(I forgot that more conversation might happen on a LW crosspost, and I again lament that the internet has yet to develop a unified routing system for same-content-different-edition discourse. Copied comment from a few days ago on substack:)
I really appreciate this (and other recent) transparency. This is much improved since AI 2027.
One area I get confused by (same with Davidson, with whom I’ve discussed this a bit) is ‘research taste’. When you say things like ‘better at research taste’, and when I look at your model diagram, it seems you’re thinking of taste as a generic competence. But what is taste? It’s nothing but a partially-generalising learned heuristic model of experiment value-of-information. (Said another way, it’s a heuristic value function for the ‘achieve insight’ objective of research).
How do you get such learned models? No other way than by experimental throughput and observation thereof (direct or indirect: can include textbooks or notes and discussions with existing experts)!
As such, taste accumulates like a stock, on the basis of experimental throughput and sample efficiency (of the individual or the team) at extracting the relevant updates to VOI model. It ‘depreciates’ as you go, because the frontier of the known moves, which moves gradually outside the generalising region of the taste heuristic (eventually getting back to naive trial and error), most saliently here with data and model scale, but also in other ways.
This makes sample efficiency (of taste accumulation) and experimental throughput extremely important, central in my view. You might think that expert interviews and reading all the textbooks ever etc provide meaningful jumpstart to the taste stock. But they certainly don’t help with the flow. So then you need to know how fast it depreciates over the relevant regime.
(Besides pure heuristic improvements, if you think faster, you can also reason your way to somewhat better experiment design, both by naively pumping your taste heuristics for best-of-k, or by combining and iterating on designs. I think this reasoning boost falls off quite sharply, but I’m unsure. See my question on this)
(I forgot that more conversation might happen on a LW crosspost, and I again lament that the internet has yet to develop a unified routing system for same-content-different-edition discourse. Copied comment from a few days ago on substack:)
I really appreciate this (and other recent) transparency. This is much improved since AI 2027.
One area I get confused by (same with Davidson, with whom I’ve discussed this a bit) is ‘research taste’. When you say things like ‘better at research taste’, and when I look at your model diagram, it seems you’re thinking of taste as a generic competence. But what is taste? It’s nothing but a partially-generalising learned heuristic model of experiment value-of-information. (Said another way, it’s a heuristic value function for the ‘achieve insight’ objective of research).
How do you get such learned models? No other way than by experimental throughput and observation thereof (direct or indirect: can include textbooks or notes and discussions with existing experts)!
See my discussion of research and taste
As such, taste accumulates like a stock, on the basis of experimental throughput and sample efficiency (of the individual or the team) at extracting the relevant updates to VOI model. It ‘depreciates’ as you go, because the frontier of the known moves, which moves gradually outside the generalising region of the taste heuristic (eventually getting back to naive trial and error), most saliently here with data and model scale, but also in other ways.
This makes sample efficiency (of taste accumulation) and experimental throughput extremely important, central in my view. You might think that expert interviews and reading all the textbooks ever etc provide meaningful jumpstart to the taste stock. But they certainly don’t help with the flow. So then you need to know how fast it depreciates over the relevant regime.
(Besides pure heuristic improvements, if you think faster, you can also reason your way to somewhat better experiment design, both by naively pumping your taste heuristics for best-of-k, or by combining and iterating on designs. I think this reasoning boost falls off quite sharply, but I’m unsure. See my question on this)