Thanks again. My general impression is that we disagree less than it first appeared, and that our disagreements are currently bottoming out in different intuitions rather than obvious cruxes we can drill down on. Plus I’m getting tired. ;) So, I say we call it a day. To be continued later, perhaps in person, perhaps in future comment chains on future posts!
For the sake of completeness, to answer your questions though:
I don’t really know what you mean when you say that this task is “hard”. Sure, humans don’t do it very well. We also don’t do arithmetic very well, while calculators do.
By “hard” I mean something like “Difficult to get AIs to do well.” If we imagine all the tasks we can get AIs to do lined up by difficulty, there is some transformative task A which is least difficult. As the tasks we succeed at getting AIs to do get harder and harder, we must be getting closer to A. I think that getting an AI to do well on all the benchmarks we throw at it despite not being trained for any of them (but rather just trained to predict random internet text) seems like a sign that we are getting close to A. You say this is because I believe in realism about rationality; I hope not, since I don’t believe in realism about rationality. Maybe there’s a contradiction in my views then which you have pointed to, but I don’t see it yet.
I feel like this is already taken into account by the methodology by which we estimated the ratio of evolution to human design? Like, taking your example of flight, presumably evolution was not optimizing just for power-to-weight ratio, it was optimizing for a bunch of other things; nonetheless we ignore those other things when making the comparison. Similarly, in the report the estimate is that evolution is ~10x better than humans on the chosen metrics, even though evolution was not literally optimizing just for the chosen metric. Why not expect the same here?
At this point I feel the need to break things down into premise-conclusion form because I am feeling confused about how the various bits of your argument are connecting to each other. I realize this is a big ask, so don’t feel any particular pressure to do it.
I totally agree that evolution wasn’t optimizing just for power-to-weight ratio. But I never claimed that it was. I don’t think that my comparison relied on the assumption that evolution was optimizing for power-to-weight ratio. By contrast, you explicitly said “presumably evolution was also going for compute-optimal performance.” Once we reject that claim, my original point stands that it’s not clear how we should apply the scaling laws to the human brain, since the scaling laws are about compute-optimal performance, i.e. how you should trade off size and training time if all you care about is minimizing compute. Since evolution obviously cares about a lot more than that (and indeed doesn’t care about minimizing compute at all, it just cares about minimizing size and training time separately, with no particular ratio between them except that which is set by the fitness landscape) the laws aren’t directly relevant. In other words, for all we know, if the human brain was 3 OOMs smaller and had one OOM more training time it would be qualitatively superior! Or for all we know, if it had 1 OOM more synapses it would need 2 OOMs less training time to be just as capable. Or… etc. Judging by the scaling laws, it seems like the human brain has a lot more synapses than its childhood length would suggest for optimal performance, or else a lot less if you buy the idea that evolutionary history is part of its training data.
Thanks again. My general impression is that we disagree less than it first appeared, and that our disagreements are currently bottoming out in different intuitions rather than obvious cruxes we can drill down on. Plus I’m getting tired. ;) So, I say we call it a day. To be continued later, perhaps in person, perhaps in future comment chains on future posts!
For the sake of completeness, to answer your questions though:
By “hard” I mean something like “Difficult to get AIs to do well.” If we imagine all the tasks we can get AIs to do lined up by difficulty, there is some transformative task A which is least difficult. As the tasks we succeed at getting AIs to do get harder and harder, we must be getting closer to A. I think that getting an AI to do well on all the benchmarks we throw at it despite not being trained for any of them (but rather just trained to predict random internet text) seems like a sign that we are getting close to A. You say this is because I believe in realism about rationality; I hope not, since I don’t believe in realism about rationality. Maybe there’s a contradiction in my views then which you have pointed to, but I don’t see it yet.
At this point I feel the need to break things down into premise-conclusion form because I am feeling confused about how the various bits of your argument are connecting to each other. I realize this is a big ask, so don’t feel any particular pressure to do it.
I totally agree that evolution wasn’t optimizing just for power-to-weight ratio. But I never claimed that it was. I don’t think that my comparison relied on the assumption that evolution was optimizing for power-to-weight ratio. By contrast, you explicitly said “presumably evolution was also going for compute-optimal performance.” Once we reject that claim, my original point stands that it’s not clear how we should apply the scaling laws to the human brain, since the scaling laws are about compute-optimal performance, i.e. how you should trade off size and training time if all you care about is minimizing compute. Since evolution obviously cares about a lot more than that (and indeed doesn’t care about minimizing compute at all, it just cares about minimizing size and training time separately, with no particular ratio between them except that which is set by the fitness landscape) the laws aren’t directly relevant. In other words, for all we know, if the human brain was 3 OOMs smaller and had one OOM more training time it would be qualitatively superior! Or for all we know, if it had 1 OOM more synapses it would need 2 OOMs less training time to be just as capable. Or… etc. Judging by the scaling laws, it seems like the human brain has a lot more synapses than its childhood length would suggest for optimal performance, or else a lot less if you buy the idea that evolutionary history is part of its training data.