There are some posts with perennial value, and some which depend heavily on their surrounding context. This post is of the latter type. I think it was pretty worthwhile in its day (and in particular, the analogy between GPT upgrades and developmental stages is one I still find interesting), but I leave it to you whether the book should include time capsules like this.
I suspect that you cannot get this out of small large amounts of gradient descent on small large layered transformers, and therefore I suspect that GPT-N does not approach superintelligence before the world is ended by systems that look differently, but I could be wrong about that.
I unpack this as the claim that someone will always be working on directly goal-oriented AI development, and that inner optimizers in an only-indirectly-goal-oriented architecture like GPT-N will take enough hardware that someone else will have already built an outer optimizer by the time it happens.
That sounds reasonable, it’s a consideration I’d missed at the time, and I’m sure that OpenAI-sized amounts of money will be paid into more goal-oriented natural language projects adapted to whatever paradigm is prominent at the time. But I still agree with Eliezer’s “but I could be wrong” here.
There are some posts with perennial value, and some which depend heavily on their surrounding context. This post is of the latter type. I think it was pretty worthwhile in its day (and in particular, the analogy between GPT upgrades and developmental stages is one I still find interesting), but I leave it to you whether the book should include time capsules like this.
It’s also worth noting that, in the recent discussions, Eliezer has pointed to the GPT architecture as an example that scaling up has worked better than expected, but he diverges from the thesis of this post on a practical level:
I unpack this as the claim that someone will always be working on directly goal-oriented AI development, and that inner optimizers in an only-indirectly-goal-oriented architecture like GPT-N will take enough hardware that someone else will have already built an outer optimizer by the time it happens.
That sounds reasonable, it’s a consideration I’d missed at the time, and I’m sure that OpenAI-sized amounts of money will be paid into more goal-oriented natural language projects adapted to whatever paradigm is prominent at the time. But I still agree with Eliezer’s “but I could be wrong” here.