To scale this approach, one will want to have “structural regularizers” towards modularity, interoperability and parsimony
I am unsure of the formal architecture or requirements for these structural regularizers you mention. I agree with using shared building blocks to speed up development and verification. I am unsure credit assignment would work well for this, maybe in the form of “the more a block is used in a code, the more we can trust it”?
Constraints on the types of admissible model code. We have strongly advocated for probabilistic causal models expressed as probabilistic programs.
What do you mean? Why is this specifically needed? Do you mean that if we want to have a go-player, we should have one portion of the code dedicated to assigning probability to what the best move is? Or does it only apply in a different context of finding policies?
Scaling this to multiple (human or LLM) contributors will require a higher-order model economy of some sort
Hmm. Is the argument something like “We want to scale and diversify the agents who will review the code for more robustness (so not just one LLM model for instance), and that means varying level of competence that we will want to figure out and sort”? I had not thought of it that way, I was mainly thinking of just using the same model, and I’m unsure that having weaker code-reviewers will not bring the system down in terms of safety.
Regarding the Gaia Network, the idea seems interesting though I am unclear about the full details yet. I had thought of extending betting markets to a full bayesian network to have a better picture of what everyone believe, and maybe this is related to your idea. In any case, I believe that conveying one’s full model of the world through this kind of network and maybe more may be doable, and quite important to solve some sort of global coordination/truth seeking?
Overall I agree with your idea of a common library and I think there should be some very promising iterations on that. I will contact you more about colaboration ideas!
Thanks a lot for the kind comment!
I am unsure of the formal architecture or requirements for these structural regularizers you mention. I agree with using shared building blocks to speed up development and verification. I am unsure credit assignment would work well for this, maybe in the form of “the more a block is used in a code, the more we can trust it”?
What do you mean? Why is this specifically needed? Do you mean that if we want to have a go-player, we should have one portion of the code dedicated to assigning probability to what the best move is? Or does it only apply in a different context of finding policies?
Hmm. Is the argument something like “We want to scale and diversify the agents who will review the code for more robustness (so not just one LLM model for instance), and that means varying level of competence that we will want to figure out and sort”? I had not thought of it that way, I was mainly thinking of just using the same model, and I’m unsure that having weaker code-reviewers will not bring the system down in terms of safety.
Regarding the Gaia Network, the idea seems interesting though I am unclear about the full details yet. I had thought of extending betting markets to a full bayesian network to have a better picture of what everyone believe, and maybe this is related to your idea. In any case, I believe that conveying one’s full model of the world through this kind of network and maybe more may be doable, and quite important to solve some sort of global coordination/truth seeking?
Overall I agree with your idea of a common library and I think there should be some very promising iterations on that. I will contact you more about colaboration ideas!