I would also call this one for Eliezer. I think we mostly just retrain AI systems without reusing anything. I think that’s what you’d guess on Eliezer’s model, and very surprising on Robin’s model. The extent to which we throw things away is surprising even to a very simple common-sense observer.
I would have called “Human content is unimportant” for Robin—it seems like the existing ML systems that are driving current excitement (and are closest to being useful) lean extremely heavily on imitation of human experts and mostly don’t make new knowledge themselves. So far game-playing AI has been an exception rather than the rule (and this special case was already mostly established by the time of the debate).
That said, I think it would be reasonable to postpone judgment on most of these questions since we’re not yet in the end of days (Robin thinks it’s still fairly far, and Eliezer thinks it’s close but things will change a lot by the intelligence explosion). The main ones I’d be prepared to call unambiguously already are:
Short AI timelines and very general AI architectures: obvious advantage to Eliezer.
Importance of compute, massive capital investment, and large projects selling their output to the world: obvious advantage to Robin.
These aren’t literally settled, but market odds have moved really far since the debate, and they both seem like defining features of the current world. In each case I’d say that one of the two participants was clearly super wrong and the other was basically right.
If someone succeeds in getting, say a ~13B parameter model to be equal in performance (at high-level tasks) to a previous-gen model 10x that size, using a 10x smaller FLOPs budget during training, isn’t that a pretty big win for Eliezer? That seems to be kind of what is happening: this list mostly has larger models at the top, but not uniformly so.
I’d say, it was more like, there was a large minimum amount of compute needed to make things work at all, but most of the innovation in LLMs comes from algorithmic improvements needed to make them work at all.
Hobbyists and startups can train their own models from scratch without massive capital investment, though not the absolute largest ones, and not completely for free. This capability does require massive capital expenditures by hardware manufacturers to improve the underlying compute technology sufficiently, but massive capital investments in silicon manufacturing technology are nothing new, even if they have been accelerated and directed a bit by AI in the last 15 years.
And I don’t think it would have been surprising to Eliezer (or anyone else in 2008) that if you dump more compute at some problems, you get gradually increasing performance. For example, in 2008, you could have made massive capital investments to build the largest supercomputer in the world, and gotten the best chess engine by enabling the SoTA algorithms to search 1 or 2 levels deeper in the Chess game tree. Or you could have used that money to pay for researchers to continue looking for algorithmic improvements and optimizations.
Coming in late, but the surprising thing on Yudkowsky’s models is that compute was way more important than he realized, with it usually being 50⁄50 on the most favorable models to Yudkowsky, which means compute increases are not negligible, and algorithms aren’t totally dominant.
Even granting the assumption that algorithms will increasingly be a bottleneck, and compute being less important, Yudkowsky way overrated the power of algorithms/thinking hard compared to just getting more resources/scaling.
I would also call this one for Eliezer. I think we mostly just retrain AI systems without reusing anything. I think that’s what you’d guess on Eliezer’s model, and very surprising on Robin’s model. The extent to which we throw things away is surprising even to a very simple common-sense observer.
I would have called “Human content is unimportant” for Robin—it seems like the existing ML systems that are driving current excitement (and are closest to being useful) lean extremely heavily on imitation of human experts and mostly don’t make new knowledge themselves. So far game-playing AI has been an exception rather than the rule (and this special case was already mostly established by the time of the debate).
That said, I think it would be reasonable to postpone judgment on most of these questions since we’re not yet in the end of days (Robin thinks it’s still fairly far, and Eliezer thinks it’s close but things will change a lot by the intelligence explosion). The main ones I’d be prepared to call unambiguously already are:
Short AI timelines and very general AI architectures: obvious advantage to Eliezer.
Importance of compute, massive capital investment, and large projects selling their output to the world: obvious advantage to Robin.
These aren’t literally settled, but market odds have moved really far since the debate, and they both seem like defining features of the current world. In each case I’d say that one of the two participants was clearly super wrong and the other was basically right.
If someone succeeds in getting, say a ~13B parameter model to be equal in performance (at high-level tasks) to a previous-gen model 10x that size, using a 10x smaller FLOPs budget during training, isn’t that a pretty big win for Eliezer? That seems to be kind of what is happening: this list mostly has larger models at the top, but not uniformly so.
I’d say, it was more like, there was a large minimum amount of compute needed to make things work at all, but most of the innovation in LLMs comes from algorithmic improvements needed to make them work at all.
Hobbyists and startups can train their own models from scratch without massive capital investment, though not the absolute largest ones, and not completely for free. This capability does require massive capital expenditures by hardware manufacturers to improve the underlying compute technology sufficiently, but massive capital investments in silicon manufacturing technology are nothing new, even if they have been accelerated and directed a bit by AI in the last 15 years.
And I don’t think it would have been surprising to Eliezer (or anyone else in 2008) that if you dump more compute at some problems, you get gradually increasing performance. For example, in 2008, you could have made massive capital investments to build the largest supercomputer in the world, and gotten the best chess engine by enabling the SoTA algorithms to search 1 or 2 levels deeper in the Chess game tree. Or you could have used that money to pay for researchers to continue looking for algorithmic improvements and optimizations.
Coming in late, but the surprising thing on Yudkowsky’s models is that compute was way more important than he realized, with it usually being 50⁄50 on the most favorable models to Yudkowsky, which means compute increases are not negligible, and algorithms aren’t totally dominant.
Even granting the assumption that algorithms will increasingly be a bottleneck, and compute being less important, Yudkowsky way overrated the power of algorithms/thinking hard compared to just getting more resources/scaling.