I haven’t read this in detail (hope to in the future); I only skimmed based on section headers. I think the stuff about “what kinds of projects count” and “advantages over other genres” seem to miss an important alternative, which is to build and study toy models of the phenomena we care about. This is a bit like the gridworlds stuff, but I thought the description of that work missed its potential, and didn’t provide much of an argument for why working at scale would be more valuable.
This approach (building and studying toy models) is popular in ML research, and the leaders of the field (e.g. Rich Sutton) are big proponents of it, and think it is undervalued in the current research climate. I agree. Shameless plug for my work that follows this approach: https://arxiv.org/abs/2009.09153
A relevant example would be to build toy models of “inaccessible information”, and try to devise methods of extracting that information.
This type of research fails your criteria for what “counts” with flying colors, but in my mind it seems approximately equally valuable to the kind of experiments you seem to have in mind—and much cheaper to perform!
The case in my mind for preferring to elicit and solve problems at scale rather than in toy demos (when that’s possible) is pretty broad and outside-view, but I’d nonetheless bet on it: I think a general bias toward wanting to “practice something as close to the real thing as possible” is likely to be productive. In terms of the more specific benefits I laid out in this section, I think that toy demos are less likely to have the first and second benefits (“Practical know-how and infrastructure” and “Better AI situation in the run-up to superintelligence”), and I think they may miss some ways to get the third benefit (“Discovering or verifying a long-term solution”) because some viable long-term solutions may depend on some details about how large models tend to behave.
I do agree that working with larger models is more expensive and time-consuming, and sometimes it makes sense to work in a toy environment instead, but other things being equal I think it’s more likely that demos done at scale will continue to work for superintelligent systems, so it’s exciting that this is starting to become practical.
Thanks for the response! I see the approaches as more complimentary. Again, I think this is in keeping with standard/good ML practice.
A prototypical ML paper might first describe a motivating intuition, then formalize it via a formal model and demonstrate the intuition in that model (empirically or theoretically), then finally show the effect on real data.
The problem with only doing the real data (i.e. at scale) experiments is that it can be hard to isolate the phenomena you wish to study. And so a positive result does less to confirm the motivating intuition, as there are many other factors as play that might be responsible. We’ve seen this happen rather a lot in Deep Learning and Deep RL, in part because of the focus on empirical performance over a more scientific approach.
I haven’t read this in detail (hope to in the future); I only skimmed based on section headers.
I think the stuff about “what kinds of projects count” and “advantages over other genres” seem to miss an important alternative, which is to build and study toy models of the phenomena we care about. This is a bit like the gridworlds stuff, but I thought the description of that work missed its potential, and didn’t provide much of an argument for why working at scale would be more valuable.
This approach (building and studying toy models) is popular in ML research, and the leaders of the field (e.g. Rich Sutton) are big proponents of it, and think it is undervalued in the current research climate. I agree.
Shameless plug for my work that follows this approach: https://arxiv.org/abs/2009.09153
A relevant example would be to build toy models of “inaccessible information”, and try to devise methods of extracting that information.
This type of research fails your criteria for what “counts” with flying colors, but in my mind it seems approximately equally valuable to the kind of experiments you seem to have in mind—and much cheaper to perform!
The case in my mind for preferring to elicit and solve problems at scale rather than in toy demos (when that’s possible) is pretty broad and outside-view, but I’d nonetheless bet on it: I think a general bias toward wanting to “practice something as close to the real thing as possible” is likely to be productive. In terms of the more specific benefits I laid out in this section, I think that toy demos are less likely to have the first and second benefits (“Practical know-how and infrastructure” and “Better AI situation in the run-up to superintelligence”), and I think they may miss some ways to get the third benefit (“Discovering or verifying a long-term solution”) because some viable long-term solutions may depend on some details about how large models tend to behave.
I do agree that working with larger models is more expensive and time-consuming, and sometimes it makes sense to work in a toy environment instead, but other things being equal I think it’s more likely that demos done at scale will continue to work for superintelligent systems, so it’s exciting that this is starting to become practical.
Thanks for the response!
I see the approaches as more complimentary.
Again, I think this is in keeping with standard/good ML practice.
A prototypical ML paper might first describe a motivating intuition, then formalize it via a formal model and demonstrate the intuition in that model (empirically or theoretically), then finally show the effect on real data.
The problem with only doing the real data (i.e. at scale) experiments is that it can be hard to isolate the phenomena you wish to study. And so a positive result does less to confirm the motivating intuition, as there are many other factors as play that might be responsible. We’ve seen this happen rather a lot in Deep Learning and Deep RL, in part because of the focus on empirical performance over a more scientific approach.