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Andy X Andersen (@andyxandersen)
This is an amazingly lucid overview of the challenges faced by formalization of knowledge. It is no surprise that AI companies decided that they will focus on the much narrower task of cataloguing all useful problem-solving paradigms, and building a bot that can weakly generalize around them. Without trying to make sense of that. If there are some unifying patterns, they will bubble up in the neural nets. The industry is now working on the "reasoning" and "grounding of symbols" aspects, via ad-hoc methods such as chain-of-thought, generating runnable code, and invoking external tools as needed.
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Andy X Andersen (@andyxandersen)
This is an amazingly lucid overview of the challenges faced by formalization of knowledge. It is no surprise that AI companies decided that they will focus on the much narrower task of cataloguing all useful problem-solving paradigms, and building a bot that can weakly generalize around them. Without trying to make sense of that. If there are some unifying patterns, they will bubble up in the neural nets. The industry is now working on the "reasoning" and "grounding of symbols" aspects, via ad-hoc methods such as chain-of-thought, generating runnable code, and invoking external tools as needed.
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Andy X Andersen (@andyxandersen)
This is an amazingly lucid overview of the challenges faced by formalization of knowledge. It is no surprise that AI companies decided that they will focus on the much narrower task of cataloguing all useful problem-solving paradigms, and building a bot that can weakly generalize around them. Without trying to make sense of that. If there are some unifying patterns, they will bubble up in the neural nets. The industry is now working on the "reasoning" and "grounding of symbols" aspects, via ad-hoc methods such as chain-of-thought, generating runnable code, and invoking external tools as needed.
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