Gary Marcus

Rank 13 of 47
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Score 83
5/17/2025, 11:25:53 PM
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In reply to:
Daniel Litt
@littmath
·
238d
@GaryMarcus @colin_fraser @MelMitchell1 @rao2z Let me put it this way—they can very reliably solve some out-of-distribution hard problems that humans can’t reliably solve. Being able to solve *all* problems is not a pre-condition for intelligence; otherwise people aren’t intelligent.
Gary Marcus
@GaryMarcus
·
238d
@littmath @colin_fraser @MelMitchell1 @rao2z Some, but not reliably. They can’t even solve large multiplication problems reliably.

LLMs exceed their predecessors but demonstrably still have not solved this problem, which is most clear when the training set is known, and when direct data augmentation is not possible.

Some
Daniel Litt
@littmath
·
238d
@GaryMarcus @colin_fraser @MelMitchell1 @rao2z They’re clearly generalizing out of distribution. E.g. they can solve some novel math problems.
Gary Marcus
@GaryMarcus
·
238d
@littmath New situations is clearly the weakness in these approaches, and has been forever.

@colin_fraser has documented this endlessly, informally; I gave the intuitions behind that in 1998. The recent Apple reasoning paper, the studies of @MelMitchell1 and @rao2z etc reinforce it.

The
Daniel Litt
@littmath
·
238d
@GaryMarcus These seem fine.
Gary Marcus
@GaryMarcus
·
238d
@littmath Under some definitions of intelligence, maybe. What’s yours?

The statement is part of a discussion on the capabilities of AI in solving problems, particularly out-of-distribution problems. The tone is defensive, aiming to clarify a previous statement and prevent misinterpretation. The intent is to engage in a technical debate about AI capabilities without misrepresentation.

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