In my framework, this is basically an argument that algorithmic-improvement-juice can be translated into a large improvement in AI R&D labor production via the mechanism of greatly increasing the productivity per “token” (or unit of thinking compute or whatever). See my breakdown here where I try to convert from historical algorithmic improvement to making AIs better at producing AI R&D research.
Your argument is basically that this taste mechanism might have higher returns than reducing cost to run more copies.
I agree this sort of argument means that returns to algorithmic improvement on AI R&D labor production might be bigger than you would otherwise think. This is both because this mechanism might be more promising than other mechanisms and even if it is somewhat less promising, diverse approaches make returns dimish less aggressively. (In my model, this means that best guess conversion might be more like algo_improvement1.3 rather than algo_improvement1.0.)
I think it might be somewhat tricky to train AIs to have very good research taste, but this doesn’t seem that hard via training them on various prediction objectives.
At a more basic level, I expect that training AIs to predict the results of experiments and then running experiments based on value of information as estimated partially based on these predictions (and skipping experiments with certain results and more generally using these predictions to figure out what to do) seems pretty promising. It’s really hard to train humans to predict the results of tens of thousands of experiments (both small and large), but this is relatively clean outcomes based feedback for AIs.
I don’t really have a strong inside view on how much the “AI R&D research taste” mechanism increases the returns to algorithmic progress.
In my framework, this is basically an argument that algorithmic-improvement-juice can be translated into a large improvement in AI R&D labor production via the mechanism of greatly increasing the productivity per “token” (or unit of thinking compute or whatever). See my breakdown here where I try to convert from historical algorithmic improvement to making AIs better at producing AI R&D research.
Your argument is basically that this taste mechanism might have higher returns than reducing cost to run more copies.
I agree this sort of argument means that returns to algorithmic improvement on AI R&D labor production might be bigger than you would otherwise think. This is both because this mechanism might be more promising than other mechanisms and even if it is somewhat less promising, diverse approaches make returns dimish less aggressively. (In my model, this means that best guess conversion might be more like algo_improvement1.3 rather than algo_improvement1.0.)
I think it might be somewhat tricky to train AIs to have very good research taste, but this doesn’t seem that hard via training them on various prediction objectives.
At a more basic level, I expect that training AIs to predict the results of experiments and then running experiments based on value of information as estimated partially based on these predictions (and skipping experiments with certain results and more generally using these predictions to figure out what to do) seems pretty promising. It’s really hard to train humans to predict the results of tens of thousands of experiments (both small and large), but this is relatively clean outcomes based feedback for AIs.
I don’t really have a strong inside view on how much the “AI R&D research taste” mechanism increases the returns to algorithmic progress.