Sure. This I think was the more informed objection to LLM capabilities. They are “just” filling in text and can’t know anything humans don’t. I mean it turns out this can likely mean 0.1 percent human capability in EVERY domain at the same time is doable, but lack the architecture to directly learn beyond human ability.
(Which isn’t true, if they embark on tasks on their own and learn from the I/O, such as solving every coding problem published or randomly generating software requirements and then tests and then code to satisfy the tests, they could easily exceed ability at that domain than all living humans)
I mistakenly thought they would be limited to median human performance.
Yep, I broadly agree. But this would also apply to multiplying matrices.
Sure. This I think was the more informed objection to LLM capabilities. They are “just” filling in text and can’t know anything humans don’t. I mean it turns out this can likely mean 0.1 percent human capability in EVERY domain at the same time is doable, but lack the architecture to directly learn beyond human ability.
(Which isn’t true, if they embark on tasks on their own and learn from the I/O, such as solving every coding problem published or randomly generating software requirements and then tests and then code to satisfy the tests, they could easily exceed ability at that domain than all living humans)
I mistakenly thought they would be limited to median human performance.
Yep, the problem is that the internet isn’t written by Humans, so much as written by Humans + The Universe. Therefore, GPT-N isn’t bounded by human capabilities.
Thanks. Interestingly this model explains why:
It can play a few moves of chess from common positions—it’s worth the weights to remember those
It can replicate the terminal text for many basic Linux commands—it’s worth the weights for that also.