Reiner Pope: evaluation

Transcript context

…There’s this idea that Dario said on the podcast, and others have said, which is, “We don’t need continual learning for AGI, in-context learning is enough.” If you believe that, then you have to think that we have to get to a hundred-million-token context length to have an employee that is the equivalent of working with you for a month. Now, maybe that’s no longer true with sparse attention or something. But if you think that, then some ML infra thing would have to change to allow for a hundred million, like the memory bandwidth, to allow for a hundred-million-token context lengths. Sparse attention gives you a get-out for sure, because you get this square root. It gives you a big improvement. But if you look at the history of context lengths of models, from earlier models like GPT-3, maybe to GPT-4—I don’t remember when the transition happened exactly—they shot up from about 8K to 100-200K. And then for the last year or two, they’ve all been hovering around there. I think that indicates that this is the reasonably balanced cost point, and going massively beyond that would be cost-prohibitive. Not because of the compute cost, because of the memory bandwidth...…

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