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by fnordpiglet
993 days ago
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Oh but it can’t, but with a sufficiently complex vector space it can seem like it can. What seems like extrapolation is an interpolation in the semantic vector space, particularly in the transformer / attention model. This is a key difference between human intelligence and current AI, it’s not able to “create” and see beyond what it’s been trained on. Any approximation of that is simply indicative of a very complete training set, and it is sufficiently powerful enough to fool people with its expectation based inference - but when you dig into the details of cutting edge stuff you’re an expert in and ask it conceptual questions that extend beyond the semantic corpus embedded in its vector space, it will hallucinate, or if well fine tuned admit lack of knowledge, because the best it can do is interpolate within its own semantic vector space. But listen, I’m a big buyer of generative AI, what it does is incredible. But it’s useful to not ascribe more power to a tool than the math allows. And there are very few machine learning algorithms that do extrapolation at all with any precision. Generally they project an expectation,often of some complex highly dimensional non linear system, which is amazing, but when they are confronted with a novel input pattern they are thrown off. The issue is they’re at their core probabilistic systems, and if the data experiences a regime change that’s unexpected the model will misbehave and output garbage. |
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