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. kooi · 1791148864 · focus · HN ↗

               I think where both camps get hung up is sometimes the process method group "ignores" the obvious outcomes and effectiveness of LLMs.

               But the outcomes group "ignores" the fundamental limitations of models which are purely text based.

               E.g, a baseball players trains to catch high-speed balls and they dont do it by: "ball velocity 50mph, vector:[1,2,3], run move hand command now"

               That's absurd.

               No, there is an embodied network which is "trained" on visual, tactile input, and control as direct output.

               LLMs are fundamentally not the right tool for that.

               1. mdp2021 · 1791150353 · focus · HN ↗

                  > E.g, a baseball players trains to catch high-speed balls and they dont do it by: "ball velocity 50mph, vector:[1,2,3], run move hand command now"

                  That is a NN that learns a skill.

                  But that is not an Analyst. If it were ballistics, then the answer to "how to parametrize the launch to reliably hit the target" excludes getting the result through natural skill.

                  The problem lies in the need to get "AI" facing "LLMs": the latter create a need for reliability, for "AI".

                  Speech is an endowment of both those who give educated guesses via developed skills and of those who return answers like Analysts, who check and compute. LLMs create a confusion between the two, and they will remain a problem until an ability to act as Analysts - strictly - will be implemented.

                  1. kooi · 1791168059 · focus · HN ↗

                     Not quite sure what all those words mean.

                     Dynamical systems will never be solved in a semantic domain.

                     IMO, they can be helpful the in robotics stack, from planning level up, but that's it.

                     1. mdp2021 · 1791182706 · focus · HN ↗

                        > Not quite sure what all those words mean

                        What exactly is not clear? I will rephrase.

                        The internal process of the blackbox oracle determine the reliability of the output.

                        Two abilities are very different: learning trajectories through empirical training ("increasingly catching thousands of thrown balls"), and determining trajectories through computation (a rational thinker at work). The former is a finetuned parametrized engine (a «NN that learns a skill»), the latter is an Analyst. The former is fuzzy, the second deterministic.

                        In front of fuzzy LLMs, which use potentially misleading outputs - text ("has it guessed or has it thought?") - the urgency of warranties of reliable output gets evident.

                        So, that they «simulate cognition [only] well enough» (Intended wrote), and that there are «fundamental limitations of models ... purely text based» (Kooi wrote) raises the urgency to overcome the "fuzzy" and achieve the "deterministic" - it is not that we can stall on a «fundamentally not the right tool for that».

                        Inventing an oracle calls for urgent striving to overcome the original weakness.

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