Thread

1. stratos123 · 1791107241 · focus · HN ↗

   LeCun also said back in 2022 that "if you train a machine, as powerful as it could be, your 'GPT-5000', on text", it will never be able to learn basic common-sense physics like that objects placed on tables will move along with them.

   1. mdp2021 · 1791117069 · focus · HN ↗

      > never be able to learn basic common-sense physics

      And has it at this stage, within in-depth take of said "learning", foundationally?

      I have not been able to properly check the studies for a long time now, but I remain unaware of achieved solutions on the problem of reliably referencing a world model out of a language model - that "counting the 'r's in 'raspberry'" be not guessing, not memory, but actually counting.

      1. Version467 · 1791121712 · focus · HN ↗

         LeCun's argument wasn't about the definition of learning though. He stated that they would never get these common sense things correct because they weren't sufficiently part of the training data. A statement that we can hopefully all agree has been thoroughly refuted.

         1. bonzini · 1791123352 · focus · HN ↗

            As of a few months ago they still have trouble, with low thinking, at the "should I drive to a car wash that is 100 m away" kind of question.

            1. keeda · 1791136205 · focus · HN ↗

               Simply appending “check your assumptions” to the question fixed it even back then: <a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=47040530">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=47040530

               Similarly for Apple’s “red herring” paper, simply adding a generic caveat to “disregard irrelevant factors” (without specifying which ones) restored performance even in the weaker local llama models back then.

               The flaw was not in the reasoning; the flaw seems to be simply that the assumptions we make are often different from the assumptions it makes. I wonder if that might be a fundamental underlying cause of misalignment.

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