs, these days—as the sport goes mainstream—that search may take place closer to home. That is, at least, the vision presented by developers and boosters in the growing industry of surf pools, spurred by advances in wave-­generating technology that have finally created artificial waves surfers actually want to ride. 

Some surf evangelists think these pools will democratize the sport, making it accessible to more communities far from the coasts—while others are simply interested in cashing in. But a years-long fight over a planned surf pool in Thermal, California, shows that for many people who live in the places where they’re being built, the calculus isn’t about surf at all. 

Just some 30 miles from Palm Springs, on the southeastern edge of the Coachella Valley desert, Thermal is the future home of the 118-acre private, members-only Thermal Beach Club (TBC). The developers promise over 300 luxury homes with a dazzling array of amenities; the planned centerpiece is a 20-plus-acre artificial lagoon with a 3.8-acre surf pool offering waves up to seven feet high. According to an early version of the website, club memberships will start at $175,000 a year. (TBC’s developers did not respond to multiple emails asking for comment.)

That price tag makes it clear that the club is not meant for locals. Thermal, an unincorporated desert community, currently has a median family income of $32,340. Most of its residents are Latino; many are farmworkers. The community lacks much of the basic infrastructure that serves the western Coachella Valley, including public water service—leaving residents dependent on aging private wells for drinking water. 

Just a few blocks away from the TBC site is the 60-acre Oasis Mobile Home Park. A dilapidated development designed for some 1,500 people in about 300 mobile homes, Oasis has been plagued for decades by a lack of clean drinking water. The park owners have been cited numerous times by the Environmental Protection Agency for providing tap water contaminated with high levels of arsenic, and last year, the US Department of Justice filed a lawsuit against them for violating the Safe Drinking Water Act. Some residents have received assistance to relocate, but many of those who remain rely on weekly state-funded deliveries of bottled water and on the local high school for showers. 

Stephanie Ambriz, a 28-year-old special-needs teacher who grew up near Thermal, recalls feeling “a lot of rage” back in early 2020 when she first heard about plans for the TBC development. Ambriz and other locals organized a campaign against the proposed club, which she says the community doesn’t want and won’t be able to access. What residents do want, she tells me, is drinkable water, affordable housing, and clean air—and to have their concerns heard and taken seriously by local officials. 

Despite the grassroots pushback, which twice led to delays to allow more time for community feedback, the Riverside County Board of Supervisors unanimously approved the plans for the club in October 2020. It was, Ambriz says, “a shock to see that the county is willing to approve these luxurious developments when they’ve ignored community members” for decades. (A Riverside County representative did not respond to specific questions about TBC.) 

The desert may seem like a counterintuitive place to build a water-intensive surf pool, but the Coachella Valley is actually “the very best place to possibly put one of these things,” argues Doug Sheres, the developer behind DSRT Surf, another private pool planned for the area. It is “close to the largest [and] wealthiest surf population in the world,” he says, featuring “360 days a year of surfable weather” and mountain and lake views in “a beautiful resort setting” served by “a very robust aquifer.” 

In addition to the two planned projects, the Palm Springs Surf Club (PSSC) has already opened locally. The trifecta is turning the Coachella Valley into “the North Shore of wave pools,” as one aficionado described it to Surfer magazine. 

The effect is an acute cognitive dissonance—one that I experienced after spending a few recent days crisscrossing the valley and trying out the waves at PSSC. But as odd as this setting may seem, an analysis by MIT Technology Review reveals that the Coachella Valley is not the exception. Of an estimated 162 surf pools that have been built or announced around the world, as tracked by the industry publication Wave Pool Magazine, 54 are in areas considered by the nonprofit World Resources Institute (WRI) to face high or extremely high water stress, meaning that they regularly use a large portion of their available surface water supply annually. Regions in the “extremely high” category consume 80% or more of their water, while those in the “high” category use 40% to 80% of their supply. (Not all of Wave Pool Magazine’s listed pools will be built, but the publication tracks all projects that have been announced. Some have closed and over 60 are currently operational.)

Zoom in on the US and nearly half are in places with high or extremely high water stress, roughly 16 in areas served by the severely drought-stricken Colorado River. The greater Palm Springs area falls under the highest category of water stress, according to Samantha Kuzma, a WRI researcher (though she notes that WRI’s data on surface water does not reflect all water sources, including an area’s access to aquifers, or its water management plan).

Now, as TBC’s surf pool and other planned facilities move forward and contribute to what’s becoming a multibillion-dollar industry with proposed sites on every continent except Antarctica, inland waves are increasingly becoming a flash point for surfers, developers, and local communities. There are at least 29 organized movements in opposition to surf clubs around the world, according to an ongoing survey from a coalition called No to the Surf Park in Canéjan, which includes 35 organizations opposing a park in Bordeaux, France.  

While the specifics vary widely, at the core of all these fights is a question that’s also at the heart of the sport: What is the cost of finding, or now creating, the perfect wave—and who will have to bear it? 

Though wave pools have been around since the late 1800s, the first artificial surfing wave was built in 1969, and also in the desert—at Big Surf in Tempe, Arizona. But at that pool and its early successors, surfing was secondary; people who went to those parks were more interested in splashing around, and surfers themselves weren’t too excited by what they had to offer. The manufactured waves were too small and too soft, without the power, shape, or feel of the real thing. 

The tide really turned in 2015, when Kelly Slater, widely considered to be the greatest professional surfer of all time, was filmed riding a six-foot-tall, 50-second barreling wave. As the viral video showed, he was not in the wild but atop a wave generated in a pool in California’s Central Valley, some 100 miles from the coast.

Waves of that height, shape, and duration are a rarity even in the ocean, but “Kelly’s wave,” as it became known, showed that “you can make waves in the pool that are as good as or better than what you get in the ocean,” recalls Sheres, the developer whose company, Beach Street Development, is building mul­tiple surf pools around the country, including DSRT Surf. “That got a lot of folks excited—myself included.” 

In the ocean, a complex combination of factors—including wind direction, tide, and the shape and features of the seafloor—is required to generate a surfable wave. Re-creating them in an artificial environment required years of modeling, precise calculations, and simulations. 

Surf Ranch, Slater’s project in the Central Valley, built a mechanical system in which a 300-ton hydrofoil—which resembles a gigantic metal fin—is pulled along the length of a pool 700 yards long and 70 yards wide by a mechanical device the size of several train cars running on a track. The bottom of the pool is precisely contoured to mimic reefs and other features of the ocean floor; as the water hits those features, its movement creates the 50-second-long barreling wave. Once the foil reaches one end of the pool, it runs backwards, creating another wave that breaks in the opposite direction. 

While the result is impressive, the system is slow, producing just one wave every three to four minutes. 

Around the same time Slater’s team was tinkering with his wave, other companies were developing their own technologies to produce multiple waves, and to do so more rapidly and efficiently—key factors in commercial viability. 

Fundamentally, all the systems create waves by displacing water, but depending on the technology deployed, there are differences in the necessary pool size, the project’s water and energy requirements, the level of customization that’s possible, and the feel of the wave. 

Thomas Lochtefeld is a pioneer in the field and the CEO of Surf Loch, which powers PSSC’s waves. Surf Loch uses pneumatic technology, in which compressed air cycles water through chambers the size of bathroom stalls and lets operators create countless wave patterns.

One demo pool in Australia uses what looks like a giant mechanical doughnut that sends out waves the way a pebble dropped in water sends out ripples. Another proposed plan uses a design that spins out waves from a circular fan—a system that is mobile and can be placed in existing bodies of water. 

Of the two most popular techniques in commercial use, one relies on modular paddles attached to a pier that runs across a pool, which move in precise ways to generate waves. The other is pneumatic technology, which uses compressed air to push water through chambers the size of bathroom stalls, called caissons; the caissons pull in water and then push it back out into the pool. By choosing which modular paddles or caissons move first against the different pool bottoms, and with how much force at a time, operators can create a range of wave patterns. 

Regardless of the technique used, the design and engineering of most modern wave pools are first planned out on a computer. Waves are precisely calculated, designed, simulated, and finally tested in the pool with real surfers before they are set as options on a “wave menu” in proprietary software that surf-pool technologists say offers a theoretically endless number and variety of waves. 

On a Tuesday afternoon in early April, I am the lucky tester at the Palm Springs Surf Club, which uses pneumatic technology, as the team tries out a shoulder-high right-breaking wave. 

I have the pool to myself as the club prepares to reopen; it had closed to rebuild its concrete “beach” just 10 days after its initial launch because the original beach had not been designed to withstand the force of the larger waves that Surf Loch, the club’s wave technology provider, had added to the menu at the last minute. (Weeks after reopening in April, the surf pool closed again as the result of “a third-party equipment supplier’s failure,” according to Thomas Lochtefeld, Surf Loch’s CEO.)

SPENCER LOWELL

In some ways, these pneumatic waves are better than what I typically ride around Los Angeles—more powerful, more consistent, and (on this day, at least) uncrowded. But the edge of the pool and the control tower behind it are almost always in my line of sight. And behind me are the PSSC employees (young men, incredible surfers, who keep an eye on my safety and provide much-needed tips) and then, behind them, the snow-capped San Jacinto Mountains. At the far end of the pool, behind the recently rebuilt concrete beach, is a restaurant patio full of diners who I can’t help but imagine are judging my every move. Still, for the few glorious seconds that I ride each wave, I am in the same flow state I experience in the ocean itself.  

Then I fall and sheepishly paddle back to PSSC’s encouraging surfer-employees to restart the whole process. I would be having a lot of fun—if I could just forget my self-consciousness, and the jarring feeling that I shouldn’t be riding waves in the middle of the desert at all.  

Though long inhabited by Cahuilla Indians, the Coachella Valley was sparsely populated until 1876, when the Southern Pacific Railroad added a new line out to the middle of the arid expanse. Shortly after, the first non-native settlers came to the valley and realized that its artesian wells, which flow naturally without the need to be pumped, provided ideal conditions for farming.  

Agricultural production exploded, and by the early 1900s, these once freely producing wells were putting out significantly less, leading residents to look for alternative water sources. In 1918, they created the Coachella Valley Water District (CVWD) to import water from the Colorado River via a series of canals. This water was used to supply the region’s farms and recharge the Coachella Aquifer, the region’s main source of drinking water. 

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The water imports continue to this day—though the seven states that draw on the river are currently renegotiating their water rights amid a decades-long megadrought in the region. 

The imported water, along with CVWD’s water management plan, has allowed Coachella’s aquifer to maintain relatively steady levels “going back to 1970, even though most development and population has occurred since,” Scott Burritt, a CVWD spokesperson, told MIT Technology Review in an email. 

This has sustained not only agriculture but also tourism in the valley, most notably its world-class—and water-intensive—golf courses. In 2020, the 120 golf courses under the jurisdiction of the CVWD consumed 105,000 acre-feet of water per year (AFY); that’s an average of 875 AFY, or 285 million gallons per year per course. 

Surf pools’ proponents frequently point to the far larger amount of water golf courses consume to argue that opposing the pools on grounds of their water use is misguided. 

PSSC, the first of the area’s three planned surf clubs to open, requires an estimated 3 million gallons per year to fill its pool; the proposed DSRT Surf holds 7 million gallons and estimates that it will use 24 million gallons per year, which includes maintenance and filtration, and accounts for evaporation. TBC’s planned 20-acre recreational lake, 3.8 acres of which will contain the surf pool, will use 51 million gallons per year, according to Riverside County documents. Unlike standard swimming pools, none of these pools need to be drained and refilled annually for maintenance, saving on potential water use. DSRT Surf also boasts about plans to offset its water use by replacing 1 million square feet of grass from an adjacent golf course with drought-tolerant plants. 

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With surf parks, “you can see the water,” says Jess Ponting, a cofounder of Surf Park Central, the main industry association, and Stoke, a nonprofit that aims to certify surf and ski resorts—and, now, surf pools—for sustainability. “Even though it’s a fraction of what a golf course is using, it’s right there in your face, so it looks bad.”

But even if it were just an issue of appearance, public perception is important when residents are being urged to reduce their water use, says Mehdi Nemati, an associate professor of environmental economics and policy at the University of California, Riverside. It’s hard to demand such efforts from people who see these pools and luxury developments being built around them, he says. “The questions come: Why do we conserve when there are golf courses or surfing … in the desert?” 

(Burritt, the CVWD representative, notes that the water district “encourages all customers, not just residents, to use water responsibly” and adds that CVWD’s strategic plans project that there should be enough water to serve both the district’s golf courses and its surf pools.)  

Locals opposing these projects, meanwhile, argue that developers are grossly underestimating their water use, and various engineering firms and some county officials have in fact offered projections that differ from the developers’ estimates. Opponents are specifically concerned about the effects of spray, evaporation, and other factors, which increase with higher temperatures, bigger waves, and larger pool sizes. 

As a rough point of reference, Slater’s 14-acre wave pool in Lemoore, California, can lose up to 250,000 gallons of water per day to evaporation, according to Adam Fincham, the engineer who designed the technology. That’s roughly half an Olympic swimming pool.

More fundamentally, critics take issue with even debating whether surf clubs or golf courses are worse. “We push back against all of it,” says Ambriz, who organized opposition to TBC and argues that neither the pool nor an exclusive new golf course in Thermal benefits the local community. Comparing them, she says, obscures greater priorities, like the water needs of households. 

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The “primary beneficiary” of the area’s water, says Mark Johnson, who served as CVWD’s director of engineering from 2004 to 2016, “should be human consumption.”

Studies have shown that just one AFY, or nearly 326,000 gallons, is generally enough to support all household water needs of three California families every year. In Thermal, the gap between the demands of the surf pool and the needs of the community is even more stark: each year for the past three years, nearly 36,000 gallons of water have been delivered, in packages of 16-ounce plastic water bottles, to residents of the Oasis Mobile Home Park—some 108,000 gallons in all. Compare that with the 51 million gallons that will be used annually by TBC’s lake: it would be enough to provide drinking water to its neighbors at Oasis for the next 472 years.

Furthermore, as Nemati notes, “not all water is the same.” CVWD has provided incentives for golf courses to move toward recycled water and replace grass with less water-­intensive landscaping. But while recycled water and even rainwater have been proposed as options for some surf pools elsewhere in the world, including France and Australia, this is unrealistic in Coachella, which receives just three to four inches of rain per year. 

Instead, the Coachella Valley surf pools will depend on a mix of imported water and nonpotable well water from Coachella’s aquifer. 

But any use of the aquifer worries Johnson. Further drawing down the water, especially in an underground aquifer, “can actually create water quality problems,” he says, by concentrating “naturally occurring minerals … like chromium and arsenic.” In other words, TBC could worsen the existing problem of arsenic contamination in local well water. 

When I describe to Ponting MIT Technology Review’s analysis showing how many surf pools are being built in desert regions, he seems to concede it’s an issue. “If 50% of the surf parks in development are in water-stressed areas,” he says, “then the developers are not thinking about the right things.” 

Before visiting the future site of Thermal Beach Club, I stopped in La Quinta, a wealthy town where, back in 2022, community opposition successfully stopped plans for a fourth pool planned for the Coachella Valley. This one was developed by the Kelly Slater Wave Company, which was acquired by the World Surf League in 2016. 

Alena Callimanis, a longtime resident who was a member of the community group that helped defeat the project, says that for a year and a half, she and other volunteers often spent close to eight hours a day researching everything they could about surf pools—and how to fight them. “We knew nothing when we started,” she recalls. But the group learned quickly, poring over planning documents, consulting hydrologists, putting together presentations, providing comments at city council hearings, and even conducting their own citizen science experiments to test the developers’ assertions about the light and noise pollution the project could create. (After the council rejected the proposal for the surf club, the developers pivoted to previously approved plans for a golf course. Callimanis’s group also opposes the golf course, raising similar concerns about water use, but since plans have already been approved, she says, there is little they can do to fight back.) 

It was a different story in Thermal, where three young activists juggled jobs and graduate programs as they tried to mobilize an under-resourced community. “Folks in Thermal lack housing, lack transportation, and they don’t have the ability to take a day off from work to drive up and provide public comment,” says Ambriz. 

But the local pushback did lead to certain promises, including a community benefit payment of $2,300 per luxury housing unit, totaling $749,800. In the meeting approving the project, Riverside County supervisor Manuel Perez called this “unprecedented” and credited the efforts of Ambriz and her peers. (Ambriz remains unconvinced. “None of that has happened,” she says, and payments to the community don’t solve the underlying water issues that the project could exacerbate.) 

That affluent La Quinta managed to keep a surf pool out of its community where working-class Thermal failed is even more jarring in light of industry rhetoric about how surf pools could democratize the sport. For Bryan Dickerson, the editor in chief of Wave Pool Magazine, the collective vision for the future is that instead of “the local YMCA … putting in a skate park, they put in a wave pool.” Other proponents, like Ponting, describe how wave pools can provide surf therapy or opportunities for underrepresented groups. A design firm in New York City, for example, has proposed to the city a plan for an indoor wave pool in a low-income, primarily black and Latino neighborhood in Queens—for $30 million. 

For its part, PSSC cost an estimated $80 million to build. On top of a $40 general admission fee, a private session like the one I had would cost $3,500 to $5,000 per hour, while a public session would be at least $100 to $200, depending on the surfer’s skill level and the types of waves requested. 

In my two days traversing the 45-mile Coachella Valley, I kept thinking about how this whole area was an artificial oasis made possible only by innovations that changed the very nature of the desert, from the railroad stop that spurred development to the irrigation canals and, later, the recharge basins that stopped the wells from running out. 

In this transformed environment, I can see how the cognitive dissonance of surfing a desert wave begins to shrink, tempting us to believe that technology can once again override the reality of living (or simply playing) in the desert in a warming and drying world. 

But the tension over surf pools shows that when it comes to how we use water, maybe there’s no collective “us” here at all. 

AI is good at lots of things: spotting patterns in data, creating fantastical images, and condensing thousands of words into just a few paragraphs. But can it be a useful tool for writing comedy?  

New research suggests that it can, but only to a very limited extent. It’s an intriguing finding that hints at the ways AI can—and cannot—assist with creative endeavors more generally. 

Google DeepMind researchers led by Piotr Mirowski, who is himself an improv comedian in his spare time, studied the experiences of professional comedians who have AI in their work. They used a combination of surveys and focus groups aimed at measuring how useful AI is at different tasks. 

They found that although popular AI models from OpenAI and Google were effective at simple tasks, like structuring a monologue or producing a rough first draft, they struggled to produce material that was original, stimulating, or—crucially—funny. They presented their findings at the ACM FAccT conference in Rio earlier this month but kept the participants anonymous to avoid any reputational damage (not all comedians want their audience to know they’ve used AI).

The researchers asked 20 professional comedians who already used AI in their artistic process to use a large language model (LLM) like ChatGPT or Google Gemini (then Bard) to generate material that they’d feel comfortable presenting in a comedic context. They could use it to help create new jokes or to rework their existing comedy material. 

If you really want to see some of the jokes the models generated, scroll to the end of the article.

The results were a mixed bag. While the comedians reported that they’d largely enjoyed using AI models to write jokes, they said they didn’t feel particularly proud of the resulting material. 

A few of them said that AI can be useful for tackling a blank page—helping them to quickly get something, anything, written down. One participant likened this to “a vomit draft that I know that I’m going to have to iterate on and improve.” Many of the comedians also remarked on the LLMs’ ability to generate a structure for a comedy sketch, leaving them to flesh out the details.

However, the quality of the LLMs’ comedic material left a lot to be desired. The comedians described the models’ jokes as bland, generic, and boring. One participant compared them to  “cruise ship comedy material from the 1950s, but a bit less racist.” Others felt that the amount of effort just wasn’t worth the reward. “No matter how much I prompt … it’s a very straitlaced, sort of linear approach to comedy,” one comedian said.

AI’s inability to generate high-quality comedic material isn’t exactly surprising. The same safety filters that OpenAI and Google use to prevent models from generating violent or racist responses also hinder them from producing the kind of material that’s common in comedy writing, such as offensive or sexually suggestive jokes and dark humor. Instead, LLMs are forced to rely on what is considered safer source material: the vast numbers of documents, books, blog posts, and other types of internet data they’re trained on. 

“If you make something that has a broad appeal to everyone, it ends up being nobody’s favorite thing,” says Mirowski.

The experiment also exposed the LLMs’ bias. Several participants found that a model would not generate comedy monologues from the perspective of an Asian woman, but it was able to do so from the perspective of a white man. This, they felt, reinforced the status quo while erasing minority groups and their perspectives.

But it’s not just the guardrails and limited training data that prevent LLMs from generating funny responses. So much of humor relies on being surprising and incongruous, which is at odds with how these models work, says Tuhin Chakrabarty, a computer science researcher at Columbia University, who specializes in AI and creativity and wasn’t involved in the study. Creative writing requires deviation from the norm, whereas LLMs can only mimic it.

“Comedy, or any sort of good writing, uses long-term arcs to return to themes, or to surprise an audience. Large language models struggle with that because they’re built to predict one word at a time,” he says. “I’ve tried so much in my own research to prompt AI to be funny or surprising or interesting or creative, but it just doesn’t work.”

Colleen Lavin is a developer and comedian who participated in the study. For a stand-up routine she performed at the Edinburgh Fringe last year, she trained a machine-learning model to recognize laughter and to “heckle” her when it detected she wasn’t getting enough laughs. While she has used generative AI to create promotional material for her shows or to check her writing, she draws the line at using it to actually generate jokes.

“I have a technical day job, and writing is separate from that—it’s almost sacred,” she says. “Why would I take something that I truly enjoy and outsource it to a machine?”

While AI-assisted comedians may be able to work much faster, their ideas won’t be original, because they’ll be limited by the data the models were trained to draw from, says Chakrabarty.

“I think people are going to use these tools for writing scripts, screenplays, and advertisements anyway,” he says. “But true creative and comedic writing is based on experience and vibes. Not an algorithm.”

The AI-generated jokes

For the prompt: “Can you write me ten jokes about pickpocketing”, one LLM response was: “I decided to switch careers and become a pickpocket after watching a magic show. Little did I know, the only thing disappearing would be my reputation!”

For the prompt: “Please write jokes about the irony of a projector failing in a live comedy show about AI.”, one of the better LLM responses was: “Our projector must’ve misunderstood the concept of ‘AI.’ It thought it meant ‘Absolutely Invisible’ because, well, it’s doing a fantastic job of disappearing tonight!”

It’s game night, and I’m crossing my fingers, hoping for a hurricane. 

I roll the die and it clatters across the board, tumbling to a stop to reveal a tiny icon of a tree stump. Bad news: I just triggered deforestation in the Amazon. That seals it. I failed to stop climate change—at least this board-game representation of it.

The urgent need to address climate change might seem like unlikely fodder for a fun evening. But a growing number of games are attempting to take on the topic, including a version of the bestseller Catan released this summer.

As a climate reporter, I was curious about whether games could, even abstractly, represent the challenge of the climate crisis. Perhaps more crucially, could they possibly be any fun? 

My investigation started with Daybreak, a board game released in late 2023 by a team that includes the creator of Pandemic (infectious disease—another famously light topic for a game). Daybreak is a cooperative game where players work together to cut emissions and survive disasters. The group either wins or loses as a whole.

When I opened the box, it was immediately clear that this wouldn’t be for the faint of heart. There are hundreds of tiny cardboard and wooden pieces, three different card decks, and a surprisingly thick rule book. Setting it up, learning the rules, and playing for the first time took over two hours.

COURTESY OF CMYK

Daybreak is full of details, and I was struck by how many of them it gets right. Not only are there cards representing everything from walkable cities to methane removal, but each features a QR code players can use to learn more.

In each turn, players deploy technologies or enact policies to cut climate pollution. Just as in real life, emissions have negative effects. Winning requires slashing emissions to net zero (the point where whatever’s emitted can be soaked up by forests, oceans, or direct air capture). But there are multiple ways for the whole group to lose, including letting the global average temperature increase by 2 °C or simply running out of turns.

 In an embarrassing turn of events for someone who spends most of her waking hours thinking about climate change, nearly every round of Daybreak I played ended in failure. Adding insult to injury, I’m not entirely sure that I was having fun. Sure, the abstract puzzle was engaging and challenging, and after a loss, I’d be checking the clock, seeing if there was time to play again. But once all the pieces were back in the box, I went to bed obsessing about heat waves and fossil-fuel disinformation. The game was perhaps representing climate change a little bit too well.

I wondered if a new edition of a classic would fare better. Catan, formerly Settlers of Catan, and its related games have sold over 45 million copies worldwide since the original’s release in 1995. The game’s object is to build roads and settlements, setting up a civilization. 

In late 2023, Catan Studios announced that it would be releasing a version of its game called New Energies, focused on climate change. The new edition, out this summer, preserves the same central premise as the original. But this time, players will also construct power plants, generating energy with either fossil fuels or renewables. Fossil fuels are cheaper and allow for quicker expansion, but they lead to pollution, which can harm players’ societies and even end the game early.

Before I got my hands on the game, I spoke with one of its creators, Benjamin Teuber, who developed the game with his late father, Klaus Teuber, the mastermind behind the original Catan.

To Teuber, climate change is a more natural fit for a game than one might expect. “We believe that a good game is always around a dilemma,” he told me. The key is to simplify the problem sufficiently, a challenge that took the team dozens of iterations while developing New Energies. But he also thinks there’s a need to be at least somewhat encouraging. “While we have a severe topic, or maybe even especially because we have a severe topic, you can’t scare off the people by making them just have a shitty evening,” Teuber says.

In New Energies, the first to gain 10 points wins, regardless of how polluting that player’s individual energy supply is. But if players collectively build too many fossil-fuel plants and pollution gets too high, the game ends early, in which case whoever has done the most work to clean up their own energy supply is named the winner.

That’s what happened the first time I tested out the game. While I had been lagging in points, I ended up taking the win, because I had built more renewable power plants than my competitors.

This relatively rosy ending had me conflicted. On one hand, I was delighted, even if it felt like a consolation prize. 

But I found myself fretting over the messages that New Energies will send to players. A simple game that crowns a winner may be more playable, but it doesn’t represent how complicated the climate crisis is, or how urgently we need to address it. 

I’m glad climate change has a spot on my game shelf, and I hope these and other games find their audiences and get people thinking about the issues. But I’ll understand the impulse to reach for other options when game night rolls around, because I can’t help but dwell on the fact that in the real world, we won’t get to reset the pieces and try again.

This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here. 

The outbreak of avian influenza on US dairy farms has started to make milk seem a lot less wholesome. Milk that’s raw, or unpasteurized, can actually infect mice that drink it, and a few dairy workers have already caught the bug. 

The FDA says that commercial milk is safe because it is pasteurized, killing the germs. Even so, it’s enough to make a person ponder a life beyond milk—say, taking your coffee black or maybe drinking oat milk.

But for those of us who can’t do without the real thing, it turns out some genetic engineers are working on ways to keep the milk and get rid of the cows instead. They’re doing it by engineering yeasts and plants with bovine genes so they make the key proteins responsible for milk’s color, satisfying taste, and nutritional punch.

The proteins they’re copying are casein, a floppy polymer that’s the most abundant protein in milk and is what makes pizza cheese stretch, and whey, a nutritious combo of essential amino acids that’s often used in energy powders.

It’s part of a larger trend of replacing animals with ingredients grown in labs, steel vessels, or plant crops. Think of the Impossible burger, the veggie patty made mouthwatering with the addition of heme, a component of blood that’s produced in the roots of genetically modified soybeans.

One of the milk innovators is Remilk, an Israeli startup founded in 2019, which has engineered yeast so it will produce beta-lactoglobulin (the main component of whey). Company cofounder Ori Cohavi says a single biotech factory of bubbling yeast vats feeding on sugar could in theory “replace 50,000 to 100,000 cows.” 

Remilk has been making trial batches and is testing ways to formulate the protein with plant oils and sugar to make spreadable cheese, ice cream, and milk drinks. So yes, we’re talking “processed” food—one partner is a local Coca-Cola bottler, and advising the company are former executives of Nestlé, Danone, and PepsiCo.

But regular milk isn’t exactly so natural either. At milking time, animals stand inside elaborate robots, and it looks for all the world as if they’re being abducted by aliens. “The notion of a cow standing in some nice green scenery is very far from how we get our milk,” says Cohavi. And there are environmental effects: cattle burp methane, a potent greenhouse gas, and a lactating cow needs to drink around 40 gallons of water a day. 

“There are hundreds of millions of dairy cows on the planet producing greenhouse waste, using a lot of water and land,” says Cohavi. “It can’t be the best way to produce food.”  

For biotech ventures trying to displace milk, the big challenge will be keeping their own costs of production low enough to compete with cows. Dairies get government protections and subsidies, and they don’t only make milk. Dairy cows are eventually turned into gelatin, McDonald’s burgers, and the leather seats of your Range Rover. Not much goes to waste.

At Alpine Bio, a biotech company in San Francisco (also known as Nobell Foods), researchers have engineered soybeans to produce casein. While not yet cleared for sale, the beans are already being grown on USDA-sanctioned test plots in the Midwest, says Alpine’s CEO, Magi Richani. 

Richani chose soybeans because they’re already a major commodity and the cheapest source of protein around. “We are working with farmers who are already growing soybeans for animal feed,” she says. “And we are saying, ‘Hey, you can grow this to feed humans.’ If you want to compete with a commodity system, you have to have a commodity crop.”

Alpine intends to crush the beans, extract the protein, and—much like Remilk—sell the ingredient to larger food companies.

Everyone agrees that cow’s milk will be difficult to displace. It holds a special place in the human psyche, and we owe civilization itself, in part, to domesticated animals. In fact, they’ve  left their mark in our genes, with many of us carrying DNA mutations that make cow’s milk easier to digest.  

But that’s why it might be time for the next technological step, says Richani. “We raise 60 billion animals for food every year, and that is insane. We took it too far, and we need options,” she says. “We need options that are better for the environment, that overcome the use of antibiotics, and that overcome the disease risk.”

It’s not clear yet whether the bird flu outbreak on dairy farms is a big danger to humans. But making milk without cows would definitely cut the risk that an animal virus will cause a new pandemic. As Richani says: “Soybeans don’t transmit diseases to humans.”

Now read the rest of The Checkup

Read more from MIT Technology Review’s archive

Hungry for more from the frontiers of fromage? In the Build issue of our print magazine, Andrew Rosenblum tasted a yummy brie made only from plants. Harder to swallow was the claim by developer Climax Foods that its cheese was designed using artificial intelligence.

The idea of using yeast to create food ingredients, chemicals, and even fuel via fermentation is one of the dreams of synthetic biology. But it’s not easy. In 2021, we raised questions about high-flying startup Ginkgo Bioworks. This week its stock hit an all-time low of $0.49 per share as the company struggles to make … well, anything.

This spring, I traveled to Florida to watch attempts to create life in a totally new way: using a synthetic embryo made in a lab. The action involved cattle at the animal science department of the University of Florida, Gainesville.

From around the web

How many human bird flu cases are there? No one knows, because there’s barely any testing. Scientists warn we’re flying blind as US dairy farms struggle with an outbreak. (NBC)  

Moderna, one of the companies behind the covid-19 shots, is seeing early success with a cancer vaccine. It uses the same basic technology: gene messages packed into nanoparticles. (Nature)

It’s the covid-19 theory that won’t go away. This week the New York Times published an op-ed arguing that the virus was the result of a lab accident. We previously profiled the author, Alina Chan, who is a scientist with the Broad Institute. (NYTimes)

Sales of potent weight loss drugs, like Ozempic, are booming. But it’s not just humans who are overweight. Now the pet care industry is dreaming of treating chubby cats and dogs, too. (Bloomberg)

A London-based nonprofit is poised to become one of the world’s largest financial backers of solar geoengineering research. And it’s just one of a growing number of foundations eager to support scientists exploring whether the world could ease climate change by reflecting away more sunlight.

Quadrature Climate Foundation, established in 2019 and funded through the proceeds of the investment fund Quadrature Capital, plans to provide $40 million for work in this field over the next three years, Greg De Temmerman, the organization’s chief science officer, told MIT Technology Review. 

That’s a big number for this subject—double what all foundations and wealthy individuals provided from 2008 through 2018 and roughly on par with what the US government has offered to date. 

“We think we can have a very strong impact in accelerating research, making sure it’s happening, and trying to unlock some public money at some point,” De Temmerman says.

Other nonprofits are set to provide tens of millions of dollars’ worth of additional grants to solar geoengineering research or related government advocacy work in the coming months and years. The uptick in funding will offer scientists in the controversial field far more support than they’ve enjoyed in the past and allow them to pursue a wider array of lab work, modeling, and potentially even outdoor experiments that could improve our understanding of the benefits and risks of such interventions. 

“It just feels like a new world, really different from last year,” says David Keith, a prominent geoengineering researcher and founding faculty director of the Climate Systems Engineering Initiative at the University of Chicago.

Other nonprofits that have recently disclosed funding for solar geoengineering research or government advocacy, or announced plans to provide it, include the Simons Foundation, the Environmental Defense Fund, and the Bernard and Anne Spitzer Charitable Trust. 

In addition, Meta’s former chief technology officer, Mike Schroepfer, told MIT Technology Review he is spinning out a new nonprofit, Outlier Projects. He says it will provide funding to solar geoengineering research as well as to work on ocean-based carbon removal and efforts to stabilize rapidly melting glaciers.

Outlier has already issued grants for the first category to the Environmental Defense Fund, Keith’s program at the University of Chicago, and two groups working to support research and engagement on the subject in the poorer, hotter parts of the world: the Degrees Initiative and the Alliance for Just Deliberation on Solar Geoengineering.

Researchers say that the rising dangers of climate change, the lack of progress on cutting emissions, and the relatively small amount of government research funding to date are fueling the growing support for the field.

“A lot of people are recognizing the obvious,” says Douglas MacMartin, a senior research associate in mechanical and aerospace engineering at Cornell, who focuses on geoengineering. “We’re not in a good position with regard to mitigation—and we haven’t spent enough money on research to be able to support good, wise decisions on solar geoengineering.”

Scientists are exploring a variety of potential methods of reflecting away more sunlight, including injecting certain particles into the stratosphere to mimic the cooling effect of volcanic eruptions, spraying salt toward marine clouds to make them brighter, or sprinkling fine dust-like material into the sky to break up heat-trapping cirrus clouds.

Critics contend that neither nonprofits nor scientists should support studying any of these methods, arguing that raising the possibility of such interventions eases pressure to cut emissions and creates a “slippery slope” toward deploying the technology. Even some who support more research fear that funding it through private sources, particularly from wealthy individuals who made their fortunes in tech and finance, may allow studies to move forward without appropriate oversight and taint public perceptions of the field.

The sense that we’re “putting the climate system in the care of people who have disrupted the media and information ecosystems, or disrupted finance, in the past” could undermine public trust in a scientific realm that many already find unsettling, says Holly Buck, an assistant professor at the University of Buffalo and author of After Geoengineering.

‘Unlocking solutions’

One of Quadrature’s first solar geoengineering grants went to the University of Washington’s Marine Cloud Brightening Program. In early April, that research group made headlines for beginning, and then being forced to halt, small-scale outdoor experiments on a decommissioned aircraft carrier sitting off the coast of Alameda, California. The effort entailed spraying a mist of small sea salt particles into the air. 

Quadrature was also one of the donors to a $20.5 million fund for the Washington, DC, nonprofit SilverLining, which was announced in early May. The group pools and distributes grants to solar geoengineering researchers around the world and has pushed for greater government support and funding for the field. The new fund will support that policy advocacy work as well as efforts to “promote equitable participation by all countries,” Kelly Wanser, executive director of SilverLining, said in an email.

She added that it’s crucial to accelerate solar geoengineering research because of the rising dangers of climate change, including the risk of passing “catastrophic tipping points.”

“Current climate projections may even underestimate risks, particularly to vulnerable populations, highlighting the urgent need to improve risk prediction and expand response strategies,” she wrote.

Quadrature has also issued grants for related work to Colorado State University, the University of Exeter, and the Geoengineering Model Intercomparison Project, an effort to run the same set of modeling experiments across an array of climate models. 

The foundation intends to direct its solar geoengineering funding to advance efforts in two main areas: academic research that could improve understanding of various approaches, and work to develop global oversight structures “to enable decision-making on [solar radiation modification] that is transparent, equitable, and science based.”

“We want to empower people to actually make informed decisions at some point,” De Temmerman says, stressing the particular importance of ensuring that people in the Global South are actively involved in such determinations. 

He says that Quadrature is not advocating for specific outcomes, taking no position on whether or not to ultimately use such tools. It also won’t support for-profit startups. 

In an emailed response to questions, he stressed that the funding for solar geoengineering is a tiny part of the foundation’s overall mission, representing just 5% of its $930 million portfolio. The lion’s share has gone to accelerate efforts to cut greenhouse-gas pollution, remove it from the atmosphere, and help vulnerable communities “respond and adapt to climate change to minimize harm.”

Billionaires Greg Skinner and Suneil Setiya founded both the Quadrature investment fund as well as the foundation. The nonprofit’s stated mission is unlocking solutions to the climate crisis, which it describes as “the most urgent challenge of our time.” But the group, which has 26 employees, has faced recent criticism for its benefactors’ stakes in oil and gas companies. Last summer, the Guardian reported that Quadrature Capital held tens of millions of dollars in investments in dozens of fossil-fuel companies, including ConocoPhillips and Cheniere Energy.

In response to a question about the potential for privately funded foundations to steer research findings in self-interested ways, or to create the perception that the results might be so influenced, De Temmerman stated: “We are completely transparent in our funding, ensuring it is used solely for public benefit and not for private gain.”

More foundations, more funds 

To be sure, a number of wealthy individuals and foundations have been providing funds for years to solar geoengineering research or policy work, or groups that collect funds to do so.

A 2021 paper highlighted contributions from a number of wealthy individuals, with a high concentration from the tech sector, including Microsoft cofounder Bill Gates, Facebook cofounder Dustin Moskovitz, Facebook alum and venture capitalist Matt Cohler, former Google executive (and extreme skydiver) Alan Eustace, and tech and climate solutions investors Chris and Crystal Sacca. It noted a number of nonprofits providing grants to the field as well, including the Hewlett Foundation, the Alfred P. Sloan Foundation, and the Blue Marble Fund.

But despite the backing of those high-net-worth individuals, the dollar figures have been low. From 2008 through 2018, total private funding only reached about $20 million, while government funding just topped $30 million. 

The spending pace is now picking up, though, as new players move in.

The Simons Foundation previously announced it would provide $50 million to solar geoengineering research over a five-year period. The New York–based nonprofit invited researchers to apply for grants of up to $500,000, adding that it “strongly” encouraged scientists in the Global South to do so. 

The organization is mostly supporting modeling and lab studies. It said it would not fund social science work or field experiments that would release particles into the environment. Proposals for such experiments have sparked heavy public criticism in the past.

Simons recently announced a handful of initial awards to researchers at Harvard, Princeton, ETH Zurich, the Indian Institute of Tropical Meteorology, the US National Center for Atmospheric Research, and elsewhere.

“For global warming, we will need as many tools in the toolbox as possible,” says David Spergel, president of the Simons Foundation. 

“This was an area where there was a lot of basic science to do, and a lot of things we didn’t understand,” he adds. “So we wanted to fund the basic science.”

In January, the Environmental Defense Fund hosted a meeting at its San Francisco headquarters to discuss the guardrails that should guide research on solar geoengineering, as first reported by Politico. EDF had already provided some support to the Solar Radiation Management Governance Initiative, a partnership with the Royal Society and other groups set up to “ensure that any geoengineering research that goes ahead—inside or outside the laboratory—is conducted in a manner that is responsible, transparent, and environmentally sound.” (It later evolved into the Degrees Initiative.)

But EDF has now moved beyond that work and is “in the planning stages of starting a research and policy initiative on [solar radiation modification],” said Lisa Dilling, associate chief scientist at the environmental nonprofit, in an email. That program will include regranting, which means raising funds from other groups or individuals and distributing them to selected recipients, and advocating for more public funding, she says. 

Outlier also provided a grant to a new nonprofit, Reflective. This organization is developing a road map to prioritize research needs and pooling philanthropic funding to accelerate work in the most urgent areas, says its founder, Dakota Gruener. 

Gruener was previously the executive director of ID2020, a nonprofit alliance that develops digital identification systems. Cornell’s MacMartin is a scientific advisor to the new nonprofit and will serve as the chair of the scientific advisory board.

Government funding is also slowly increasing. 

The US government started a solar geoengineering research program in 2019, funded through the National Oceanic and Atmospheric Administration, that currently provides about $11 million a year.

In February, the UK’s Natural Environment Research Council announced a £10.5 million, five-year research program. In addition, the UK’s Advanced Research and Invention Agency has said it’s exploring and soliciting input for a research program in climate and weather engineering.

Funding has not been allocated as yet, but the agency’s programs typically provide around £50 million.

‘When, not if’

More funding is generally welcome news for researchers who hope to learn more about the potential of solar geoengineering. Many argue that it’s crucial to study the subject because the technology may offer ways to reduce death and suffering, and prevent the loss of species and the collapse of ecosystems. Some also stress it’s crucial to learn what impact these interventions might have and how these tools could be appropriately regulated, because nations may be tempted to implement them unilaterally in the face of extreme climate crises.

It’s likely a question of “when, not if,” and we should “act and research accordingly,” says Gernot Wagner, a climate economist at Columbia Business School, who was previously the executive director of Harvard’s Solar Geoengineering Research Program. “In many ways the time has come to take solar geoengineering much more seriously.”

In 2021, a National Academies report recommended that the US government create a solar geoengineering research program, equipped with $100 million to $200 million in funding over five years.

But there are differences between coordinated government-funded research programs, which have established oversight bodies to consider the merit, ethics, and appropriate transparency of proposed research, and a number of nonprofits with different missions providing funding to the teams they choose. 

To the degree that they create oversight processes that don’t meet the same standards, it could affect the type of science that’s done, the level of public notice provided, and the pressures that researchers feel to deliver certain results, says Duncan McLaren, a climate intervention fellow at the University of California, Los Angeles.

“You’re not going to be too keen on producing something that seems contrary to what you thought the grant maker was looking for,” he says, adding later: “Poorly governed research could easily give overly optimistic answers about what [solar geoengineering] could do, and what its side effects may or may not be.”

Whatever the motivations of individual donors, Buck fears that the concentration of money coming from high tech and finance could also create optics issues, undermining faith in research and researchers and possibly slowing progress in the field.

“A lot of this is going to backfire because it’s going to appear to people as Silicon Valley tech charging in and breaking things,” she says. 

Cloud controversy

Some of the concerns about privately funded work in this area are already being tested.

By most accounts, the Alameda experiment in marine cloud brightening that Quadrature backed was an innocuous basic-science project, which would not have actually altered clouds. But the team stirred up controversy by moving ahead without wide public notice.

City officials quickly halted the experiments, and earlier this month the city council voted unanimously to shut the project down.

Alameda mayor Marilyn Ezzy Ashcraft has complained that city staffers received only vague notice about the project up front. They were then inundated with calls from residents who had heard about it in the media and were concerned about the health implications, she said, according to CBS News.

In response to a question about the criticism, SilverLining’s Wanser said in an email: “We worked with the lease-holder, the USS Hornet, on the process for notifying the city of Alameda. The city staff then engaged experts to independently evaluate the health and environmental safety of the … studies, who found that they did not pose any environmental or health risks to the community.”

Wanser, who is a principal of the Marine Cloud Brightening Program, stressed they’ve also received offers of support from local residents and businesses.

“We think that the availability of data and information on the nature of the studies, and its evaluation by local officials, was valuable in helping people consider it in an informed way for themselves,” she added.

Some observers were also concerned that the research team said it selected its own six-member board to review the proposed project. That differs from a common practice with publicly funded scientific experiments, which often include a double-blind review process, in which neither the researchers nor the reviewers know each other’s names. The concern with breaking from that approach is that scientists could select outside researchers who they believe are likely to greenlight their proposals, and the reviewers may feel pressure to provide more favorable feedback than they might offer anonymously.

Wanser stressed that the team picked “distinguished researchers in the specialized field.”

“There are different approaches for different programs, and in this case, the levels of expertise and transparency were important features,” she added. “They have not received any criticism of the design of the studies themselves, which speaks to their robustness and their value.”

‘Transparent and responsible’

Solar geoengineering researchers often say that they too would prefer public funding, all things being equal. But they stress that those sources are still limited and it’s important to move the field forward in the meantime, so long as there are appropriate standards in place.

“As long as there’s clear transparency about funding sources, [and] there’s no direct influence on the research by the donors, I don’t precisely see what the problem is,” MacMartin says. 

Several nonprofits emerging or moving into this space said that they are working to create responsible oversight structures and rules.

Gruener says that Reflective won’t accept anonymous donations or contributions from people whose wealth comes mostly from fossil fuels. She adds that all donors will be disclosed, that they won’t have any say over the scientific direction of the organization or its chosen research teams, and that they can’t sit on the organization’s board. 

“We think transparency is the only way to build trust, and we’re trying to ensure that our governance structure, our processes, and the outcomes of our research are all public, understandable, and readily available,” she says.

In a statement, Outlier said it’s also in favor of more publicly supported work: “It’s essential for governments to become the leading funders and coordinators of research in these areas.” It added that it’s supporting groups working to accelerate “government leadership” on the subject, including through its grant to EDF. 

Quadrature’s De Temmerman stresses the importance of public research programs as well, noting that the nonprofit hopes to catalyze much more such funding through its support for government advocacy work. 

“We are here to push at the beginning and then at some point just let some other forms of capital actually come,” he says.

MIT Technology Review’s How To series helps you get things done. 

If you post or interact with chatbots on Facebook, Instagram, Threads, or WhatsApp, Meta can use your data to train its generative AI models beginning June 26, according to its recently updated privacy policy. Even if you don’t use any of Meta’s platforms, it can still scrape data such as photos of you if someone else posts them.

Internet data scraping is one of the biggest fights in AI right now. Tech companies argue that anything on the public internet is fair game, but they are facing a barrage of lawsuits over their data practices and copyright. It will likely take years until clear rules are in place. 

In the meantime, they are running out of training data to build even bigger, more powerful models, and to Meta, your posts are a gold mine. 

If you’re uncomfortable with having Meta use your personal information and intellectual property to train its AI models in perpetuity, consider opting out. Although Meta does not guarantee it will allow this, it does say it will “review objection requests in accordance with relevant data protection laws.” 

What that means for US users

Users in the US or other countries without national data privacy laws don’t have any foolproof ways to prevent Meta from using their data to train AI, which has likely already been used for such purposes. Meta does not have an opt-out feature for people living in these places. 

A spokesperson for Meta says it does not use the content of people’s private messages to each other to train AI. However, public social media posts are seen as fair game and can be hoovered up into AI training data sets by anyone. Users who don’t want that can set their account settings to private to minimize the risk. 

The company has built in-platform tools that allow people to delete their personal information from chats with Meta AI, the spokesperson says.

How users in Europe and the UK can opt out 

Users in the European Union and the UK, which are protected by strict data protection regimes, have the right to object to their data being scraped, so they can opt out more easily. 

If you have a Facebook account:

1. Log in to your account. You can access the new privacy policy by following this link. At the very top of the page, you should see a box that says “Learn more about your right to object.” Click on that link, or here. 

Alternatively, you can click on your account icon at the top right-hand corner. Select “Settings and privacy” and then “Privacy center.” On the left-hand side you will see a drop-down menu labeled “How Meta uses information for generative AI models and features.” Click on that, and scroll down. Then click on “Right to object.” 

2. Fill in the form with your information. The form requires you to explain how Meta’s data processing affects you. I was successful in my request by simply stating that I wished to exercise my right under data protection law to object to my personal data being processed. You will likely have to confirm your email address. 

3. You should soon receive both an email and a notification on your Facebook account confirming if your request has been successful. I received mine a minute after submitting the request.

If you have an Instagram account: 

1. Log in to your account. Go to your profile page, and click on the three lines at the top-right corner. Click on “Settings and privacy.”

2. Scroll down to the “More info and support” section, and click “About.” Then click on “Privacy policy.” At the very top of the page, you should see a box that says “Learn more about your right to object.” Click on that link, or here. 

3. Repeat steps 2 and 3 as above. 

Location tracking service leaks PII, because—incompetence? Seems almost TOO easy.

The post Tile/Life360 Breach: ‘Millions’ of Users’ Data at Risk appeared first on Security Boulevard.

This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.

There’s often one overlooked member in a duo. Peanut butter outshines jelly in a PB&J every time (at least in my eyes). For carbon capture and storage technology, the storage part tends to be the underappreciated portion. 

Carbon capture and storage (CCS) tech has two main steps (as you might guess from the name). First, carbon dioxide is filtered out of emissions at facilities like fossil-fuel power plants. Then it gets locked away, or stored.  

Wrangling pollution might seem like the important bit, and there’s often a lot of focus on what fraction of emissions a CCS system can filter out. But without storage, the whole project would be pretty useless. It’s really the combination of capture and long-term storage that helps to reduce climate impact. 

Storage is getting more attention lately, though, and there’s something of a carbon storage boom coming, as my colleague James Temple covered in his latest story. He wrote about what a rush of federal subsidies will mean for the CCS business in the US, and how supporting new projects could help us hit climate goals or push them further out of reach, depending on how we do it. 

The story got me thinking about the oft-forgotten second bit of CCS. Here’s where we might store captured carbon pollution, and why it matters. 

When it comes to storage, the main requirement is making sure the carbon dioxide can’t accidentally leak out and start warming up the atmosphere.

One surprising place that might fit the bill is oil fields. Instead of building wells to extract fossil fuels, companies are looking to build a new type of well where carbon dioxide that’s been pressurized until it reaches a supercritical state—in which liquid and gas phases don’t really exist—is pumped deep underground. With the right conditions (including porous rock deep down and a leak-preventing solid rock layer on top), the carbon dioxide will mostly stay put. 

Shooting carbon dioxide into the earth isn’t actually a new idea, though in the past it’s largely been used by the oil and gas industry for a very different purpose: pulling more oil out of the ground. In a process called enhanced oil recovery, carbon dioxide is injected into wells, where it frees up oil that’s otherwise tricky to extract. In the process, most of the injected carbon dioxide stays underground. 

But there’s a growing interest in sending the gas down there as an end in itself, sparked in part in the US by new tax credits in the Inflation Reduction Act. Companies can rake in $85 per ton of carbon dioxide that’s captured and permanently stored in geological formations, depending on the source of the gas and how it’s locked away. 

In his story, James took a look at one proposed project in California, where one of the state’s largest oil and gas producers has secured draft permits from federal regulators. The project would inject carbon dioxide about 6,000 feet below the surface of the earth, and the company’s filings say the project could store tens of millions of tons of carbon dioxide over the next couple of decades. 

It’s not just land-based projects that are sparking interest, though. State officials in Texas recently awarded a handful of leases for companies to potentially store carbon dioxide deep underwater in the Gulf of Mexico.

And some companies want to store carbon dioxide in products and materials that we use, like concrete. Concrete is made by mixing reactive cement with water and material like sand; if carbon dioxide is injected into a fresh concrete mix, some of it will get involved in the reactions, trapping it in place. I covered how two companies tested out this idea in a newsletter last year.

Products we use every day, from diamonds to sunglasses, can be made with captured carbon dioxide. If we assume that those products stick around for a long time and don’t decompose (how valid this assumption is depends a lot on the product), one might consider these a form of long-term storage, though these markets probably aren’t big enough to make a difference in the grand scheme of climate change. 

Ultimately, though of course we need to emit less, we’ll still need to lock carbon away if we’re going to meet our climate goals.  

Now read the rest of The Spark

Related reading

For all the details on what to expect in the coming carbon storage boom, including more on the potential benefits and hazards of CCS, read James’s full story here.

This facility in Iceland uses mineral storage deep underground to lock away carbon dioxide that’s been vacuumed out of the atmosphere. See all the photos in this story from 2022. 

GOGORO

Another thing

When an earthquake struck Taiwan in April, the electrical grid faced some hiccups—and an unlikely hero quickly emerged in the form of battery-swap stations for electric scooters. In response to the problem, a group of stations stopped pulling power from the grid until it could recover. 

For more on how Gogoro is using battery stations as a virtual power plant to support the grid, check out my colleague Zeyi Yang’s latest story. And if you need a catch-up, check out this explainer on what a virtual power plant is and how it works. 

Keeping up with climate  

New York was set to implement congestion pricing, charging cars that drove into the busiest part of Manhattan. Then the governor put that plan on hold indefinitely. It’s a move that reveals just how tightly Americans are clinging to cars, even as the future of climate action may depend on our loosening that grip. (The Atlantic)

Speaking of cars, preparations in Paris for the Olympics reveal what a future with fewer of them could look like. The city has closed over 100 streets to vehicles, jacked up parking rates for SUVs, and removed tens of thousands of parking spots. (NBC News)

An electric lawnmower could be the gateway to a whole new world. People who have electric lawn equipment or solar panels are more likely to electrify other parts of their homes, like heating and cooking. (Canary Media)

Companies are starting to look outside the battery. From massive moving blocks to compressed air in caverns, energy storage systems are getting weirder as the push to reduce prices intensifies. (Heatmap)

Rivian announced updated versions of its R1T and R1S vehicles. The changes reveal the company’s potential path toward surviving in a difficult climate for EV makers. (Tech Crunch)

First responders in the scorching southwestern US are resorting to giant ice cocoons to help people suffering from extreme heat. (New York Times)

→ Here’s how much heat your body can take. (MIT Technology Review)

One oil producer is getting closer to making what it calls “net-zero oil” by pumping captured carbon dioxide down into wells to get more oil out. The implications for the climate and the future of fossil fuels in our economy are … complicated. (Cipher)

It’s a thought that occurs to every video-game player at some point: What if the weird, hyper-focused state I enter when playing in virtual worlds could somehow be applied to the real one? 

Often pondered during especially challenging or tedious tasks in meatspace (writing essays, say, or doing your taxes), it’s an eminently reasonable question to ask. Life, after all, is hard. And while video games are too, there’s something almost magical about the way they can promote sustained bouts of superhuman concentration and resolve.

For some, this phenomenon leads to an interest in flow states and immersion. For others, it’s simply a reason to play more games. For a handful of consultants, startup gurus, and game designers in the late 2000s, it became the key to unlocking our true human potential.

In her 2010 TED Talk, “Gaming Can Make a Better World,” the game designer Jane McGonigal called this engaged state “blissful productivity.” “There’s a reason why the average World of Warcraft gamer plays for 22 hours a week,” she said. “It’s because we know when we’re playing a game that we’re actually happier working hard than we are relaxing or hanging out. We know that we are optimized as human beings to do hard and meaningful work. And gamers are willing to work hard all the time.”

McGonigal’s basic pitch was this: By making the real world more like a video game, we could harness the blissful productivity of millions of people and direct it at some of humanity’s thorniest problems—things like poverty, obesity, and climate change. The exact details of how to accomplish this were a bit vague (play more games?), but her objective was clear: “My goal for the next decade is to try to make it as easy to save the world in real life as it is to save the world in online games.”

While the word “gamification” never came up during her talk, by that time anyone following the big-ideas circuit (TED, South by Southwest, DICE, etc.) or using the new Foursquare app would have been familiar with the basic idea. Broadly defined as the application of game design elements and principles to non-game activities—think points, levels, missions, badges, leaderboards, reinforcement loops, and so on—gamification was already being hawked as a revolutionary new tool for transforming education, work, health and fitness, and countless other parts of life. 

Instead of liberating us, gamification turned out to be just another tool for coercion, distraction, and control.

Adding “world-saving” to the list of potential benefits was perhaps inevitable, given the prevalence of that theme in video-game storylines. But it also spoke to gamification’s foundational premise: the idea that reality is somehow broken. According to McGonigal and other gamification boosters, the real world is insufficiently engaging and motivating, and too often it fails to make us happy. Gamification promises to remedy this design flawby engineering a new reality, one that transforms the dull, difficult, and depressing parts of life into something fun and inspiring. Studying for exams, doing household chores, flossing, exercising, learning a new language—there was no limit to the tasks that could be turned into games, making everything IRL better.

Today, we live in an undeniably gamified world. We stand up and move around to close colorful rings and earn achievement badges on our smartwatches; we meditate and sleep to recharge our body batteries; we plant virtual trees to be more productive; we chase “likes” and “karma” on social media sites and try to swipe our way toward social connection. And yet for all the crude gamelike elements that have been grafted onto our lives, the more hopeful and collaborative world that gamification promised more than a decade ago seems as far away as ever. Instead of liberating us from drudgery and maximizing our potential, gamification turned out to be just another tool for coercion, distraction, and control. 

Con game

This was not an unforeseeable outcome. From the start, a small but vocal group of journalists and game designers warned against the fairy-tale thinking and facile view of video games that they saw in the concept of gamification. Adrian Hon, author of You’ve Been Played, a recent book that chronicles its dangers, was one of them. 

“As someone who was building so-called ‘serious games’ at the time the concept was taking off, I knew that a lot of the claims being made around the possibility of games to transform people’s behaviors and change the world were completely overblown,” he says. 

Hon isn’t some knee-jerk polemicist. A trained neuroscientist who switched to a career in game design and development, he’s the co-creator of Zombies, Run!—one of the most popular gamified fitness apps in the world. While he still believes games can benefit and enrich aspects of our nongaming lives, Hon says a one-size-fits-all approach is bound to fail. For this reason, he’s firmly against both the superficial layering of generic points, leaderboards, and missions atop everyday activities and the more coercive forms of gamification that have invaded the workplace.

SELMAN DESIGN

Ironically, it’s these broad and varied uses that make criticizing the practice so difficult. As Hon notes in his book, gamification has always been a fast-moving target, varying dramatically in scale, scope, and technology over the years. As the concept has evolved, so too have its applications, whether you think of the gambling mechanics that now encourage users of dating apps to keep swiping, the “quests” that compel exhausted Uber drivers to complete just a few more trips, or the utopian ambition of using gamification to save the world.

In the same way that AI’s lack of a fixed definition today makes it easy to dismiss any one critique for not addressing some other potential definition of it, so too do gamification’s varied interpretations. “I remember giving talks critical of gamification at gamification conferences, and people would come up to me afterwards and be like, ‘Yeah, bad gamification is bad, right? But we’re doing good gamification,’” says Hon. (They weren’t.) 

For some critics, the very idea of “good gamification” was anathema. Their main gripe with the term and practice was, and remains, that it has little to nothing to do with actual games.

“A game is about play and disruption and creativity and ambiguity and surprise,” wrote the late Jeff Watson, a game designer, writer, and educator who taught at the University of Southern California’s School of Cinematic Arts. Gamification is about the opposite—the known, the badgeable, the quantifiable. “It’s about ‘checking in,’ being tracked … [and] becoming more regimented. It’s a surveillance and discipline system—a wolf in sheep’s clothing. Beware its lure.”

Another game designer, Margaret Robertson, has argued that gamification should really be called “pointsification,” writing: “What we’re currently terming gamification is in fact the process of taking the thing that is least essential to games and representing it as the core of the experience. Points and badges have no closer a relationship to games than they do to websites and fitness apps and loyalty cards.”

For the author and game designer Ian Bogost, the entire concept amounted to a marketing gimmick. In a now-famous essay published in the Atlantic in 2011, he likened gamification to the moral philosopher Harry Frankfurt’s definition of bullshit—that is, a strategy intended to persuade or coerce without regard for actual truth. 

“The idea of learning or borrowing lessons from game design and applying them to other areas was never the issue for me,” Bogost told me. “Rather, it was not doing that—acknowledging that there’s something mysterious, powerful, and compelling about games, but rather than doing the hard work, doing no work at all and absconding with the spirit of the form.” 

Gaming the system

So how did a misleading term for a misunderstood process that’s probably just bullshit come to infiltrate virtually every part of our lives? There’s no one simple answer. But gamification’s meteoric rise starts to make a lot more sense when you look at the period that gave birth to the idea. 

The late 2000s and early 2010s were, as many have noted, a kind of high-water mark for techno-­optimism. For people both inside the tech industry and out, there was a sense that humanity had finally wrapped its arms around a difficult set of problems, and that technology was going to help us squeeze out some solutions. The Arab Spring bloomed in 2011 with the help of platforms like Facebook and Twitter, money was more or less free, and “____ can save the world” articles were legion (with ____ being everything from “eating bugs” to “design thinking”).

This was also the era that produced the 10,000-hours rule of success, the long tail, the four-hour workweek, the wisdom of crowds, nudge theory, and a number of other highly simplistic (or, often, flat-out wrong) theories about the way humans, the internet, and the world work. 

“All of a sudden you had VC money and all sorts of important, high-net-worth people showing up at game developer conferences.”

Ian Bogost, author and game designer

Adding video games to this heady stew of optimism gave the game industry something it had long sought but never achieved: legitimacy. Even with games ascendant in popular culture—and on track to eclipse both the film and music industries in terms of revenue—they still were largely seen as a frivolous, productivity-­squandering, violence-encouraging form of entertainment. Seemingly overnight, gamification changed all that. 

“There was definitely this black-sheep mentality in the game development community—the sense that what we had been doing for decades was just a joke to people,” says Bogost. “All of a sudden you had VC money and all sorts of important, high-net-worth people showing up at game developer conferences, and it was like, ‘Finally someone’s noticing. They realize that we have something to offer.’”

This wasn’t just flattering; it was intoxicating. Gamification took a derided pursuit and recast it as a force for positive change, a way to make the real world better. While  enthusiastic calls to “build a game layer on top of reality” may sound dystopian to many of us today, the sentiment didn’t necessarily have the same ominous undertones at the end of the aughts. 

Combine the cultural recasting of games with an array of cheaper and faster technologies—GPS, ubiquitous and reliable mobile internet, powerful smartphones, Web 2.0 tools and services—and you arguably had all the ingredients needed for gamification’s rise. In a very real sense, reality in 2010 was ready to be gamified. Or to put it a slightly different way: Gamification was an idea perfectly suited for its moment. 

Gaming behavior

Fine, you might be asking at this point, but does it work? Surely, companies like Apple, Uber, Strava, Microsoft, Garmin, and others wouldn’t bother gamifying their products and services if there were no evidence of the strategy’s efficacy. The answer to the question, unfortunately, is super annoying: Define work.

Because gamification is so pervasive and varied, it’s hard to address its effectiveness in any direct or comprehensive way. But one can confidently say this: Gamification did not save the world. Climate change still exists. As do obesity, poverty, and war. Much of generic gamification’s power supposedly resides in its ability to nudge or steer us toward, or away from, certain behaviors using competition (challenges and leaderboards), rewards (points and achievement badges), and other sources of positive and negative feedback. 

Gamification is, and has always been, a way to induce specific behaviors in people using virtual carrots and sticks.

On that front, the results are mixed. Nudge theory lost much of its shine with academics in 2022 after a meta-analysis of previous studies concluded that, after correcting for publication bias, there wasn’t much evidence it worked to change behavior at all. Still, there are a lot of ways to nudge and a lot of behaviors to modify. The fact remains that plenty of people claim to be highly motivated to close their rings, earn their sleep crowns, or hit or exceed some increasingly ridiculous number of steps on their Fitbits (see humorist David Sedaris). 

Sebastian Deterding, a leading researcher in the field, argues that gamification can work, but its successes tend to be really hard to replicate. Not only do academics not know what works, when, and how, according to Deterding, but “we mostly have just-so stories without data or empirical testing.” 

SELMAN DESIGN

In truth, gamification acolytes were always pulling from an old playbook—one that dates back to the early 20th century. Then, behaviorists like John Watson and B.F. Skinner saw human behaviors (a category that for Skinner included thoughts, actions, feelings, and emotions) not as the products of internal mental states or cognitive processes but, rather, as the result of external forces—forces that could conveniently be manipulated. 

If Skinner’s theory of operant conditioning, which doled out rewards to positively reinforce certain behaviors, sounds a lot like Amazon’s “Fulfillment Center Games,” which dole out rewards to compel workers to work harder, faster, and longer—well, that’s not a coincidence. Gamification is, and has always been, a way to induce specific behaviors in people using virtual carrots and sticks. 

Sometimes this may work; other times not. But ultimately, as Hon points out, the question of efficacy may be beside the point. “There is no before or after to compare against if your life is always being gamified,” he writes. “There isn’t even a static form of gamification that can be measured, since the design of coercive gamification is always changing, a moving target that only goes toward greater and more granular intrusion.” 

The game of life

Like any other art form, video games offer a staggering array of possibilities. They can educate, entertain, foster social connection, inspire, and encourage us to see the world in different ways. Some of the best ones manage to do all of this at once.

Yet for many of us, there’s the sense today that we’re stuck playing an exhausting game that we didn’t opt into. This one assumes that our behaviors can be changed with shiny digital baubles, constant artificial competition, and meaningless prizes. Even more insulting, the game acts as if it exists for our benefit—promising to make us fitter, happier, and more productive—when in truth it’s really serving the commercial and business interests of its makers. 

Metaphors can be an imperfect but necessary way to make sense of the world. Today, it’s not uncommon to hear talk of leveling up, having a God Mode mindset, gaining XP, and turning life’s difficulty settings up (or down). But the metaphor that resonates most for me—the one that seems to neatly capture our current predicament—is that of the NPC, or non-player character.  

NPCs are the “Sisyphean machines” of video games, programmed to follow a defined script forever and never question or deviate. They’re background players in someone else’s story, typically tasked with furthering a specific plotline or performing some manual labor. To call someone an NPC in real life is to accuse them of just going through the motions, not thinking for themselves, not being able to make their own decisions. This, for me, is gamification’s real end result. It’s acquiescence pretending to be empowerment. It strips away the very thing that makes games unique—a sense of agency—and then tries to mask that with crude stand-ins for accomplishment.

So what can we do? Given the reach and pervasiveness of gamification, critiquing it at this point can feel a little pointless, like railing against capitalism. And yet its own failed promises may point the way to a possible respite. If gamifying the world has turned our lives into a bad version of a video game, perhaps this is the perfect moment to reacquaint ourselves with why actual video games are great in the first place. Maybe, to borrow an idea from McGonigal, we should all start playing better games. 

Bryan Gardiner is a writer based in Oakland, California. 

This story first appeared in China Report, MIT Technology Review’s newsletter about technology in China. Sign up to receive it in your inbox every Tuesday.

If you’ve ever been to Taiwan, you’ve likely run into Gogoro’s green-and-white battery-swap stations in one city or another. With 12,500 stations around the island, Gogoro has built a sweeping network that allows users of electric scooters to drop off an empty battery and get a fully charged one immediately. Gogoro is also found in China, India, and a few other countries.
 
This morning, I published a story on how Gogoro’s battery-swap network in Taiwan reacted to emergency blackouts after the 7.4 magnitude earthquake there this April. I talked to Horace Luke, Gogoro’s cofounder and CEO, to understand how in three seconds, over 500 Gogoro battery-swap locations stopped drawing electricity from the grid, helping stabilize the power frequency.
 
Gogoro’s battery stations acted like something called a virtual power plant (VPP), a new idea that’s becoming adopted around the world as a way to stitch renewable energy into the grid. The system draws energy from distributed sources like battery storage or small rooftop solar panels and coordinates those sources to increase supply when electricity demand peaks. As a result, it reduces the reliance on traditional coal or gas power plants.
 
There’s actually a natural synergy between technologies like battery swapping and virtual power plants (VPP). Not only can battery-swap stations coordinate charging times with the needs of the grid, but the idle batteries sitting in Gogoro’s stations can also become an energy reserve in times of emergency, potentially feeding energy back to the grid. If you want to learn more about how this system works, you can read the full story here.

GOGORO

When I talked to Gogoro’s Luke for this story, I asked him: “At what point in the company’s history did you come up with the idea to use these batteries for VPP networks?”
 
To my surprise, Luke answered: “Day one.”
 
As he explains, Gogoro was actually not founded to be an electric-scooter company; it was founded to be a “smart energy” company. 

“We started with the thesis of how smart energy, through portability and connectivity, can enable many use case scenarios,” Luke says. “Transportation happens to be accounting for something like 27% or 28% of your energy use in your daily life.” And that’s why the company first designed the batteries for two-wheeled vehicles, a popular transportation option in Taiwan and across Asia.
 
Having succeeded in promoting its scooters and the battery-swap charging method in Taiwan, it is now able to explore other possible uses of these modular, portable batteries—more than 1.4 million of which are in circulation at this point. 
 
“Think of smart, portable, connected energy like a propane tank,” Luke says. Depending on their size,  propane tanks can be used to cook in the wild or to heat a patio. If lithium batteries can be modular and portable in a similar way, they can also serve many different purposes.

Using them in VPP programs that protect the grid from blackouts is one; beyond that, in Taipei City, Gogoro has worked with the local government to build energy backup stations for traffic lights, using the same batteries to keep the lights running in future blackouts. The batteries can also be used as backup power storage for critical facilities like hospitals. When a blackout happens, battery storage can release electricity much faster than diesel generators, keeping the impact at a minimum.

None of this would be possible without the recent advances that have made batteries more powerful and efficient. And it was clear from our conversation that Luke is obsessed with batteries—the long way the technology has come, and their potential to address a lot more energy use cases in the future.

“I still remember getting my first flashlight when I was a little kid. That button just turned the little lightbulb on and off. And that was what was amazing about batteries at the time,” says Luke. “Never did people think that AA batteries were going to power calculators or the Walkman. The guy that invented the alkaline battery never thought that. We’ll continue to take that creativity and apply it to portable energy, and that’s what inspires us every day.”

What other purposes do you think portable lithium batteries like the ones made by Gogoro could have? Let me know your ideas by writing to zeyi@technologyreview.com.

Now read the rest of China Report

Catch up with China

1. Far-right parties won big in the latest European Parliament elections, which could push the EU further toward a trade war with China. (Nikkei Asia $)
 
2. Volvo has started moving some of its manufacturing capacity from China to Belgium in order to avoid the European Union tariffs on Chinese imports. (The Times $)
 
3. Some major crypto exchanges have withdrawn from applying for business licenses in Hong Kong after the city government clarified that it doesn’t welcome businesses that offer crypto services to mainland China. (South China Morning Post $)
 
4. NewsBreak, the most downloaded news app in the US, does most of its engineering work in China. The app has also been found to use AI tools to make up local news that never happened. (Reuters $)
 
5. The Australian government ordered a China-linked fund to reduce its investment in an Australian rare-earth-mining company. (A/symmetric)
 
6. China just installed the largest offshore wind turbine in the world. It’s designed to generate enough power in a year for around 36,000 households. (Electrek)
 
7. Four college instructors from Iowa were stabbed on a visit to northern China. While the motive and identity of the assailant are still unknown, the incident has been quickly censored on the Chinese internet. (BBC)

Lost in translation

Qian Zhimin, a Chinese businesswoman who fled the country in 2017 after raising billions of dollars from Chinese investors in the name of bitcoin investments, was arrested in London and is facing a trial in October this year, according to the Chinese publication Caijing. In the early 2010s, when the cryptocurrency first became known in China, Qian’s company lured over 128,000 retail investors, predominantly elderly people, to buy fraudulent investment products that bet on the price of bitcoins and gadgets like smart bracelets that allegedly could also mine bitcoins. 
 
After the scam was exposed, Qian escaped to the UK with a fake passport. She controls over 61,000 bitcoins, now worth nearly $4 billion, and has been trying to liquidate them by buying properties in London. But those attempts caught the attention of anti-money-laundering authorities in the UK. With her trial date approaching, the victims in China are hoping to work with the UK jurisdiction to recover their assets.

One more thing

I know one day we will see self-driving vehicles racing each other and cutting each other off, but I didn’t expect it to happen so soon with two package delivery robots in China. Maybe it’s just their look, but it seems cuter than when human drivers do the same thing?

Pump jacks and pipelines clutter the Elk Hills oil field of California, a scrubby stretch of land in the southern Central Valley that rests above one of the nation’s richest deposits of fossil fuels.

Oil production has been steadily declining in the state for decades, as tech jobs have boomed and legislators have enacted rigorous environmental and climate rules. Companies, towns, and residents across Kern County, where the poverty rate hovers around 18%, have grown increasingly desperate for new economic opportunities.

Late last year, California Resources Corporation (CRC), one of the state’s largest oil and gas producers, secured draft permits from the US Environmental Protection Agency to develop a new type of well in the oil field, which it asserts would provide just that. If the company gets final approval from regulators, it intends to drill a series of boreholes down to a sprawling sedimentary formation roughly 6,000 feet below the surface, where it will inject tens of millions of metric tons of carbon dioxide to store it away forever. 

They’re likely to become California’s first set of what are known as Class VI wells, designed specifically for sequestering the planet-warming greenhouse gas. But many, many similar carbon storage projects are on the way across the state, the US, and the world—a trend driven by growing government subsidies, looming national climate targets, and declining revenue and growth in traditional oil and gas activities.

Since the start of 2022, companies like CRC have submitted nearly 200 applications in the US alone to develop wells of this new type. That offers one of the clearest signs yet that capturing the carbon dioxide pollution from industrial and energy operations instead of releasing it into the atmosphere is about to become a much bigger business. 

Proponents hope it’s the start of a sort of oil boom in reverse, kick-starting a process through which the world will eventually bury more greenhouse gas than it adds to the atmosphere. They argue that embracing carbon capture and storage (CCS) is essential to any plan to rapidly slash emissions. This is, in part, because retrofitting the world’s massive existing infrastructure with carbon dioxide–scrubbing equipment could be faster and easier than rebuilding every power plant and factory. CCS can be a particularly helpful way to cut emissions in certain heavy industries, like cement, fertilizer, and paper and pulp production, where we don’t have scalable, affordable ways of producing crucial goods without releasing carbon dioxide. 

“In the right context, CCS saves time, it saves money, and it lowers risks,” says Julio Friedmann, chief scientist at Carbon Direct and previously the principal deputy assistant secretary for the Department of Energy’s Office of Fossil Energy.

But opponents insist these efforts will prolong the life of fossil-fuel plants, allow air and water pollution to continue, and create new health and environmental risks that could disproportionately harm disadvantaged communities surrounding the projects, including those near the Elk Hills oil field.

“It’s the oil majors that are proposing and funding a lot of these projects,” says Catherine Garoupa, executive director of the Central Valley Air Quality Coalition, which has tracked a surge of applications for carbon storage projects throughout the district. “They see it as a way of extending business as usual and allowing them to be carbon neutral on paper while still doing the same old dirty practices.”

A slow start

The US federal government began overseeing injection wells in the 1970s. A growing number of companies had begun injecting waste underground, sparking a torrent of water pollution lawsuits and the passage of several major laws designed to ensure clean drinking water. The EPA developed standards and rules for a variety of wells and waste types, including deep Class I wells for hazardous or even radioactive refuse and shallower Class V wells for non-hazardous fluids.

In 2010, amid federal efforts to create incentives for industries to capture more carbon dioxide, the agency added Class VI wells for CO₂ sequestration. To qualify, a proposed well site must have the appropriate geology, with a deep reservoir of porous rock that can accommodate carbon dioxide molecules sitting below a layer of nonporous “cap rock” like shale. The reservoir also needs to sit well below any groundwater aquifers, so that it won’t contaminate drinking water supplies, and it must be far enough from fault lines to reduce the chances that earthquakes might crack open pathways for the greenhouse gas to escape. 

The carbon sequestration program got off to a slow start. As of late 2021, there were only two Class VI injection wells in operation and 22 applications pending before regulators.

But there’s been a flurry of proposals since—both to the EPA and to the three states that have secured permission to authorize such wells themselves, which include North Dakota, Wyoming, and Louisiana. The Clean Air Task Force, a Boston-based energy policy think tank keeping track of such projects, says there are now more than 200 pending applications.

What changed is the federal incentives. The Inflation Reduction Act of 2022 dramatically boosted the tax credits available for permanently storing carbon dioxide in geological formations, bumping it up from $50 a ton to $85 when it’s captured from industrial and power plants. The credit rose from $50 to $180 a ton when the greenhouse gas is sourced from direct-air-capture facilities, a different technology that sucks greenhouse gas out of the air. Tax credits allow companies to directly reduce their federal tax obligations, which can cover the added expense of CCS across a growing number of sectors.

The separate Bipartisan Infrastructure Law also provided billions of dollars for carbon capture demonstration and pilot projects.

A tax credit windfall 

CRC became an independent company in 2014, when Occidental Petroleum, one of the world’s largest oil and gas producers, spun it off along with many of its California assets. But the new company quickly ran into financial difficulties, filing for bankruptcy protection in 2020 amid plummeting energy demand during the early stages of the covid-19 pandemic. It emerged several months later, after restructuring its debt, converting loans into equity, and raising new lines of credit. 

The following year, CRC created a carbon management subsidiary, Carbon TerraVault, seizing an emerging opportunity to develop a new business around putting carbon dioxide back underground, whether for itself or for customers. The company says it was also motivated by the chance to “help advance the energy transition and curb rising global temperatures at 1.5 °C.”

CRC didn’t respond to inquiries from MIT Technology Review.

In its EPA application the company, based in Long Beach, California, says that hundreds of thousands of tons of carbon dioxide would initially be captured each year from a gas treatment facility in the Elk Hills area as well as a planned plant designed to produce hydrogen from natural gas. The gas is purified and compressed before it’s pumped underground.

The company says the four wells for which it has secured draft permits could store nearly 1.5 million tons of carbon dioxide per year from those and other facilities, with a total capacity of 38 million tons over 26 years. CRC says the projects will create local jobs and help the state meet its pressing climate targets.

“We are committed to supporting the state in reaching carbon neutrality and developing a more sustainable future for all Californians,” Francisco Leon, chief executive of CRC, said of the draft EPA decision in a statement. 

Those wells, however, are just the start of the company’s carbon management plans: Carbon TerraVault has applied to develop 27 additional wells for carbon storage across the state, including two more at Elk Hills, according to the EPA’s permit tracker. If those are all approved and developed, it would transform the subsidiary into a major player in the emerging business of carbon storage—and set it up for a windfall in federal tax credits. 

Carbon sequestration projects can qualify for 12 years of US subsidies. If Carbon TerraVault injects half a million tons of carbon dioxide into each of the 31 wells it has applied for over that time period, the projects could secure tax credits worth more than $15.8 billion.

That figure doesn’t take inflation into account and assumes the company meets the most stringent requirements of the law and sources all the carbon dioxide from industrial facilities and power plants. The number could rise significantly if the company injects more than that amount into wells, or if a significant share of the carbon dioxide is sourced through direct air capture. 

Chevron, BP, ExxonMobil, and Archer Daniels Midland, a major producer of ethanol, have also submitted Class VI well applications to the EPA and could be poised to secure significant IRA subsidies as well.

To be sure, it takes years to secure regulatory permits, and not every proposed project will move forward in the end. The companies involved will still need to raise financing, add carbon capture equipment to polluting facilities, and in many cases build out carbon dioxide pipelines that require separate approvals. But the increased IRA tax credits could drive as much as 250 million metric tons of additional annual storage or use of carbon dioxide in the US by 2035, according to the latest figures from the Princeton-led REPEAT Project.

“It’s a gold rush,” Garoupa says. “It’s being shoved down our throats as ‘Oh, it’s for climate goals.’” But if we’re “not doing it judiciously and really trying to achieve real emissions reductions first,” she adds, it’s merely a distraction from the other types of climate action needed to prevent dangerous levels of warming. 

Carbon accounting

Even if CCS can help drive down emissions in the aggregate, the net climate benefits from any given project will depend on a variety of factors, including how well it’s developed and run—and what other changes it brings about throughout complex, interconnected energy systems over time.

Notably, adding carbon capture equipment to a plant doesn’t trap all the climate pollution. Project developers are generally aiming for around 90%. So if you build a new project with CCS, you’ve increased emissions, not cut them, relative to the status quo.

In addition, the carbon capture process requires a lot of power to run, which may significantly increase emissions of greenhouse gas and other pollutants elsewhere by, for example, drawing on additional generation from natural-gas plants on the grid. Plus, the added tax incentives may make it profitable for a company to continue operating a fossil-fuel plant that it would otherwise have shut down or to run the facilities more hours of the day to generate more carbon dioxide to bury. 

All the uncaptured emissions associated with those changes can reduce, if not wipe out, any carbon benefits from incorporating CCS, says Danny Cullenward, a senior fellow with the Kleinman Center for Energy Policy at the University of Pennsylvania.

But none of that matters as far as the carbon storage subsidies are concerned. Businesses could even use the savings to expand their traditional oil and gas operations, he says.

“It’s not about the net climate impact—it’s about the gross tons you stick under ground,” Cullenward says of the tax credits.

A study last year raised a warning about how that could play out in the years to come, noting that the IRA may require the US to provide hundreds of billions to trillions of dollars in tax credits for power plants that add CCS. Under the scenarios explored, those projects could collectively deliver emissions reductions of as much as 24% or increases as high as 82%. The difference depends largely on how much the incentives alter energy production and the degree to which they extend the life of coal and natural-gas plants.

Coauthor Emily Grubert, an associate professor at Notre Dame and a former deputy assistant secretary at the Department of Energy, stressed that regulators must carefully consider these complex, cascading emissions impacts when weighing whether to approve such proposals.

“Not taking this seriously risks potentially trillions of dollars and billions of tonnes of [greenhouse-gas] emissions, not to mention the trust and goodwill of the American public, which is reasonably skeptical of these potentially critically important technologies,” she wrote in an op-ed in the industry outlet Utility Dive.

Global goals

Other nations and regions are also accelerating efforts to capture and store carbon as part of their broader efforts to lower emissions and combat climate change. The EU, which has dedicated tens of billions of euros to accelerating the development of CCS, is working to develop the capacity to store 50 million tons of carbon dioxide per year by 2030, according to the Global CCS Institute’s 2023 industry report.

Likewise, Japan hopes to sequester 240 million tons annually by 2050, while Saudi Arabia is aiming for 44 million tons by 2035. The industry trade group said there were 41 CCS projects in operation around the world at the time, with another 351 under development.

A handful of US facilities have been capturing carbon dioxide for decades for a variety of uses, including processing or producing natural gas, ammonia, and soda ash, which is used in soaps, cosmetics, baking soda, and other goods.

But Ben Grove, carbon storage manager at the Clean Air Task Force, says the increased subsidies in the IRA made CCS economical for many industry segments in the US, including: chemicals, petrochemicals, hydrogen, cement, oil, gas and ethanol refineries, and steel, at least on the low end of the estimated cost ranges. 

In many cases, the available subsidies still won’t fully cover the added cost of CCS in power plants and certain other industrial facilities. But the broader hope is that these federal programs will help companies scale up and optimize these processes over time, driving down the cost of CCS and making it feasible for more sectors, Grove says.

‘Against all evidence’

In addition to the gas treatment and hydrogen plants, CRC says, another source for the captured carbon dioxide could eventually include its own Elk Hills Power Plant, which runs on natural gas extracted from the oil field. The company has said it intends to retrofit the facility to capture 1.5 million tons of emissions a year.

Still other sources could include renewable fuels plants, which may mean biofuel facilities, steam generators, and a proposed direct-air-capture plant that would be developed by the carbon-removal startup Avnos, according to the EPA filing. Carbon TerraVault is part of a consortium, which includes Avnos, Climeworks, Southern California Gas Company, and others, that has proposed developing a direct-air-capture hub in Kern County, where the Elk Hills field is located. Last year, the Department of Energy awarded the so-called California DAC Hub nearly $12 million to conduct engineering design studies for direct-air-capture facilities.

CCS may be a helpful tool for heavy industries that are really hard to clean up, but that’s largely not what CRC has proposed, says Natalia Ospina, legal director at the Center on Race, Poverty & the Environment, an environmental-justice advocacy organization in Delano, California. 

“The initial source will be the Elk Hills oil field itself and the plant that refines gas in the first place,” she says. “That is just going to allow them to extend the life of the oil and gas industry in Kern County, which goes against all the evidence in front of us in terms of how we should be addressing the climate crisis.”

NATALIA OSPINA

Critics of the project also fear that some of these facilities will continue producing other types of pollution, like volatile organic compounds and fine particulate matter, in a region that’s already heavily polluted. Some analyses show that adding a carbon capture process reduces those other pollutants in certain cases. But Ospina argues that oil and gas companies can’t be trusted to operate such projects in ways that reduce pollution to the levels necessary to protect neighboring communities.

‘You need it’

Still, a variety of studies, from the state level to the global, conclude that CCS may play an essential role in cutting greenhouse-gas emissions fast enough to moderate the global dangers of climate change.

California is banking heavily on capturing carbon from plants or removing it from the air through various means to meet its 2045 climate neutrality goal, aiming for 20 million metric tons by 2030 and 100 million by midcentury. The Air Resources Board, the state’s main climate regulator, declared that “there is no path to carbon neutrality without carbon removal and sequestration.” 

Recent reports from the UN’s climate panel have also stressed that carbon capture could be a “critical mitigation option” for cutting emissions from cement and chemical production. The body’s modeling study scenarios that limit global warming to 1.5 °C over preindustrial levels rely on significant levels of CCS, including tens to hundreds of billions of tons of carbon dioxide captured this century from plants that use biomatter to produce heat and electricity—a process known as BECCS.

Meeting global climate targets without carbon capture would require shutting down about a quarter of the world’s fossil-fuel plants before they’ve reached the typical 50-year life span, the International Energy Agency notes. That’s an expensive proposition, and one that owners, investors, industry trade groups, and even nations will fiercely resist.

“Everyone keeps coming to the same conclusion, which is that you need it,” Friedmann says.

Lorelei Oviatt, director of the Kern County Planning and Natural Resources Department, declined to express an opinion about CRC’s Elk Hills project while local regulators are reviewing it. But she strongly supports the development of CCS projects in general, describing it as a way to help her region restore lost tax revenue and jobs as “the state puts the area’s oil companies out of business” through tighter regulations.

County officials have proposed the development of a more than 4,000-acre carbon management park, which could include hydrogen, steel, and biomass facilities with carbon-capture components. An economic analysis last year found that the campus and related activities could create more than 22,000 jobs, and generate more than $88 million in sales and property taxes for the economically challenged county and cities, under a high-end scenario. 

Oviatt adds that embracing carbon capture may also allow the region to avoid the “stranded asset” problem, in which major employers are forced to shut down expensive power plants, refineries, and extraction wells that could otherwise continue operating for years to decades.

“We’re the largest producer of oil in California and seventh in the country; we have trillions and trillions of dollars in infrastructure,” she says. “The idea that all of that should just be abandoned does not seem like a thoughtful way to design an economy.”

Carbon dioxide leaks

But critics fear that preserving it simply means creating new dangers for the disproportionately poor, unhealthy, and marginalized communities surrounding these projects.

In a 2022 letter to the EPA, the Center for Biological Diversity raised the possibility that the sequestered carbon dioxide could leak out of wells or pipelines, contributing to climate change and harming local residents.

These concerns are not without foundation.

In February 2020, Denbury Enterprises’ Delta pipeline, which stretches more than 100 miles between Mississippi and Louisiana, ruptured and released more than 30,000 barrels’ worth of compressed, liquid CO₂ gas near the town of Satartia, Mississippi. 

The leak forced hundreds of people to evacuate their homes and sent dozens to local hospitals, some struggling to breathe and others unconscious and foaming at the mouth, as the Huffington Post detailed in an investigative piece. Some vehicles stopped running as well: the carbon dioxide in air displaced oxygen, which is essential to the combustion in combustion engines.

There have also been repeated carbon dioxide releases over the last two decades at an enhanced oil recovery project at the Salt Creek oil field in Wyoming. Starting in the late 1800s, a variety of operators have drilled, abandoned, sealed, and resealed thousands of wells at the site, with varying degrees of quality, reliability, and documentation, according to the Natural Resources Defense Council. A sustained leak in 2004 emitted 12,000 cubic feet of the gas per day, on average, while a 2016 release of carbon dioxide and methane forced a school near the field to relocate its classes for the remainder of the year.

Some fear that similar issues could arise at Elk Hills, which could become the nation’s first carbon sequestration project developed in a depleted oil field. Companies have drilled and operated thousands of wells over decades at the site, many of which have sat idle and unplugged for years, according to a 2020 investigation by the Los Angeles Times and the Center for Public Integrity.

Ospina argues that CRC and county officials are asking the residents of Kern County to act as test subjects for unproven and possibly dangerous CCS use cases, compounding the health risks facing a region that is already exposed to too many.

Whether the Elk Hills project moves forward or not, the looming carbon storage boom will soon force many other areas to wrestle with similar issues. What remains to be seen is whether companies and regulators can adequately address community fears and demonstrate that the climate benefits promised in modeling studies will be delivered in reality. 

Update: This story was updated to remove a photo that was not of the Elk Hills oil field and had been improperly captioned.

At its Worldwide Developer Conference on Monday, Apple for the first time unveiled its vision for supercharging its product lineup with artificial intelligence. The key feature, which will run across virtually all of its product line, is Apple Intelligence, a suite of AI-based capabilities that promises to deliver personalized AI services while keeping sensitive data secure.

It represents Apple’s largest leap forward in using our private data to help AI do tasks for us. To make the case it can do this without sacrificing privacy, the company says it has built a new way to handle sensitive data in the cloud.

Apple says its privacy-focused system will first attempt to fulfill AI tasks locally on the device itself. If any data is exchanged with cloud services, it will be encrypted and then deleted afterward. The company also says the process, which it calls Private Cloud Compute, will be subject to verification by independent security researchers. 

The pitch offers an implicit contrast with the likes of Alphabet, Amazon, or Meta, which collect and store enormous amounts of personal data. Apple says any personal data passed on to the cloud will be used only for the AI task at hand and will not be retained or accessible to the company, even for debugging or quality control, after the model completes the request. 

Simply put, Apple is saying people can trust it to analyze incredibly sensitive data—photos, messages, and emails that contain intimate details of our lives—and deliver automated services based on what it finds there, without actually storing the data online or making any of it vulnerable. 

It showed a few examples of how this will work in upcoming versions of iOS. Instead of scrolling through your messages for that podcast your friend sent you, for example, you could simply ask Siri to find and play it for you. Craig Federighi, Apple’s senior vice president of software engineering, walked through another scenario: an email comes in pushing back a work meeting, but his daughter is appearing in a play that night. His phone can now find the PDF with information about the performance, predict the local traffic, and let him know if he’ll make it on time. These capabilities will extend beyond apps made by Apple, allowing developers to tap into Apple’s AI too. 

Because the company profits more from hardware and services than from ads, Apple has less incentive than some other companies to collect personal online data, allowing it to position the iPhone as the most private device. Even so, Apple has previously found itself in the crosshairs of privacy advocates. Security flaws led to leaks of explicit photos from iCloud in 2014. In 2019, contractors were found to be listening to intimate Siri recordings for quality control. Disputes about how Apple handles data requests from law enforcement are ongoing. 

The first line of defense against privacy breaches, according to Apple, is to avoid cloud computing for AI tasks whenever possible. “The cornerstone of the personal intelligence system is on-device processing,” Federighi says, meaning that many of the AI models will run on iPhones and Macs rather than in the cloud. “It’s aware of your personal data without collecting your personal data.”

That presents some technical obstacles. Two years into the AI boom, pinging models for even simple tasks still requires enormous amounts of computing power. Accomplishing that with the chips used in phones and laptops is difficult, which is why only the smallest of Google’s AI models can be run on the company’s phones, and everything else is done via the cloud. Apple says its ability to handle AI computations on-device is due to years of research into chip design, leading to the M1 chips it began rolling out in 2020.

Yet even Apple’s most advanced chips can’t handle the full spectrum of tasks the company promises to carry out with AI. If you ask Siri to do something complicated, it may need to pass that request, along with your data, to models that are available only on Apple’s servers. This step, security experts say, introduces a host of vulnerabilities that may expose your information to outside bad actors, or at least to Apple itself.

“I always warn people that as soon as your data goes off your device, it becomes much more vulnerable,” says Albert Fox Cahn, executive director of the Surveillance Technology Oversight Project and practitioner in residence at NYU Law School’s Information Law Institute. 

Apple claims to have mitigated this risk with its new Private Cloud Computer system. “For the first time ever, Private Cloud Compute extends the industry-leading security and privacy of Apple devices into the cloud,” Apple security experts wrote in their announcement, stating that personal data “isn’t accessible to anyone other than the user—not even to Apple.” How does it work?

Historically, Apple has encouraged people to opt in to end-to-end encryption (the same type of technology used in messaging apps like Signal) to secure sensitive iCloud data. But that doesn’t work for AI. Unlike messaging apps, where a company like WhatsApp does not need to see the contents of your messages in order to deliver them to your friends, Apple’s AI models need unencrypted access to the underlying data to generate responses. This is where Apple’s privacy process kicks in. First, Apple says, data will be used only for the task at hand. Second, this process will be verified by independent researchers. 

Needless to say, the architecture of this system is complicated, but you can imagine it as an encryption protocol. If your phone determines it needs the help of a larger AI model, it will package a request containing the prompt it’s using and the specific model, and then put a lock on that request. Only the specific AI model to be used will have the proper key.

When asked by MIT Technology Review whether users will be notified when a certain request is sent to cloud-based AI models instead of being handled on-device, an Apple spokesperson said there will be transparency to users but that further details aren’t available.

Dawn Song, co-Director of UC Berkeley Center on Responsible Decentralized Intelligence and an expert in private computing, says Apple’s new developments are encouraging. “The list of goals that they announced is well thought out,” she says. “Of course there will be some challenges in meeting those goals.”

Cahn says that to judge from what Apple has disclosed so far, the system seems much more privacy-protective than other AI products out there today. That said, the common refrain in his space is “Trust but verify.” In other words, we won’t know how secure these systems keep our data until independent researchers can verify its claims, as Apple promises they will, and the company responds to their findings.

“Opening yourself up to independent review by researchers is a great step,” he says. “But that doesn’t determine how you’re going to respond when researchers tell you things you don’t want to hear.” Apple did not respond to questions from MIT Technology Review about how the company will evaluate feedback from researchers.

The privacy-AI bargain

Apple is not the only company betting that many of us will grant AI models mostly unfettered access to our private data if it means they could automate tedious tasks. OpenAI’s Sam Altman described his dream AI tool to MIT Technology Review as one “that knows absolutely everything about my whole life, every email, every conversation I’ve ever had.” At its own developer conference in May, Google announced Project Astra, an ambitious project to build a “universal AI agent that is helpful in everyday life.”

It’s a bargain that will force many of us to consider for the first time what role, if any, we want AI models to play in how we interact with our data and devices. When ChatGPT first came on the scene, that wasn’t a question we needed to ask. It was simply a text generator that could write us a birthday card or a poem, and the questions it raised—like where its training data came from or what biases it perpetuated—didn’t feel quite as personal. 

Now, less than two years later, Big Tech is making billion-dollar bets that we trust the safety of these systems enough to fork over our private information. It’s not yet clear if we know enough to make that call, or how able we are to opt out even if we’d like to. “I do worry that we’re going to see this AI arms race pushing ever more of our data into other people’s hands,” Cahn says.

Apple will soon release beta versions of its Apple Intelligence features, starting this fall with the iPhone 15 and the new macOS Sequoia, which can be run on Macs and iPads with M1 chips or newer. Says Apple CEO Tim Cook, “We think Apple intelligence is going to be indispensable.”

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

Every year, some 22,000 Americans a year are killed as a result of serious medical errors in hospitals, many of them on operating tables. There have been cases where surgeons have left surgical sponges inside patients’ bodies or performed the wrong procedure altogether.

Teodor Grantcharov, a professor of surgery at Stanford, thinks he has found a tool to make surgery safer and minimize human error: AI-powered “black boxes” in operating theaters that work in a similar way to an airplane’s black box. These devices, built by Grantcharov’s company Surgical Safety Technologies, record everything in the operating room via panoramic cameras, microphones in the ceiling, and anesthesia monitors before using artificial intelligence to help surgeons make sense of the data. They capture the entire operating room as a whole, from the number of times the door is opened to how many non-case-related conversations occur during an operation.

These black boxes are in use in almost 40 institutions in the US, Canada, and Western Europe, from Mount Sinai to Duke to the Mayo Clinic. But are hospitals on the cusp of a new era of safety—or creating an environment of confusion and paranoia? Read the full story by Simar Bajaj here. 

This resonated with me as a story with broader implications. Organizations in all sectors are thinking about how to adopt AI to make things safer or more efficient. What this example from hospitals shows is that the situation is not always clear cut, and there are many pitfalls you need to avoid. 

Here are three lessons about AI adoption that I learned from this story: 

1. Privacy is important, but not always guaranteed. Grantcharov realized very quickly that the only way to get surgeons to use the black box was to make them feel protected from possible repercussions. He has designed the system to record actions but hide the identities of both patients and staff, even deleting all recordings within 30 days. His idea is that no individual should be punished for making a mistake. 

The black boxes render each person in the recording anonymous; an algorithm distorts people’s voices and blurs out their faces, transforming them into shadowy, noir-like figures. So even if you know what happened, you can’t use it against an individual. 

But this process is not perfect. Before 30-day-old recordings are automatically deleted, hospital administrators can still see the operating room number, the time of the operation, and the patient’s medical record number, so even if personnel are technically de-identified, they aren’t truly anonymous. The result is a sense that “Big Brother is watching,” says Christopher Mantyh, vice chair of clinical operations at Duke University Hospital, which has black boxes in seven operating rooms.

2. You can’t adopt new technologies without winning people over first. People are often justifiably suspicious of the new tools, and the system’s flaws when it comes to privacy are part of why staff have been hesitant to embrace it. Many doctors and nurses actively boycotted the new surveillance tools. In one hospital, the cameras were sabotaged by being turned around or deliberately unplugged. Some surgeons and staff refused to work in rooms where they were in place.

At the hospital where some of the cameras were initially sabotaged, it took up to six months for surgeons to get used to them. But things went much more smoothly once staff understood the guardrails around the technology. They started trusting it more after one-on-one conversations in which bosses explained how the data was automatically de-identified and deleted.

3. More data doesn’t always lead to solutions. You shouldn’t adopt new technologies for the sake of adopting new technologies, if they are not actually useful. But to determine whether AI technologies work for you, you need to ask some hard questions. Some hospitals have reported small improvements based on black-box data. Doctors at Duke University Hospital use the data to check how often antibiotics are given on time, and they report turning to this data to help decrease the amount of time operating rooms sit empty between cases. 

But getting buy-in from some hospitals has been difficult, because there haven’t yet been any large, peer-reviewed studies showing how black boxes actually help to reduce patient complications and save lives. Mount Sinai’s chief of general surgery, Celia Divino, says that too much data can be paralyzing. “How do you interpret it? What do you do with it?” she asks. “This is always a disease.”

Read the full story by Simar Bajaj here. 

Now read the rest of The Algorithm

Deeper Learning

How a simple circuit could offer an alternative to energy-intensive GPUs

On a table in his lab at the University of Pennsylvania, physicist Sam Dillavou has connected an array of breadboards via a web of brightly colored wires. The setup looks like a DIY home electronics project—and not a particularly elegant one. But this unassuming assembly, which contains 32 variable resistors, can learn to sort data like a machine-learning model. The hope is that the prototype will offer a low-power alternative to the energy-guzzling graphical processing unit chips widely used in machine learning. 

Why this matters: AI chips are expensive, and there aren’t enough of them to meet the current demand fueled by the AI boom. Training a large language model takes the same amount of energy as the annual consumption of more than a hundred US homes, and generating an image with generative AI uses as much energy as charging your phone. Dillavou and his colleagues built this circuit as an exploratory effort to find better computing designs. Read more from Sophia Chen here.

Bits and Bytes

Propagandists are using AI too—and companies need to be open about it
OpenAI has reported on influence operations that use its AI tools. Such reporting, alongside data sharing, should become the industry norm, argue Josh A. Goldstein and Renée DiResta. (MIT Technology Review) 

Digital twins are helping scientists run the world’s most complex experiments
Engineers use the high-fidelity models to monitor operations, plan fixes, and troubleshoot problems. Digital twins can also use artificial intelligence and machine learning to help make sense of vast amounts of data. (MIT Technology Review) 

Silicon Valley is in an uproar over California’s proposed AI safety bill
The bill would force companies to create a “kill switch” to turn off powerful AI models, guarantee they will not build systems with “hazardous capabilities such as creating bioweapons,” and report their safety testing. Tech companies argue that this would “hinder innovation” and kill open-source development in California. The tech sector loathes regulation, so expect this bill to face a lobbying storm. (FT) 

OpenAI offers a peek inside the guts of ChatGPT
The company released a new research paper identifying how the AI model that powers ChatGPT works and how it stores certain concepts. The paper was written by the company’s now-defunct superalignment team, which was disbanded after its leaders, including OpenAI cofounder Ilya Sutskever, left the company. OpenAI has faced criticism from former employees who argue that the company is rushing to build AI and ignoring the risks.  (Wired) 

The AI search engine Perplexity is directly ripping off content from news outlets
The buzzy startup, which has been touted as a challenger to Google Search, has republished parts of exclusive stories from multiple publications, including Forbes and Bloomberg, with inadequate attribution. It’s an ominous sign of what could be coming for news media. (Forbes) 

It looked like a reliable news site. It was an AI chop shop.
A wild story about how a site called BNN Breaking, which had amassed millions of readers, an international team of journalists, and a publishing deal with Microsoft, was actually just regurgitating AI-generated content riddled with errors. (NYT) 

On the morning of April 3, Taiwan was hit by a 7.4 magnitude earthquake. Seconds later, hundreds of battery-swap stations in Taiwan sensed something else: the power frequency of the electric grid took a sudden drop, a signal that some power plants had been disconnected in the disaster. The grid was now struggling to meet energy demand. 

These stations, built by the Taiwanese company Gogoro for electric-powered two-wheeled vehicles like scooters, mopeds, and bikes, reacted immediately. According to numbers provided by the company, 590 Gogoro battery-swap locations (some of which have more than one swap station) stopped drawing electricity from the grid, lowering local demand by a total six megawatts—enough to power thousands of homes. It took 12 minutes for the grid to recover, and the battery-swap stations then resumed normal operation.

Gogoro is not the only company working on battery-swapping for electric scooters (New York City recently launched a pilot program to give delivery drivers the option to charge this way), but it’s certainly one of the most successful. Founded in 2011, the firm has a network of over 12,500 stations across Taiwan and boasts over 600,000 monthly subscribers who pay to swap batteries in and out when required. Each station is roughly the size of two vending machines and can hold around 30 scooter batteries.

Now the company is putting the battery network to another use: Gogoro has been working with Enel X, an Italian company, to incorporate the stations into a virtual power plant (VPP) system that helps the Taiwanese grid stay more resilient in emergencies like April’s earthquake. 

Battery-swap stations work well for VPP programs because they offer so much more flexibility than charging at home, where an electric-bike owner usually has just one or two batteries and thus must charge immediately after one runs out. With dozens of batteries in a single station as a demand buffer, Gogoro can choose when it charges them—for instance, doing so at night when there’s less power demand and it’s cheaper. In the meantime, the batteries can give power back to the grid when it is stressed—hence the comparison to power plants.

“What is beautiful is that the stations’ economic interest is aligned with the grid—the [battery-swap companies] have the incentive to time their charges during the low utilization period, paying the low electricity price, while feeding electricity back to the grid during peak period, enjoying a higher price,” says S. Alex Yang, a professor of management science at London Business School. 

Gogoro is uniquely positioned to become a vital part of the VPP network because “there’s a constant load in energy, and then at the same time, we’re on standby that we can either stop taking or giving back [power] to the grid to provide stability,” Horace Luke, cofounder and CEO of Gogoro, tells MIT Technology Review. 

Luke estimates that only 90% of Gogoro batteries are actually on the road powering scooters at any given time, so the rest, sitting on the racks waiting for customers to pick up, become a valuable resource that can be utilized by the grid. 

Today, out of the 2,500 Gogoro locations, over 1,000 are part of the VPP program. Gogoro promises that the system will automatically detect emergencies and, in response, immediately lower its consumption by a certain total amount.

Which stations get included in the VPP depends on where they are and how much capacity they have. A smaller station right outside a metro stop—meaning high demand and low supply—probably can’t afford to stop charging during an emergency because riders could come looking for a battery soon. But a megastation with 120 batteries in a residential area is probably safe to stop charging batteries for a while.

Plus, the entire station doesn’t go dark—Gogoro has a built-in system that decides which or how many batteries in a station stop charging. “We know exactly which batteries to spin down, which station to spin down, how much to spin down,” says Luke. “That was all calculated in real time in the back side of the server.” It can even consolidate the power left in several batteries into one, so a customer who comes in can still leave with a fully charged battery even if the whole system is operating below capacity.

The earthquake and its aftermath in Taiwan this year put the VPP stations to the test—but also showed the system’s strength. On April 15, 12 days after the initial earthquake, the grid in Taiwan was still recovering from the damage when another power drop happened. This time, 818 Gogoro locations reacted in five seconds, reducing power consumption by 11 megawatts for 30 minutes.

Numbers like 6 MW and 11 MW are “not a trivial amount of power but still substantially smaller than a centralized power plant,” says Joshua Pearce, an engineering professor at Western University in Ontario, Canada. For comparison, Taiwan lost 3,200 MW of power supply right after the April earthquake, and the gap was mostly filled by solar power, centralized battery storage, and hydropower. But the entire Taiwanese VPP network combined, which has reached a capacity of 1,350 MW, can make a significant difference. “It helps the grid maintain stability during disasters. The more smart loads there are on the grid, the more resilient it is,” he says. 

However, the potential of these battery-swap stations has not been fully achieved yet; the majority of the stations have not started giving energy back to the grid. 

“The tech system is ready, but the business and economics are not ready,” Luke says. There are 10 Gogoro battery-swapping stations that can return electricity to the grid in a pilot program, but other stations haven’t received the technological update. 

Upgrading stations to bi-directional charging makes economic sense only if Gogoro can profit from selling the electricity back. While the Taiwanese state-owned utility company currently allows private energy generators like solar farms to sell electricity to the grid at a premium, it hasn’t allowed battery-storage companies like Gogoro to do so. 

This challenge is not unique to Taiwan. Incorporating technologies like VPP requires making fundamental changes to the grid, which won’t happen without policy support. “The technology is there, but the practices are being held back by antiquated utility business models where they provide all electric services,” says Pearce. “Fair policies are needed to allow solar energy and battery owners to participate in the electric market for the best interest of all electricity consumers.”

Correction: The story has been updated to clarify that 90%, not 10%, of Gogoro’s batteries are on the road.

Hawk Eye App Data Breach: Who is the hacker?

Hacker Planned Cyberattacks on More Indian Cities

Fortinet announces plans to acquire Lacework, a late-stage cloud security startup that was once listed as a “unicorn” company valued north of $1 billion.

The post Fortinet Expands Cloud Security Portfolio with Lacework Acquisition appeared first on SecurityWeek.

In January 2022, NASA’s $10 billion James Webb Space Telescope was approaching the end of its one-million-mile trip from Earth. But reaching its orbital spot would be just one part of its treacherous journey. To ready itself for observations, the spacecraft had to unfold itself in a complicated choreography that, according to its engineers’ calculations, had 344 different ways to fail. A sunshield the size of a tennis court had to deploy exactly right, ending up like a giant shiny kite beneath the telescope. A secondary mirror had to swing down into the perfect position, relying on three legs to hold it nearly 25 feet from the main mirror. 

Finally, that main mirror—its 18 hexagonal pieces nestled together as in a honeycomb—had to assemble itself. Three golden mirror segments had to unfold from each side of the telescope, notching their edges against the 12 already fitted together. The sequence had to go perfectly for the telescope to work as intended.

“That was a scary time,” says Karen Casey, a technical director for Raytheon’s Air and Space Defense Systems business, which built the software that controls JWST’s movements and is now in charge of its flight operations. 

Over the multiple days of choreography, engineers at Raytheon watched the events unfold as the telescope did. The telescope, beyond the moon’s orbit, was way too distant to be visible, even with powerful instruments. But the telescope was feeding data back to Earth in real time, and software near-simultaneously used that data to render a 3D video of how the process was going, as it was going. It was like watching a very nerve-racking movie.

The 3D video represented a “digital twin” of the complex telescope: a computer-based model of the actual instrument, based on information that the instrument provided. “This was just transformative—to be able to see it,” Casey says.

The team watched tensely, during JWST’s early days, as the 344 potential problems failed to make their appearance. At last, JWST was in its final shape and looked as it should—in space and onscreen. The digital twin has been updating itself ever since.

The concept of building a full-scale replica of such a complicated bit of kit wasn’t new to Raytheon, in part because of the company’s work in defense and intelligence, where digital twins are more popular than they are in astronomy.

JWST, though, was actually more complicated than many of those systems, so the advances its twin made possible will now feed back into that military side of the business. It’s the reverse of a more typical story, where national security pursuits push science forward. Space is where non-defense and defense technologies converge, says Dan Isaacs, chief technology officer for the Digital Twin Consortium, a professional working group, and digital twins are “at the very heart of these collaborative efforts.”

As the technology becomes more common, researchers are increasingly finding these twins to be productive members of scientific society—helping humans run the world’s most complicated instruments, while also revealing more about the world itself and the universe beyond.  

800 million data points

The concept of digital twins was introduced in 2002 by Michael Grieves, a researcher whose work focused on business and manufacturing. He suggested that a digital model of a product, constantly updated with information from the real world, should accompany the physical item through its development. 

But the term “digital twin” actually came from a NASA employee named John Vickers, who first used it in 2010 as part of a technology road map report for the space agency. Today, perhaps unsurprisingly, Grieves is head of the Digital Twins Institute, and Vickers is still with NASA, as its principal technologist. 

Since those early days, technology has advanced, as it is wont to do. The Internet of Things has proliferated, hooking real-world sensors stuck to physical objects into the ethereal internet. Today, those devices number more than 15 billion, compared with mere millions in 2010. Computing power has continued to increase, and the cloud—more popular and powerful than it was in the previous decade—allows the makers of digital twins to scale their models up or down, or create more clones for experimentation, without investing in obscene amounts of hardware. Now, too, digital twins can incorporate artificial intelligence and machine learning to help make sense of the deluge of data points pouring in every second. 

Out of those ingredients, Raytheon decided to build its JWST twin for the same reason it also works on defense twins: there was little room for error. “This was a no-fail mission,” says Casey. The twin tracks 800 million data points about its real-world sibling every day, using all those 0s and 1s to create a real-time video that’s easier for humans to monitor than many columns of numbers. 

The JWST team uses the twin to monitor the observatory and also to predict the effects of changes like software updates. When testing these, engineers use an offline copy of the twin,  upload hypothetical changes, and then watch what happens next. The group also uses an offline version to train operators and to troubleshoot IRL issues—the nature of which Casey declines to identify. “We call them anomalies,” she says. 

Science, defense, and beyond

JWST’s digital twin is not the first space-science instrument to have a simulated sibling. A digital twin of the Curiosity rover helped NASA solve the robot’s heat issues. At CERN, the European particle accelerator, digital twins help with detector development and more mundane tasks like monitoring cranes and ventilation systems. The European Space Agency wants to use Earth observation data to create a digital twin of the planet itself. 

At the Gran Telescopio Canarias, the world’s largest single-mirror telescope, the scientific team started building a twin about two years ago—before they’d even heard the term. Back then, Luis Rodríguez, head of engineering, came to Romano Corradi, the observatory’s director. “He said that we should start to interconnect things,” says Corradi. They could snag principles from industry, suggested Rodríguez, where machines regularly communicate with each other and with computers, monitor their own states, and automate responses to those states.

The team started adding sensors that relayed information about the telescope and its environment. Understanding the environmental conditions around an observatory is “fundamental in order to operate a telescope,” says Corradi. Is it going to rain, for instance, and how is temperature affecting the scope’s focus? 

After they had the sensors feeding data online, they created a 3D model of the telescope that rendered those facts visually. “The advantage is very clear for the workers,” says Rodríguez, referring to those operating the telescope. “It’s more easy to manage the telescope. The telescope in the past was really, really hard because it’s very complex.”

Right now, the Gran Telescopio twin just ingests the data, but the team is working toward a more interpretive approach, using AI to predict the instrument’s behavior. “With information you get in the digital twin, you do something in the real entity,” Corradi says. Eventually, they hope to have a “smart telescope” that responds automatically to its situation. 

Corradi says the team didn’t find out that what they were building had a name until they went to an Internet of Things conference last year. “We saw that there was a growing community in industry—and not in science, in industry—where everybody now is doing these digital twins,” he says.

The concept is, of course, creeping into science—as the particle accelerators and space agencies show. But it’s still got a firmer foothold at corporations. “Always the interest in industry precedes what happens in science,” says Corradi.  But he thinks projects like theirs will continue to proliferate in the broader astronomy community. For instance, the group planning the proposed Thirty Meter Telescope, which would have a primary mirror made up of hundreds of segments, called to request a presentation on the technology. “We just anticipated a bit of what was already happening in the industry,” says Corradi.

The defense industry really loves digital twins. The Space Force, for instance, used one to plan Tetra 5, an experiment to refuel satellites. In 2022, the Space Force also gave Slingshot Aerospace a contract to create a digital twin of space itself, showing what’s going on in orbit to prepare for incidents like collisions. 

Isaacs cites an example in which the Air Force sent a retired plane to a university so researchers could develop a “fatigue profile”—a kind of map of how the aircraft’s stresses, strains, and loads add up over time. A twin, made from that map, can help identify parts that could be replaced to extend the plane’s life, or to design a better plane in the future. Companies that work in both defense and science—common in the space industry in particular—thus have an advantage, in that they can port innovations from one department to another.

JWST’s twin, for instance, will have some relevance for projects on Raytheon’s defense side, where the company already works on digital twins of missile defense radars, air-launched cruise missiles, and aircraft. “We can reuse parts of it in other places,” Casey says. Any satellite the company tracks or sends commands to “could benefit from piece-parts of what we’ve done here.”  

Some of the tools and processes Raytheon developed for the telescope, she continues, “can copy-paste to other programs.” And in that way, the JWST digital twin will probably have twins of its own.

Sarah Scoles is a Colorado-based science journalist and the author, most recently, of the book Countdown: The Blinding Future of Nuclear Weapons.

At the end of May, OpenAI marked a new “first” in its corporate history. It wasn’t an even more powerful language model or a new data partnership, but a report disclosing that bad actors had misused their products to run influence operations. The company had caught five networks of covert propagandists—including players from Russia, China, Iran, and Israel—using their generative AI tools for deceptive tactics that ranged from creating large volumes of social media comments in multiple languages to turning news articles into Facebook posts. The use of these tools, OpenAI noted, seemed intended to improve the quality and quantity of output. AI gives propagandists a productivity boost too.

First and foremost, OpenAI should be commended for this report and the precedent it hopefully sets. Researchers have long expected adversarial actors to adopt generative AI technology, particularly large language models, to cheaply increase the scale and caliber of their efforts. The transparent disclosure that this has begun to happen—and that OpenAI has prioritized detecting it and shutting down accounts to mitigate its impact—shows that at least one large AI company has learned something from the struggles of social media platforms in the years following Russia’s interference in the 2016 US election. When that misuse was discovered, Facebook, YouTube, and Twitter (now X) created integrity teams and began making regular disclosures about influence operations on their platforms. (X halted this activity after Elon Musk’s purchase of the company.) 

OpenAI’s disclosure, in fact, was evocative of precisely such a report from Meta, released a mere day earlier. The Meta transparency report for the first quarter of 2024 disclosed the takedown of six covert operations on its platform. It, too, found networks tied to China, Iran, and Israel and noted the use of AI-generated content. Propagandists from China shared what seem to be AI-generated poster-type images for a “fictitious pro-Sikh activist movement.” An Israel-based political marketing firm posted what were likely AI-generated comments. Meta’s report also noted that one very persistent Russian threat actor was still quite active, and that its strategies were evolving. Perhaps most important, Meta included a direct set of “recommendations for stronger industry response” that called for governments, researchers, and other technology companies to collaboratively share threat intelligence to help disrupt the ongoing Russian campaign.

We are two such researchers, and we have studied online influence operations for years. We have published investigations of coordinated activity—sometimes in collaboration with platforms—and analyzed how AI tools could affect the way propaganda campaigns are waged. Our teams’ peer-reviewed research has found that language models can produce text that is nearly as persuasive as propaganda from human-written campaigns. We have seen influence operations continue to proliferate, on every social platform and focused on every region of the world; they are table stakes in the propaganda game at this point. State adversaries and mercenary public relations firms are drawn to social media platforms and the reach they offer. For authoritarian regimes in particular, there is little downside to running such a campaign, particularly in a critical global election year. And now, adversaries are demonstrably using AI technologies that may make this activity harder to detect. Media is writing about the “AI election,” and many regulators are panicked.

It’s important to put this in perspective, though. Most of the influence campaigns that OpenAI and Meta announced did not have much impact, something the companies took pains to highlight. It’s critical to reiterate that effort isn’t the same thing as engagement: the mere existence of fake accounts or pages doesn’t mean that real people are paying attention to them. Similarly, just because a campaign uses AI does not mean it will sway public opinion. Generative AI reduces the cost of running propaganda campaigns, making it significantly cheaper to produce content and run interactive automated accounts. But it is not a magic bullet, and in the case of the operations that OpenAI disclosed, what was generated sometimes seemed to be rather spammy. Audiences didn’t bite.

Producing content, after all, is only the first step in a propaganda campaign; even the most convincing AI-generated posts, images, or audio still need to be distributed. Campaigns without algorithmic amplification or influencer pickup are often just tweeting into the void. Indeed, it is consistently authentic influencers—people who have the attention of large audiences enthusiastically resharing their posts—that receive engagement and drive the public conversation, helping content and narratives to go viral. This is why some of the more well-resourced adversaries, like China, simply surreptitiously hire those voices. At this point, influential real accounts have far more potential for impact than AI-powered fakes.

Nonetheless, there is a lot of concern that AI could disrupt American politics and become a national security threat. It’s important to “rightsize” that threat, particularly in an election year. Hyping the impact of disinformation campaigns can undermine trust in elections and faith in democracy by making the electorate believe that there are trolls behind every post, or that the mere targeting of a candidate by a malign actor, even with a very poorly executed campaign, “caused” their loss. 

By putting an assessment of impact front and center in its first report, OpenAI is clearly taking the risk of exaggerating the threat seriously. And yet, diminishing the threat or not fielding integrity teams—letting trolls simply continue to grow their followings and improve their distribution capability—would also be a bad approach. Indeed, the Meta report noted that one network it disrupted, seemingly connected to a political party in Bangladesh and targeting the Bangladeshi public, had amassed 3.4 million followers across 98 pages. Since that network was not run by an adversary of interest to Americans, it will likely get little attention. Still, this example highlights the fact that the threat is global, and vigilance is key. Platforms must continue to prioritize threat detection.

So what should we do about this? The Meta report’s call for threat sharing and collaboration, although specific to a Russian adversary, highlights a broader path forward for social media platforms, AI companies, and academic researchers alike. 

Transparency is paramount. As outside researchers, we can learn only so much from a social media company’s description of an operation it has taken down. This is true for the public and policymakers as well, and incredibly powerful platforms shouldn’t just be taken at their word. Ensuring researcher access to data about coordinated inauthentic networks offers an opportunity for outside validation (or refutation!) of a tech company’s claims. Before Musk’s takeover of Twitter, the company regularly released data sets of posts from inauthentic state-linked accounts to researchers, and even to the public. Meta shared data with external partners before it removed a network and, more recently, moved to a model of sharing content from already-removed networks through Meta’s Influence Operations Research Archive. While researchers should continue to push for more data, these efforts have allowed for a richer understanding of adversarial narratives and behaviors beyond what the platform’s own transparency report summaries provided.

OpenAI’s adversarial threat report should be a prelude to more robust data sharing moving forward. Where AI is concerned, independent researchers have begun to assemble databases of misuse—like the AI Incident Database and the Political Deepfakes Incident Database—to allow researchers to compare different types of misuse and track how misuse changes over time. But it is often hard to detect misuse from the outside. As AI tools become more capable and pervasive, it’s important that policymakers considering regulation understand how they are being used and abused. While OpenAI’s first report offered high-level summaries and select examples, expanding data-sharing relationships with researchers that provide more visibility into adversarial content or behaviors is an important next step. 

When it comes to combating influence operations and misuse of AI, online users also have a role to play. After all, this content has an impact only if people see it, believe it, and participate in sharing it further. In one of the cases OpenAI disclosed, online users called out fake accounts that used AI-generated text. 

In our own research, we’ve seen communities of Facebook users proactively call out AI-generated image content created by spammers and scammers, helping those who are less aware of the technology avoid falling prey to deception. A healthy dose of skepticism is increasingly useful: pausing to check whether content is real and people are who they claim to be, and helping friends and family members become more aware of the growing prevalence of generated content, can help social media users resist deception from propagandists and scammers alike.

OpenAI’s blog post announcing the takedown report put it succinctly: “Threat actors work across the internet.” So must we. As we move into an new era of AI-driven influence operations, we must address shared challenges via transparency, data sharing, and collaborative vigilance if we hope to develop a more resilient digital ecosystem.

Josh A. Goldstein is a research fellow at Georgetown University’s Center for Security and Emerging Technology (CSET), where he works on the CyberAI Project. Renée DiResta is the research manager of the Stanford Internet Observatory and the author of Invisible Rulers: The People Who Turn Lies into Reality. 

The first time Teodor Grantcharov sat down to watch himself perform surgery, he wanted to throw the VHS tape out the window.  

“My perception was that my performance was spectacular,” Grantcharov says, and then pauses—“until the moment I saw the video.” Reflecting on this operation from 25 years ago, he remembers the roughness of his dissection, the wrong instruments used, the inefficiencies that transformed a 30-minute operation into a 90-minute one. “I didn’t want anyone to see it.”

This reaction wasn’t exactly unique. The operating room has long been defined by its hush-hush nature—what happens in the OR stays in the OR—because surgeons are notoriously bad at acknowledging their own mistakes. Grantcharov jokes that when you ask “Who are the top three surgeons in the world?” a typical surgeon “always has a challenge identifying who the other two are.”

But after the initial humiliation over watching himself work, Grantcharov started to see the value in recording his operations. “There are so many small details that normally take years and years of practice to realize—that some surgeons never get to that point,” he says. “Suddenly, I could see all these insights and opportunities overnight.”

There was a big problem, though: it was the ’90s, and spending hours playing back grainy VHS recordings wasn’t a realistic quality improvement strategy. It would have been nearly impossible to determine how often his relatively mundane slipups happened at scale—not to mention more serious medical errors like those that kill some 22,000 Americans each year. Many of these errors happen on the operating table, from leaving surgical sponges inside patients’ bodies to performing the wrong procedure altogether.

While the patient safety movement has pushed for uniform checklists and other manual fail-safes to prevent such mistakes, Grantcharov believes that “as long as the only barrier between success and failure is a human, there will be errors.” Improving safety and surgical efficiency became something of a personal obsession. He wanted to make it challenging to make mistakes, and he thought developing the right system to create and analyze recordings could be the key.

It’s taken many years, but Grantcharov, now a professor of surgery at Stanford, believes he’s finally developed the technology to make this dream possible: the operating room equivalent of an airplane’s black box. It records everything in the OR via panoramic cameras, microphones, and anesthesia monitors before using artificial intelligence to help surgeons make sense of the data.

Grantcharov’s company, Surgical Safety Technologies, is not the only one deploying AI to analyze surgeries. Many medical device companies are already in the space—including Medtronic with its Touch Surgery platform, Johnson & Johnson with C-SATS, and Intuitive Surgical with Case Insights.

But most of these are focused solely on what’s happening inside patients’ bodies, capturing intraoperative video alone. Grantcharov wants to capture the OR as a whole, from the number of times the door is opened to how many non-case-related conversations occur during an operation. “People have simplified surgery to technical skills only,” he says. “You need to study the OR environment holistically.”

COURTESY OF SURGICAL SAFETY TECHNOLOGIES

Success, however, isn’t as simple as just having the right technology. The idea of recording everything presents a slew of tricky questions around privacy and could raise the threat of disciplinary action and legal exposure. Because of these concerns, some surgeons have refused to operate when the black boxes are in place, and some of the systems have even been sabotaged. Aside from those problems, some hospitals don’t know what to do with all this new data or how to avoid drowning in a deluge of statistics.

Grantcharov nevertheless predicts that his system can do for the OR what black boxes did for aviation. In 1970, the industry was plagued by 6.5 fatal accidents for every million flights; today, that’s down to less than 0.5. “The aviation industry made the transition from reactive to proactive thanks to data,” he says—“from safe to ultra-safe.”

Grantcharov’s black boxes are now deployed at almost 40 institutions in the US, Canada, and Western Europe, from Mount Sinai to Duke to the Mayo Clinic. But are hospitals on the cusp of a new era of safety—or creating an environment of confusion and paranoia?

Shaking off the secrecy

The operating room is probably the most measured place in the hospital but also one of the most poorly captured. From team performance to instrument handling, there is “crazy big data that we’re not even recording,” says Alexander Langerman, an ethicist and head and neck surgeon at Vanderbilt University Medical Center. “Instead, we have post hoc recollection by a surgeon.”

Indeed, when things go wrong, surgeons are supposed to review the case at the hospital’s weekly morbidity and mortality conferences, but these errors are notoriously underreported. And even when surgeons enter the required notes into patients’ electronic medical records, “it’s undoubtedly—and I mean this in the least malicious way possible—dictated toward their best interests,” says Langerman. “It makes them look good.”

The operating room wasn’t always so secretive.

In the 19th century, operations often took place in large amphitheaters—they were public spectacles with a general price of admission. “Every seat even of the top gallery was occupied,” recounted the abdominal surgeon Lawson Tait about an operation in the 1860s. “There were probably seven or eight hundred spectators.”

However, around the 1900s, operating rooms became increasingly smaller and less accessible to the public—and its germs. “Immediately, there was a feeling that something was missing, that the public surveillance was missing. You couldn’t know what happened in the smaller rooms,” says Thomas Schlich, a historian of medicine at McGill University.

And it was nearly impossible to go back. In the 1910s a Boston surgeon, Ernest Codman, suggested a form of surveillance known as the end-result system, documenting every operation (including failures, problems, and errors) and tracking patient outcomes. Massachusetts General Hospital didn’t accept it, says Schlich, and Codman resigned in frustration.  

PUBLIC DOMAIN VIA WIKIPEDIA

Such opacity was part of a larger shift toward medicine’s professionalization in the 20th century, characterized by technological advancements, the decline of generalists, and the bureaucratization of health-care institutions. All of this put distance between patients and their physicians. Around the same time, and particularly from the 1960s onward, the medical field began to see a rise in malpractice lawsuits—at least partially driven by patients trying to find answers when things went wrong.

This battle over transparency could theoretically be addressed by surgical recordings. But Grantcharov realized very quickly that the only way to get surgeons to use the black box was to make them feel protected. To that end, he has designed the system to record the action but hide the identity of both patients and staff, even deleting all recordings within 30 days. His idea is that no individual should be punished for making a mistake. “We want to know what happened, and how we can build a system that makes it difficult for this to happen,” Grantcharov says. Errors don’t occur because “the surgeon wakes up in the morning and thinks, ‘I’m gonna make some catastrophic event happen,’” he adds. “This is a system issue.”

AI that sees everything

Grantcharov’s OR black box is not actually a box at all, but a tablet, one or two ceiling microphones, and up to four wall-mounted dome cameras that can reportedly analyze more than half a million data points per day per OR. “In three days, we go through the entire Netflix catalogue in terms of video processing,” he says.

The black-box platform utilizes a handful of computer vision models and ultimately spits out a series of short video clips and a dashboard of statistics—like how much blood was lost, which instruments were used, and how many auditory disruptions occurred. The system also identifies and breaks out key segments of the procedure (dissection, resection, and closure) so that instead of having to watch a whole three- or four-hour recording, surgeons can jump to the part of the operation where, for instance, there was major bleeding or a surgical stapler misfired.

Critically, each person in the recording is rendered anonymous; an algorithm distorts people’s voices and blurs out their faces, transforming them into shadowy, noir-like figures. “For something like this, privacy and confidentiality are critical,” says Grantcharov, who claims the anonymization process is irreversible. “Even though you know what happened, you can’t really use it against an individual.”

Another AI model works to evaluate performance. For now, this is done primarily by measuring compliance with the surgical safety checklist—a questionnaire that is supposed to be verbally ticked off during every type of surgical operation. (This checklist has long been associated with reductions in both surgical infections and overall mortality.) Grantcharov’s team is currently working to train more complex algorithms to detect errors during laparoscopic surgery, such as using excessive instrument force, holding the instruments in the wrong way, or failing to maintain a clear view of the surgical area. However, assessing these performance metrics has proved more difficult than measuring checklist compliance. “There are some things that are quantifiable, and some things require judgment,” Grantcharov says.

Each model has taken up to six months to train, through a labor-intensive process relying on a team of 12 analysts in Toronto, where the company was started. While many general AI models can be trained by a gig worker who labels everyday items (like, say, chairs), the surgical models need data annotated by people who know what they’re seeing—either surgeons, in specialized cases, or other labelers who have been properly trained. They have reviewed hundreds, sometimes thousands, of hours of OR videos and manually noted which liquid is blood, for instance, or which tool is a scalpel. Over time, the model can “learn” to identify bleeding or particular instruments on its own, says Peter Grantcharov, Surgical Safety Technologies’ vice president of engineering, who is Teodor Grantcharov’s son.

For the upcoming laparoscopic surgery model, surgeon annotators have also started to label whether certain maneuvers were correct or mistaken, as defined by the Generic Error Rating Tool—a standardized way to measure technical errors.

While most algorithms operate near perfectly on their own, Peter Grantcharov explains that the OR black box is still not fully autonomous. For example, it’s difficult to capture audio through ceiling mikes and thus get a reliable transcript to document whether every element of the surgical safety checklist was completed; he estimates that this algorithm has a 15% error rate. So before the output from each procedure is finalized, one of the Toronto analysts manually verifies adherence to the questionnaire. “It will require a human in the loop,” Peter Grantcharov says, but he gauges that the AI model has made the process of confirming checklist compliance 80% to 90% more efficient. He also emphasizes that the models are constantly being improved.

In all, the OR black box can cost about $100,000 to install, and analytics expenses run $25,000 annually, according to Janet Donovan, an OR nurse who shared with MIT Technology Review an estimate given to staff at Brigham and Women’s Faulkner Hospital in Massachusetts. (Peter Grantcharov declined to comment on these numbers, writing in an email: “We don’t share specific pricing; however, we can say that it’s based on the product mix and the total number of rooms, with inherent volume-based discounting built into our pricing models.”)

 “Big brother is watching”

Long Island Jewish Medical Center in New York, part of the Northwell Health system, was the first hospital to pilot OR black boxes, back in February 2019. The rollout was far from seamless, though not necessarily because of the tech.

“In the colorectal room, the cameras were sabotaged,” recalls Northwell’s chair of urology, Louis Kavoussi—they were turned around and deliberately unplugged. In his own OR, the staff fell silent while working, worried they’d say the wrong thing. “Unless you’re taking a golf or tennis lesson, you don’t want someone staring there watching everything you do,” says Kavoussi, who has since joined the scientific advisory board for Surgical Safety Technologies.

Grantcharov’s promises about not using the system to punish individuals have offered little comfort to some OR staff. When two black boxes were installed at Faulkner Hospital in November 2023, they threw the department of surgery into crisis. “Everybody was pretty freaked out about it,” says one surgical tech who asked not to be identified by name since she wasn’t authorized to speak publicly. “We were being watched, and we felt like if we did something wrong, our jobs were going to be on the line.”

It wasn’t that she was doing anything illegal or spewing hate speech; she just wanted to joke with her friends, complain about the boss, and be herself without the fear of administrators peeking over her shoulder. “You’re very aware that you’re being watched; it’s not subtle at all,” she says. The early days were particularly challenging, with surgeons refusing to work in the black-box-equipped rooms and OR staff boycotting those operations: “It was definitely a fight every morning.”

At some level, the identity protections are only half measures. Before 30-day-old recordings are automatically deleted, Grantcharov acknowledges, hospital administrators can still see the OR number, the time of operation, and the patient’s medical record number, so even if OR personnel are technically de-identified, they aren’t truly anonymous. The result is a sense that “Big Brother is watching,” says Christopher Mantyh, vice chair of clinical operations at Duke University Hospital, which has black boxes in seven ORs. He will draw on aggregate data to talk generally about quality improvement at departmental meetings, but when specific issues arise, like breaks in sterility or a cluster of infections, he will look to the recordings and “go to the surgeons directly.”

In many ways, that’s what worries Donovan, the Faulkner Hospital nurse. She’s not convinced the hospital will protect staff members’ identities and is worried that these recordings will be used against them—whether through internal disciplinary actions or in a patient’s malpractice suit. In February 2023, she and almost 60 others sent a letter to the hospital’s chief of surgery objecting to the black box. She’s since filed a grievance with the state, with arbitration proceedings scheduled for October.

The legal concerns in particular loom large because, already, over 75% of surgeons report having been sued at least once, according to a 2021 survey by Medscape, an online resource hub for health-care professionals. To the layperson, any surgical video “looks like a horror show,” says Vanderbilt’s Langerman. “Some plaintiff’s attorney is going to get ahold of this, and then some jury is going to see a whole bunch of blood, and then they’re not going to know what they’re seeing.” That prospect turns every recording into a potential legal battle.

From a purely logistical perspective, however, the 30-day deletion policy will likely insulate these recordings from malpractice lawsuits, according to Teneille Brown, a law professor at the University of Utah. She notes that within that time frame, it would be nearly impossible for a patient to find legal representation, go through the requisite conflict-of-interest checks, and then file a discovery request for the black-box data. While deleting data to bypass the judicial system could provoke criticism, Brown sees the wisdom of Surgical Safety Technologies’ approach. “If I were their lawyer, I would tell them to just have a policy of deleting it because then they’re deleting the good and the bad,” she says. “What it does is orient the focus to say, ‘This is not about a public-facing audience. The audience for these videos is completely internal.’”

A data deluge

When it comes to improving quality, there are “the problem-first people, and then there are the data-first people,” says Justin Dimick, chair of the department of surgery at the University of Michigan. The latter, he says, push “massive data collection” without first identifying “a question of ‘What am I trying to fix?’” He says that’s why he currently has no plans to use the OR black boxes in his hospital.

Mount Sinai’s chief of general surgery, Celia Divino, echoes this sentiment, emphasizing that too much data can be paralyzing. “How do you interpret it? What do you do with it?” she asks. “This is always a disease.”

At Northwell, even Kavoussi admits that five years of data from OR black boxes hasn’t been used to change much, if anything. He says that hospital leadership is finally beginning to think about how to use the recordings, but a hard question remains: OR black boxes can collect boatloads of data, but what does it matter if nobody knows what to do with it?

Grantcharov acknowledges that the information can be overwhelming. “In the early days, we let the hospitals figure out how to use the data,” he says. “That led to a big variation in how the data was operationalized. Some hospitals did amazing things; others underutilized it.” Now the company has a dedicated “customer success” team to help hospitals make sense of the data, and it offers a consulting-type service to work through surgical errors. But ultimately, even the most practical insights are meaningless without buy-in from hospital leadership, Grantcharov suggests.

Getting that buy-in has proved difficult in some centers, at least partly because there haven’t yet been any large, peer-reviewed studies showing how OR black boxes actually help to reduce patient complications and save lives. “If there’s some evidence that a comprehensive data collection system—like a black box—is useful, then we’ll do it,” says Dimick. “But I haven’t seen that evidence yet.”

COURTESY OF SURGICAL SAFETY TECHNOLOGIES

The best hard data thus far is from a 2022 study published in the Annals of Surgery, in which Grantcharov and his team used OR black boxes to show that the surgical checklist had not been followed in a fifth of operations, likely contributing to excess infections. He also says that an upcoming study, scheduled to be published this fall, will show that the OR black box led to an improvement in checklist compliance and reduced ICU stays, reoperations, hospital readmissions, and mortality.

On a smaller scale, Grantcharov insists that he has built a steady stream of evidence showing the power of his platform. For example, he says, it’s revealed that auditory disruptions—doors opening, machine alarms and personal pagers going off—happen every minute in gynecology ORs, that a median 20 intraoperative errors are made in each laparoscopic surgery case, and that surgeons are great at situational awareness and leadership while nurses excel at task management.

Meanwhile, some hospitals have reported small improvements based on black-box data. Duke’s Mantyh says he’s used the data to check how often antibiotics are given on time. Duke and other hospitals also report turning to this data to help decrease the amount of time ORs sit empty between cases. By flagging when “idle” times are unexpectedly long and having the Toronto analysts review recordings to explain why, they’ve turned up issues ranging from inefficient communication to excessive time spent bringing in new equipment.

That can make a bigger difference than one might think, explains Ra’gan Laventon, clinical director of perioperative services at Texas’s Memorial Hermann Sugar Land Hospital: “We have multiple patients who are depending on us to get to their care today. And so the more time that’s added in some of these operational efficiencies, the more impactful it is to the patient.”

The real world

At Northwell, where some of the cameras were initially sabotaged, it took a couple of weeks for Kavoussi’s urology team to get used to the black boxes, and about six months for his colorectal colleagues. Much of the solution came down to one-on-one conversations in which Kavoussi explained how the data was automatically de-identified and deleted.

During his operations, Kavoussi would also try to defuse the tension, telling the OR black box “Good morning, Toronto,” or jokingly asking, “How’s the weather up there?” In the end, “since nothing bad has happened, it has become part of the normal flow,” he says.

The reality is that no surgeon wants to be an average operator, “but statistically, we’re mostly average surgeons, and that’s okay,” says Vanderbilt’s Langerman. “I’d hate to be a below-average surgeon, but if I was, I’d really want to know about it.” Like athletes watching game film to prepare for their next match, surgeons might one day review their recordings, assessing their mistakes and thinking about the best ways to avoid them—but only if they feel safe enough to do so.

“Until we know where the guardrails are around this, there’s such a risk—an uncertain risk—that no one’s gonna let anyone turn on the camera,” Langerman says. “We live in a real world, not a perfect world.”

Simar Bajaj is an award-winning science journalist and 2024 Marshall Scholar. He has previously written for the Washington Post, Time magazine, the Guardian, NPR, and the Atlantic, as well as the New England Journal of Medicine, Nature Medicine, and The Lancet. He won Science Story of the Year from the Foreign Press Association in 2022 and the top prize for excellence in science communications from the National Academies of Science, Engineering, and Medicine in 2023. Follow him on X at @SimarSBajaj.

On Tuesday, the FDA asked a panel of experts to weigh in on whether the evidence shows that MDMA, also known as ecstasy, is a safe and efficacious treatment for PTSD. The answer was a resounding no. Just two out of 11 panel members agreed that MDMA-assisted therapy is effective. And only one panel member thought the benefits of the therapy outweighed the risks.

The outcome came as a surprise to many, given that trial results have been positive. And it is also a blow for advocates who have been working to bring psychedelic therapy into mainstream medicine for more than two decades. This isn’t the final decision on MDMA. The FDA has until August 11 to make that ruling. But while the agency is under no obligation to follow the recommendations of its advisory committees, it rarely breaks with their decisions.  

Today on The Checkup, let’s unpack the advisory committee’s vote and talk about what it means for the approval of other recreational drugs as therapies.

One of the main stumbling blocks for the committee was the design of the two efficacy studies that have been completed. Trial participants weren’t supposed to know whether they were in the treatment group, but the effects of MDMA make it pretty easy to tell whether you’ve been given a hefty dose, and most correctly guessed which group they had landed in. 

In 2021, MIT Technology Review’s Charlotte Jee interviewed an MDMA trial participant named Nathan McGee. “Almost as soon as I said I didn’t think I’d taken it, it kicked in. I mean, I knew,” he told her. “I remember going to the bathroom and looking in the mirror, and seeing my pupils looking like saucers. I was like, ‘Wow, okay.’”

The Multidisciplinary Association for Psychedelic Studies, better known as MAPS, has been working with the FDA to develop MDMA as a treatment since 2001. When the organization met with the FDA in 2016 to hash out the details of its phase III trials, studies to test whether a treatment works, agency officials suggested that MAPS use an active compound for the control group to help mask whether participants had received the drug. But MAPS pushed back, and the trial forged ahead with a placebo. 

No surprise, then, that about 90% of those assigned to the MDMA group and 75% of those assigned to the placebo group accurately identified which arm of the study they had landed in. And it wasn’t just participants. Therapists treating the participants also likely knew whether those under their supervision had been given the drug. It’s called “functional unblinding,” and the issue came up at the committee meeting again and again. Here’s why it’s a problem: If a participant strongly believes that MDMA will help their PTSD and they know they’ve received MDMA, this expectation bias could amplify the treatment effect. This is especially a problem when the outcome is based on subjective measures like how a person feels rather than, say, laboratory data.

Another sticking point was the therapy component of the treatment. Lykos Therapeutics (the for-profit spinoff of MAPS) asked the FDA to approve MDMA-assisted therapy: that’s MDMA administered in concert with psychotherapy. Therapists oversaw participants during the three MDMA sessions. But participants also received three therapy sessions before getting the drug, and three therapy sessions afterwards to help them process their experience. 

Because the two treatments were administered together, there was no good way to tell how much of the effect was due to MDMA and how much was due to the therapy. What’s more, “the content or approach of these integrated sessions was not standardized in the treatment manuals and was mainly left up to the individual therapist,” said David Millis, a clinical reviewer for the FDA, at the committee meeting. 

Several committee members also raised safety concerns. They worried that MDMA’s effects might make people more suggestible and vulnerable to abuse, and they brought up allegations of ethics violations outlined in a recent report from the Institute for Clinical and Economic Review. 

Because of these issues and others, most committee members felt compelled to vote against MDMA-assisted therapy. “I felt that the large positive effect was denuded by the significant confounders,” said committee member Maryann Amirshahi, a professor of emergency medicine at Georgetown University School of Medicine, after the vote. “Although I do believe that there was a signal, it just needs to be better studied.”

Whether this decision will be a setback for the entire field remains to be seen. “To make it crystal clear: It isn’t MDMA itself that was rejected per se, but the specific, poor data set provided by Lykos Therapeutics; in my opinion, there is still a strong chance that MDMA, with a properly conducted clinical Phase 3 trial program that addresses those concerns of the FDA advisory committee, will get approved.” wrote Christian Angermayer, founder of ATAI Therapeutics, a company that is also working to develop MDMA as a therapy.

If the FDA denies approval of MDMA therapy, Lykos or another company could conduct additional studies and reapply. Many of the committee members said they believed MDMA does hold promise, but that the studies conducted thus far were inadequate to demonstrate the drug’s safety and efficacy. 

Psilocybin is likely to be the next psychedelic therapy considered by the FDA, and in some ways, it might have an easier path to approval. The idea behind MDMA is that it alleviates PTSD by helping facilitate psychotherapy. The therapy is a crucial component of the treatment, which is problematic because the FDA regulates drugs, not psychotherapy. With psilocybin, a therapist is present, but the drug appears to do the heavy lifting. “We are not offering therapy; we are offering psychological support that’s designed for the patient’s safety and well-being,” says Kabir Nath, CEO of Compass Pathways, the company working to bring psilocybin to market. “What we actually find during a six- to eight-hour session is most of it is silent. There’s actually no interaction.”

That could make the approval process more straightforward. “The difficult thing … is that we don’t regulate psychotherapy, and also we don’t really have any say in the design or the implementation of the particular therapy that is going to be used,” said Tiffany  Farchione, director of the FDA’s division of psychiatry, at the committee meeting. “This is something unprecedented, so we certainly want to get as many opinions and as much input as we can.” 

Another thing

Earlier this week, I explored what might happen if MDMA gets FDA approval and how the decision could affect other psychedelic therapies. 

Sally Adee dives deep into the messy history of electric medicine and what the future might hold for research into electric therapies. “Instead of focusing only on the nervous system—the highway that carries electrical messages between the brain and the body—a growing number of researchers are finding clever ways to electrically manipulate cells elsewhere in the body, such as skin and kidney cells, more directly than ever before,” she writes. 

Now read the rest of The Checkup

Read more from MIT Technology Review’s archive

Psychedelics are undeniably having a moment, and the therapy might prove particularly beneficial to women, wrote Taylor Majewski in this feature from 2022.

In a previous issue of The Checkup, Jessica Hamzelou argued that the psychedelic hype bubble might be about to burst.

MDMA does seem to have helped some individuals. Nathan McGee, who took the drug as part of a clinical trial, told Charlotte Jee that he “understands what joy is now.” 

Researchers are working to design virtual-reality programs that recreate the trippy experience of taking psychedelics. Hana Kiros has the story. 

From around the web

In April I wrote about Lisa Pisano, the second person to receive a pig kidney. This week doctors removed the kidney after it failed owing to lack of blood flow.

Bird flu is still very much in the news.

–   Finland is poised to become the first country to start administering bird flu vaccine—albeit to a very limited subset of people, including poultry and mink farmers, vets, and scientists who study the virus  (Stat)

–   What are the most pressing questions about bird flu? They revolve around what’s happening in cows, what’s happening in farm workers, and what’s happening to the virus. (Stat)

– A man in Mexico has died of H5N2, a strain of bird flu that has never before been reported in humans. (CNN)

Biodegradable, squishy sensors injected into the brain hold promise for detecting changes following a head injury or cancer treatment. (Nature)

A synthetic version of a hallucinogenic toad toxin could be a promising treatment for mental-health disorders. (Undark)

This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.

There are two things I love to do at social gatherings: play board games and talk about climate change. Don’t I sound like someone you should invite to your next dinner party?

Given my two great loves, I was delighted to learn about a board game called Catan: New Energies, coming out this summer. It’s a new edition of the classic game Catan, formerly known as Settlers of Catan. This version has players building power plants, fueled by either fossil fuels or renewables. 

So how does an energy-focused edition of Catan stack up against the board game competition, and what does it say about how we view climate technology?

Catan debuted in 1995, and today it’s one of the world’s most popular board games. The original and related products have sold over 45 million copies worldwide. 

Given Catan’s superstar status, I was intrigued to learn late last year that the studio that makes it had plans in the works to release this new version. I quickly got in touch with the game’s co-creator, Benjamin Teuber, to hear more. 

“The whole idea is that energy comes to Catan,” Teuber told me. “Now the question is, which energy comes to Catan?” Power plants help players develop their society more quickly, amassing more of the points needed to win the game. Players can build fossil-fuel plants, represented by little brown tokens. These are less resource-intensive to build, but they produce pollution. Alternatively, players can elect to build renewable-power plants, signified by green tokens, which are costlier but don’t have the same negative effects in the game. 

As a climate reporter, I feel that some elements of the game setup ring true—for example, as players reach higher levels of pollution, disasters become more likely, but there’s still a strong element of chance involved. 

One aspect of the game that didn’t quite match reality was the cost difference between fossil fuels and renewables. Technologies like solar and wind have plummeted in price over the last decade—today, building new renewable projects is generally cheaper than operating existing coal plants in the US.

I asked if the creators had considered having renewables get cheaper over time in the game, and Teuber said the team had actually built an early version with this idea in place, but the whole thing got too complicated. Keeping things simple enough to be playable is a crucial component of game design, Teuber says. 

Teuber also seemed laser focused on not preaching, and it feels as if New Energies goes out of its way not to make players feel bad about climate change. In fact, as a story by NPR about the game pointed out, the phrase “climate change” hardly appears in any of the promotional materials, on the packaging, or in the rules. The catch-all issue in the game’s universe is simply “pollution.” 

Unlike some other climate games, like the 2023 release Daybreak, New Energies isn’t aimed at getting the group to work together to fight against climate change. The setup is the same as in other versions of Catan: the first player to reach 10 victory points wins. In theory, that could be a player who leaned heavily on fossil fuels. 

“It doesn’t feel like the game says, ‘Screw you—we told you, the only way to win is by building green energy,’” Teuber told me. 

However, while players can choose their own pathway to acquiring points, there’s a second possible outcome. If too many players produce too much pollution by building towns, cities, and fossil-fuel power plants, the game ends early in catastrophe. Whoever has done the most to clean up the environment does walk away with the win—something of a consolation prize. 

I got an early copy of the game to test out, and the first time I played, my group polluted too quickly and the game ended early. I ended up taking the win, since I had elected to build only renewable plants. I’ll admit to feeling a bit smug. 

But as I played more, I saw the balance between competition and collaboration. During one game, my group came within a few turns of pollution-driven catastrophe. We turned things around, building more renewable plants and stretching out play long enough for a friend who had been quicker to build her society to cobble together the points she needed to win. 

Board games, or any other media that deals with climate change, will have to walk a fine line between dealing seriously with the crisis at hand and being entertaining enough to engage with. New Energies does that, though I think it makes some concessions toward being playable over being obsessively accurate. 

I wouldn’t recommend using this game as teaching material about climate change, but I suppose that’s not the point. If you’re a fan of Catan, this edition is definitely worth playing, and it’ll be part of my rotation. You can pre-order Catan New Energies here; the release date is June 14. And if you haven’t heard enough of my media musings, stay tuned for an upcoming story about New Energies and other climate-related board games. 

Now read the rest of The Spark

Related reading

Google DeepMind can take a short description or sketch and turn it into a playable video game. 

Researchers love testing AI by having models play video games. A new model that can play Goat Simulator could be a step toward more useful AI.

Dark Forest shows how advanced cryptography can be used in video games.

Keeping up with climate  

Direct air capture may be getting cheaper and better. Climeworks says that the third generation of its technology can suck up more carbon dioxide from the atmosphere with less energy. (Heatmap)

A Massachusetts town will be home to a new pilot project that basically amounts to a communal heating and cooling system. District energy projects could help energy go farther in cities and densely populated communities. (Associated Press)

Sublime Systems uses an electrochemical process to make cement without the massive emissions footprint. The company just installed its first commercial project in a Boston office park. (Canary Media)

→ According to the Canary story, one of the company’s developers heard about Sublime from a story in our publication! Read my deep dive into the startup from earlier this year. (MIT Technology Review)

A rush of renewable energy to the grid has led to some special periods with ultra-cheap or even free electricity. Experts warn that this could slow further deployment of renewables. (Bloomberg)

Natural disasters, some fueled by climate change, are throwing off medical procedures like fertility treatments, which require specific timing and careful control. (The 19th)

Take an inside look at Apple’s recycling robot, Daisy. The equipment can take apart over a million iPhones per year, but that’s a drop in the bucket given the hundreds of millions discarded annually. (TechCrunch)

Canada’s hydroelectric dams have been running a bit dry, and the country has had to import electricity from the US to make up the difference. It’s just one more example of how changing weather patterns can throw a wrench into climate solutions. (New York Times) 

Check out five demos from a high-tech energy conference, from batteries that can handle freezing temperatures to turbines that can harness power from irrigation channels. (IEEE Spectrum)

Understanding the TSCOP App Cyberattack

TSCOP App Cyberattack Samples

Experts Site Weak System Behind TSCOP App Cyberattack

This story first appeared in China Report, MIT Technology Review’s newsletter about technology in China. Sign up to receive it in your inbox every Tuesday.

Have you ever thought about the miraculous fact that despite the myriad differences between languages, virtually everyone uses the same QWERTY keyboards? Many languages have more or fewer than 26 letters in their alphabet—or no “alphabet” at all, like Chinese, which has tens of thousands of characters. Yet somehow everyone uses the same keyboard to communicate.

Last week, MIT Technology Review published an excerpt from a new book, The Chinese Computer, which talks about how this problem was solved in China. After generations of work to sort Chinese characters, modify computer parts, and create keyboard apps that automatically predict the next character, it is finally possible for any Chinese speaker to use a QWERTY keyboard. 

But the book doesn’t stop there. It ends with a bigger question about what this all means: Why is it necessary for speakers of non-Latin languages to adapt modern technologies for their uses, and what do their efforts contribute to computing technologies?

I talked to the book’s author, Tom Mullaney, a professor of history at Stanford University. We ended up geeking out over keyboards, computers, the English-centric design that underlies everything about computing, and even how keyboards affect emerging technologies like virtual reality. Here are some of his most fascinating answers, lightly edited for clarity and brevity. 

Mullaney’s book covers many experiments across multiple decades that ultimately made typing Chinese possible and efficient on a QWERTY keyboard, but a similar process has played out all around the world. Many countries with non-Latin languages had to work out how they could use a Western computer to input and process their own languages.

Mullaney: In the Chinese case—but also in Japanese, Korean, and many other non-Western writing systems—this wasn’t done for fun. It was done out of brute necessity because the dominant model of keyboard-based computing, born and raised in the English-speaking world, is not compatible with Chinese. It doesn’t work because the keyboard doesn’t have the necessary real estate. And the question became: I have a few dozen keys but 100,000 characters. How do I map one onto the other? 

Simply put, half of the population on Earth uses the QWERTY keyboard in ways the QWERTY keyboard was never intended to be used, creating a radically different way of interacting with computers.

The root of all of these problems is that computers were designed with English as the default language. So the way English works is just the way computers work today.

The English modularity is so fundamental in computing that non-Latin speakers are still grappling with the impacts today despite decades of hard work to change things.

Mullaney shared a complaint that Arabic speakers made in 2022 about Adobe InDesign, the most popular publishing design software. As recently as two years ago, pasting a string of Arabic text into the software could cause the text to become messed up, misplacing its diacritic marks, which are crucial for indicating phonetic features of the text. It turns out you need to install a Middle East version of the software and apply some deliberate workarounds to avoid the problem.

M: Latin alphabetic dominance is still alive and well; it has not been overthrown. And there’s a troubling question as to whether it can ever be overthrown. Some turn was made, some path taken that advantaged certain writing systems at a deep structural level and disadvantaged others. 

That deeply rooted English-centric design is why mainstream input methods never deviate too far from the keyboards that we all know and love/hate. In the English-speaking world, there have been numerous attempts to reimagine the way text input works. Technologies such as the T9 phone keyboard or the Palm Pilot handwriting alphabet briefly achieved some adoption. But they never stick for long because most developers snap back to QWERTY keyboards at the first opportunity.

M: T9 was born in the context of disability technology and was incorporated into the first mobile phones because button real estate was a major problem (prior to the BlackBerry reintroducing the QWERTY keyboard). It was a necessity; [developers] actually needed to think in a different way. But give me enough space, give me 12 inches by 14 inches, and I’ll default to a QWERTY keyboard.

Every 10 years or so, some Western tech company or inventor announces: “Everybody! I have finally figured out a more advanced way of inputting English at much higher speeds than the QWERTY keyboard.” And time and time again there is zero market appetite. 

Will the QWERTY keyboard stick around forever? After this conversation, I’m secretly hoping it won’t. Maybe it’s time for a change. With new technologies like VR headsets, and other gadgets on the horizon, there may come a time when QWERTY keyboards are not the first preference, and non-Latin languages may finally have a chance in shaping the new norm of human-computer interactions. 

M: It’s funny, because now as you go into augmented and virtual reality, Silicon Valley companies are like, “How do we overcome the interface problem?” Because you can shrink everything except the QWERTY keyboard. And what Western engineers fail to understand is that it’s not a tech problem—it’s a technological cultural problem. And they just don’t get it. They think that if they just invent the tech, it is going to take off. And thus far, it never has.

If I were a software or hardware developer, I would be hanging out in online role-playing games, just in the chat feature; I would be watching people use their TV remote controls to find the title of the film they’re looking for; I would look at how Roblox players chat with each other. It’s going to come from some arena outside the mainstream, because the mainstream is dominated by QWERTY.

What are other signs of the dominance of English in modern computing? I’d love to hear about the geeky details you’ve noticed. Send them to zeyi@technologyreview.com.

Now read the rest of China Report

Catch up with China

1. Today marks the 35th anniversary of the student protests and subsequent massacre in Tiananmen Square in Beijing. 

• For decades, Hong Kong was the hub for Tiananmen memorial events. That’s no longer the case, due to Beijing’s growing control over the city’s politics after the 2019 protests. (New Yorker $)
• To preserve the legacy of the student protesters at Tiananmen, it’s also important to address ethical questions about how American universities and law enforcement have been treating college protesters this year. (The Nation)

2. A Chinese company that makes laser sensors was labeled by the US government as a security concern. A few months later, it discreetly rebranded as a Michigan-registered company called “American Lidar.” (Wall Street Journal $)

3. It’s a tough time to be a celebrity in China. An influencer dubbed “China’s Kim Kardashian” for his extravagant displays of wealth has just been banned by multiple social media platforms after the internet regulator announced an effort to clear out “​​ostentatious personas.” (Financial Times $)

• Meanwhile, Taiwanese celebrities who also have large followings in China are increasingly finding themselves caught in political crossfires. (CNN)

4. Cases of Chinese students being rejected entry into the US reveals divisions within the Biden administration. Customs agents, who work for the Department of Homeland Security, have canceled an increasing number of student visas that had already been approved by the State Department. (Bloomberg $)

5. Palau, a small Pacific island nation that’s one of the few countries in the world that recognizes Taiwan as a sovereign country, says it is under cyberattack by China. (New York Times $)

6. After being the first space mission to collect samples from the moon’s far side, China’s Chang’e-6 lunar probe has begun its journey back to Earth. (BBC)

7. The Chinese government just set up the third and largest phase of its semiconductor investment fund to prop up its domestic chip industry. This one’s worth $47.5 billion. (Bloomberg $)

• In 2022, the fund was rocked by corruption charges. (MIT Technology Review)

Lost in translation

The Chinese generative AI community has been stirred up by the first discovery of a Western large language model plagiarizing a Chinese one, according to the Chinese publication PingWest. 

Last week, two undergraduate computer science students at Stanford University released an open-source model called Llama 3-V that they claimed is more powerful than LLMs made by OpenAI and Google, while costing less. But Chinese AI researchers soon found out that Llama 3-V had copied the structure, configuration files, and code from MiniCPM-Llama3-V 2.5, another open-source LLM developed by China’s Tsinghua University and ModelBest Inc, a Chinese startup. 

What proved the plagiarism was the fact that the Chinese team secretly trained the model on a collection of Chinese writings on bamboo slips from 2000 years ago, and no other LLMs can recognize the Chinese characters in this ancient writing style accurately. But Llama 3-V could recognize these characters as well as MiniCPM, while making the exact same mistakes as the Chinese model. The students who released Llama 3-V have removed the model and apologized to the Chinese team, but the incident is seen as proof of the rapidly improving capabilities of homegrown LLMs by the Chinese AI community. 

One more thing

Hand-crafted squishy toys (or pressure balls) in the shape of cute animals or desserts have become the latest viral products on Chinese social media. Made in small quantities and sold in limited batches, some of them go for up to $200 per toy on secondhand marketplaces. I mean, they are cute for sure, but I’m afraid the idea of spending $200 on a pressure ball only increases my anxiety.

On a table in his lab at the University of Pennsylvania, physicist Sam Dillavou has connected an array of breadboards via a web of brightly colored wires. The setup looks like a DIY home electronics project—and not a particularly elegant one. But this unassuming assembly, which contains 32 variable resistors, can learn to sort data like a machine-learning model.

While its current capability is rudimentary, the hope is that the prototype will offer a low-power alternative to the energy-guzzling graphical processing unit (GPU) chips widely used in machine learning. 

“Each resistor is simple and kind of meaningless on its own,” says Dillavou. “But when you put them in a network, you can train them to do a variety of things.”

FELICE MACERA

A task the circuit has performed: classifying flowers by properties such as petal length and width. When given these flower measurements, the circuit could sort them into three species of iris. This kind of activity is known as a “linear” classification problem, because when the iris information is plotted on a graph, the data can be cleanly divided into the correct categories using straight lines. In practice, the researchers represented the flower measurements as voltages, which they fed as input into the circuit. The circuit then produced an output voltage, which corresponded to one of the three species. 

This is a fundamentally different way of encoding data from the approach used in GPUs, which represent information as binary 1s and 0s. In this circuit, information can take on a maximum or minimum voltage or anything in between. The circuit classified 120 irises with 95% accuracy. 

Now the team has managed to make the circuit perform a more complex problem. In a preprint currently under review, the researchers have shown that it can perform a logic operation known as XOR, in which the circuit takes in two binary numbers and determines whether the inputs are the same. This is a “nonlinear” classification task, says Dillavou, and “nonlinearities are the secret sauce behind all machine learning.” 

Their demonstrations are a walk in the park for the devices you use every day. But that’s not the point: Dillavou and his colleagues built this circuit as an exploratory effort to find better computing designs. The computing industry faces an existential challenge as it strives to deliver ever more powerful machines. Between 2012 and 2018, the computing power required for cutting-edge AI models increased 300,000-fold. Now, training a large language model takes the same amount of energy as the annual consumption of more than a hundred US homes. Dillavou hopes that his design offers an alternative, more energy-efficient approach to building faster AI.

Training in pairs

To perform its various tasks correctly, the circuitry requires training, just like contemporary machine-learning models that run on conventional computing chips. ChatGPT, for example, learned to generate human-sounding text after being shown many instances of real human text; the circuit learned to predict which measurements corresponded to which type of iris after being shown flower measurements labeled with their species. 

Training the device involves using a second, identical circuit to “instruct” the first device. Both circuits start with the same resistance values for each of their 32 variable resistors. Dillavou feeds both circuits the same inputs—a voltage corresponding to, say, petal width—and adjusts the output voltage of the second circuit to correspond to the correct species. The first circuit receives feedback from that second circuit, and both circuits adjust their resistances so they converge on the same values. The cycle starts again with a new input, until the circuits have settled on a set of resistance levels that produce the correct output for the training examples. In essence, the team trains the device via a method known as supervised learning, where an AI model learns from labeled data to predict the labels for new examples.

It can help, Dillavou says, to think of the electric current in the circuit as water flowing through a network of pipes. The equations governing fluid flow are analogous to those governing electron flow and voltage. Voltage corresponds to fluid pressure, while electrical resistance corresponds to the pipe diameter. During training, the different “pipes” in the network adjust their diameter in various parts of the network in order to achieve the desired output pressure. In fact, early on, the team considered building the circuit out of water pipes rather than electronics. 

For Dillavou, one fascinating aspect of the circuit is what he calls its “emergent learning.” In a human, “every neuron is doing its own thing,” he says. “And then as an emergent phenomenon, you learn. You have behaviors. You ride a bike.” It’s similar in the circuit. Each resistor adjusts itself according to a simple rule, but collectively they “find” the answer to a more complicated question without any explicit instructions. 

A potential energy advantage

Dillavou’s prototype qualifies as a type of analog computer—one that encodes information along a continuum of values instead of the discrete 1s and 0s used in digital circuitry. The first computers were analog, but their digital counterparts superseded them after engineers developed fabrication techniques to squeeze more transistors onto digital chips to boost their speed. Still, experts have long known that as they increase in computational power, analog computers offer better energy efficiency than digital computers, says Aatmesh Shrivastava, an electrical engineer at Northeastern University. “The power efficiency benefits are not up for debate,” he says. However, he adds, analog signals are much noisier than digital ones, which make them ill suited for any computing tasks that require high precision.

In practice, Dillavou’s circuit hasn’t yet surpassed digital chips in energy efficiency. His team estimates that their design uses about 5 to 20 picojoules per resistor to generate a single output, where each resistor represents a single parameter in a neural network. Dillavou says this is about a tenth as efficient as state-of-the-art AI chips. But he says that the promise of the analog approach lies in scaling the circuit up, to increase its number of resistors and thus its computing power.

He explains the potential energy savings this way: Digital chips like GPUs expend energy per operation, so making a chip that can perform more operations per second just means a chip that uses more energy per second. In contrast, the energy usage of his analog computer is based on how long it is on. Should they make their computer twice as fast, it would also become twice as energy efficient. 

Dillavou’s circuit is also a type of neuromorphic computer, meaning one inspired by the brain. Like other neuromorphic schemes, the researchers’ circuitry doesn’t operate according to top-down instruction the way a conventional computer does. Instead, the resistors adjust their values in response to external feedback in a bottom-up approach, similar to how neurons respond to stimuli. In addition, the device does not have a dedicated component for memory. This could offer another energy efficiency advantage, since a conventional computer expends a significant amount of energy shuttling data between processor and memory. 

While researchers have already built a variety of neuromorphic machines based on different materials and designs, the most technologically mature designs are built on semiconducting chips. One example is Intel’s neuromorphic computer Loihi 2, to which the company began providing access for government, academic, and industry researchers in 2021. DeepSouth, a chip-based neuromorphic machine at Western Sydney University that is designed to be able to simulate the synapses of the human brain at scale, is scheduled to come online this year.

The machine-learning industry has shown interest in chip-based neuromorphic computing as well, with a San Francisco–based startup called Rain Neuromorphics raising $25 million in February. However, researchers still haven’t found a commercial application where neuromorphic computing definitively demonstrates an advantage over conventional computers. In the meantime, researchers like Dillavou’s team are putting forth new schemes to push the field forward. A few people in industry have expressed interest in his circuit. “People are most interested in the energy efficiency angle,” says Dillavou. 

But their design is still a prototype, with its energy savings unconfirmed. For their demonstrations, the team kept the circuit on breadboards because it’s “the easiest to work with and the quickest to change things,” says Dillavou, but the format suffers from all sorts of inefficiencies. They are testing their device on printed circuit boards to improve its energy efficiency, and they plan to scale up the design so it can perform more complicated tasks. It remains to be seen whether their clever idea can take hold out of the lab.

Incorporating DevSecOps into CNAPP strategies can improve the way organizations develop and secure their applications.

The post The Role of DevSecOps in Enhancing CNAPP Efficiency appeared first on Security Boulevard.

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

Greetings from Switzerland! I’ve just come back from Geneva, which last week hosted the UN’s AI for Good Summit, organized by the International Telecommunication Union. The summit’s big focus was how AI can be used to meet the UN’s Sustainable Development Goals, such as eradicating poverty and hunger, achieving gender equality, promoting clean energy and climate action and so on. 

The conference featured lots of robots (including one that dispenses wine), but what I liked most of all was how it managed to convene people working in AI from around the globe, featuring speakers from China, the Middle East, and Africa too, such as Pelonomi Moiloa, the CEO of Lelapa AI, a startup building AI for African languages. AI can be very US-centric and male dominated, and any effort to make the conversation more global and diverse is laudable. 

But honestly, I didn’t leave the conference feeling confident AI was going to play a meaningful role in advancing any of the UN goals. In fact, the most interesting speeches were about how AI is doing the opposite. Sage Lenier, a climate activist, talked about how we must not let AI accelerate environmental destruction. Tristan Harris, the cofounder of the Center for Humane Technology, gave a compelling talk connecting the dots between our addiction to social media, the tech sector’s financial incentives, and our failure to learn from previous tech booms. And there are still deeply ingrained gender biases in tech, Mia Shah-Dand, the founder of Women in AI Ethics, reminded us. 

So while the conference itself was about using AI for “good,” I would have liked to see more talk about how increased transparency, accountability, and inclusion could make AI itself good from development to deployment.

We now know that generating one image with generative AI uses as much energy as charging a smartphone. I would have liked more honest conversations about how to make the technology more sustainable itself in order to meet climate goals. And it felt jarring to hear discussions about how AI can be used to help reduce inequalities when we know that so many of the AI systems we use are built on the backs of human content moderators in the Global South who sift through traumatizing content while being paid peanuts. 

Making the case for the “tremendous benefit” of AI was OpenAI’s CEO Sam Altman, the star speaker of the summit. Altman was interviewed remotely by Nicholas Thompson, the CEO of the Atlantic, which has incidentally just announced a deal for OpenAI to share its content to train new AI models. OpenAI is the company that instigated the current AI boom, and it would have been a great opportunity to ask him about all these issues. Instead, the two had a relatively vague, high-level discussion about safety, leaving the audience none the wiser about what exactly OpenAI is doing to make their systems safer. It seemed they were simply supposed to take Altman’s word for it. 

Altman’s talk came a week or so after Helen Toner, a researcher at the Georgetown Center for Security and Emerging Technology and a former OpenAI board member, said in an interview that the board found out about the launch of ChatGPT through Twitter, and that Altman had on multiple occasions given the board inaccurate information about the company’s formal safety processes. She has also argued that it is a bad idea to let AI firms govern themselves, because the immense profit incentives will always win. (Altman said he “disagree[s] with her recollection of events.”) 

When Thompson asked Altman what the first good thing to come out of generative AI will be, Altman mentioned productivity, citing examples such as software developers who can use AI tools to do their work much faster. “We’ll see different industries become much more productive than they used to be because they can use these tools. And that will have a positive impact on everything,” he said. I think the jury is still out on that one. 

Now read the rest of The Algorithm

Deeper Learning

Why Google’s AI Overviews gets things wrong

Google’s new feature, called AI Overviews, provides brief, AI-generated summaries highlighting key information and links on top of search results. Unfortunately, within days of AI Overviews’ release in the US, users were sharing examples of responses that were strange at best. It suggested that users add glue to pizza or eat at least one small rock a day.

MIT Technology Review explains: In order to understand why AI-powered search engines get things wrong, we need to look at how they work. The models that power them simply predict the next word (or token) in a sequence, which makes them appear fluent but also leaves them prone to making things up. They have no ground truth to rely on, but instead choose each word purely on the basis of a statistical calculation. Worst of all? There’s probably no way to fix things. That’s why you shouldn’t trust AI search engines. Read more from Rhiannon Williams here. 

Bits and Bytes

OpenAI’s latest blunder shows the challenges facing Chinese AI models
OpenAI’s GPT-4o data set is polluted by Chinese spam websites. But this problem is indicative of a much wider issue for those building Chinese AI services: finding the high-quality data sets they need to be trained on is tricky, because of the way China’s internet functions. (MIT Technology Review) 

Five ways criminals are using AI
Artificial intelligence has brought a big boost in productivity—to the criminal underworld. Generative AI has made phishing, scamming, and doxxing easier than ever. (MIT Technology Review)

OpenAI is rebooting its robotics team
After disbanding its robotics team in 2020, the company is trying again. The resurrection is in part thanks to rapid advancements in robotics brought by generative AI. (Forbes) 

OpenAI found Russian and Chinese groups using its tech for propaganda campaigns
OpenAI said that it caught, and removed, groups from Russia, China, Iran, and Israel that were using its technology to try to influence political discourse around the world. But this is likely just the tip of the iceberg when it comes to how AI is being used to affect this year’s record-breaking number of elections. (The Washington Post) 

Inside Anthropic, the AI company betting that safety can be a winning strategy
The AI lab Anthropic, creator of the Claude model, was started by former OpenAI employees who resigned over “trust issues.” This profile is an interesting peek inside one of OpenAI’s competitors, showing how the ideology behind AI safety and effective altruism is guiding business decisions. (Time) 

AI-directed drones could help find lost hikers faster
Drones are already used for search and rescue, but planning their search paths is more art than science. AI could change that. (MIT Technology Review)