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- cross-posted to:
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Google apologizes for ‘missing the mark’ after Gemini generated racially diverse Nazis::Google says it’s aware of historically inaccurate results for its Gemini AI image generator, following criticism that it depicted historically white groups as people of color.
I don’t think it’s generally true, because current AI can solve some reasoning tasks very well. But it’s definitely something where they are lacking.
It isn’t reasoning about anything. A human did the reasoning at some point, and the LLM’s dataset includes that original information. The LLM is simply matching your prompt to that training data. It’s not doing anything else. It’s not thinking about the question you asked it. It’s a glorified keyword search.
It’s obvious you have no idea how LLMs work at a fundamental level, yet you keep talking about them like you’re an expert.
So if I find a single example of an AI doing a reasoning task that’s not in its training material, would you agree that you’re wrong and AI does reason?
You won’t find one. LLMs are literally incapable of the kind of reasoning you’re talking about. All of their solutions are based on training data, no matter how “original” your problem might seem.
You didn’t answer my question.
That’s fair, I have seen AI reason at a low level, but it seems to me that it is lacking higher levels of reasoning and context
It definitely is lacking for now, but the question is: are these differences in degrees, or fundamental differences? I haven’t seen research suggesting that it’s the latter so far.