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I think more importantly the lack of agency implies reasoning is impossible. You can argue our brain is also an expectation based optimizer based on gradient descent producing a most likely response to external and internal stimulus. It’s definitely lossy in its function and must be optimizing the neuronal weights at some level. But reasoning, being a seeking of the truth through method and application of conscious agency, can not be had by a model without any form of autonomous agency. The model only responds to prompts and can not do anything but what it’s determined to do by the prompt, and the prompt is extrinsic to the model. I’d note that we have already built excellent goal based agent AIs, as well as other facilities required for reasoning like inductive, deductive, and analogical reasoning. Generally we aren’t good at abductive reasoning with classical AI, but LLMs seem to do well here. That’s specifically where I think LLM fill in the reasoning gaps in AI - the ability to operate in an abstract semantic space and arrive at likely and plausible solutions even with incomplete knowledge. This also leads to hallucinations - because they are poor at tasks that require optimization, inductive and deductive reasoning, information retrieval, mechanical calculation, etc. But it’s really pretty obvious the answer is to mix the models in a feedback loop deferring to the model that most makes sense for a given problem, or some combination. Agency, logic, optimization, abstract semantic reasoning (abductive), etc - they’re all achievable with the tools we have now. It’s just a matter of figuring out the integrations. |