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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. cavoirom · 1791120561 · focus · HN ↗

         counting 'r' in 'raspberry' to the LLM is similar to 4-dimension space to human. Their world's unit is token, not character, although they could use indirect method such as "run code" to find out. It will stay that way until they change the fundamental of the token that the LLM can perceive characters.

         1. mdp2021 · 1791149145 · focus · HN ↗

            I hope you understand: it is a core point that systems that answer questions must have the ability to internally represent the objects they assess in a way that allows reliability. Whatever the object.

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