| >people misunderstand
and anthropomorphize LLMs I agree but there is a certain "god of the gaps" process that's happening here. To me what's been achieved so far is encoding text into natural (and programming) languages. These models can't reason and e.g., can't pass a Turing test. For example, if you play with GPT-3 or ChatGPT enough you can definitely get it to fail on a task a human would succeed at. When models so far have achieved "reasoning" behavior, usually it's in conjunction with a classical algorithm -- for example AlphaZero used a model to evaluate chess positions but then ran a tree search algorithm to decide what move to play. I think if LLMs can be combined effectively with some kind of computation process like in AlphaZero or some kind of "chains of thought" [1], then we might see them do things we'd truly call "reasoning". Some "gaps" have been (1) "(encoding into) images is hard" (2) "(encoding into) natural language is hard" and now (3) "'reasoning' is hard" and afterwards (4) "subjective experience is hard". Whether to be optimistic or pessimistic about AI depends on the final "gap" -- whether there is an irreplicable/noncomputable/etc. property of human consciousness other than qualia. That is, if "understanding" is hard (e.g., via some voodoo like [2]). If not humans will become obsolete, so I hope the answer is yes. [1]: https://ai.googleblog.com/2022/05/language-models-perform-re... [2]: https://en.m.wikipedia.org/wiki/Orchestrated_objective_reduc... |