Ted Chiang on Insight, and the Corporate Lack Thereof "The fears of superintelligent AI are probably genuine on the part of the doomsayers. That doesn't mean they reflect a real threat; what they reflect is the inability of technologists to conceive of moderation as a virtue."

Using bullshit as a term of art (as defined by Harry G. Frankfurt), ChatGPT and its various LLM cohort can best be described as bullshit machines.

Abstract: Recently, there has been considerable interest in large language models: machine learning systems which produce human-like text and dialogue. Applications of these systems have been plagued by persistent inaccuracies in their output; these are often called "AI hallucinations". We argue that these falsehoods, and the overall activity of large language models, is better understood as bullshit in the sense explored by Frankfurt (On Bullshit, Princeton, 2005): the models are in an important way indifferent to the truth of their outputs. We distinguish two ways in which the models can be said to be bullshitters, and argue that they clearly meet at least one of these definitions. We further argue that describing AI misrepresentations as bullshit is both a more useful and more accurate way of predicting and discussing the behaviour of these systems. And this bullshit might just be 'good enough' to give bosses even more leverage against workers.

People are flipping out over Leopold Aschenbrenner's gargantuan look at the current and future state of AI in Situational Awareness [PDF]. Is it the start of the world? Is it the end of the world? When? 2027! Summary by ChatGPT.

Maybe you can see the future first in Dallas? [Previously] Or is the future no more than what the markets will pay for?