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This is a rational argument, and I see it a lot, but it does not probe deep enough. GPT-3 use cases "fall apart at the lightest real world case" because our exp

This is a rational argument, and I see it a lot, but it does not probe deep enough. GPT-3 use cases "fall apart at the lightest real world case" because our expectations are wrong. For the vast majority of computing history, computers have been used to do things deterministically and accurately. Next-gen AI does not operate in this way. It encroaches on the human part of work where we expect and allow for people to get it wrong. GPT-3 does shockingly well at classification tasks with basically 0 training/prompting (outside of the base model). And it works for an incredibly broad set of use cases. But using QA and generation are much harder to judge because we can't say (in general) whether generated text is "correct"

zozbot234 5y ago
> with basically 0 training/prompting (outside of the base model) That's a heck of a lot of training. It might seem to work if you ask it to repeat stuff that it has seen in the base model, but there's nothing reliable about that. Most attempts to find some practical use for AI language models have basically been failures. > our expectations are wrong. For the vast majority of computing history, computers have been used to do things deterministically and accurately. Actually, accuracy matters even when dealing with non-deterministic, statistical/random/sampled data. A lot of supposed 'AI' is little more than a glorified toy or party trick, founded on ad-hoc data mining rather than rigorous inference from a well-defined model.