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. intended · 1791126331 · focus · HN ↗

            No?

            This is always the issues in the discussions.

            There’s the outcomes camp (objectivists?), which points at the things LLMs can do.

            Then there’s the process methods camp, which talks about what is actually going on.

            If you only care about the outcome, then the process does t matter.

            If you are talking about what is happening, what the underlying mechanics and science of it is, then the process matters.

            These models aren’t thinking. They simulate cognition well enough to do useful work in several fields and domains.

            Both are true.

            1. IshKebab · 1791131534 · focus · HN ↗

               > These models aren’t thinking.

               They are for any definition of the word that makes any kind of sense. I'm sure you have a contorted definition that magically only includes humans though...

               1. intended · 1791139161 · focus · HN ↗

                  [delayed]

                  1. IshKebab · 1791140631 · focus · HN ↗

                     Tbh it doesn't even matter if humans turn out to have a soul, or quantum microtubules or whatever other magic LLMs can't have.

                     The normal definition of the word "thinking" definitely includes what LLMs do. Hell people used to say computers were thinking even before AI. It's super weird to get all uppity about the semantics of the word now.

                     1. intended · 1791173469 · focus · HN ↗

                        [delayed]

                        1. sampullman · 1791174915 · focus · HN ↗

                           Do we have to invent a new word, then? Thinking seems close enough, and I don't see how it's useful to quibble about semantics in this particular case.

                           Language changes over time anyway, so even if you really believe what LLMs are doing isn't the "thinking" of 2024, it probably will be the "thinking" of 2027, because most people are using it that way.

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