Currently a MATS scholar working with Vanessa Kosoy on the learning-theoretic agenda for AI Alignment.
arjunpi
Karma: 16
Yes, thanks!
representation of a variable for variable
Hm, I don’t understand what is supposed to be here.
Isn’t it the case that when you sing a high note, you feel something higher in your mouth/larynx/whatever , and when you sing a low note, you feel something lower? Seems difficult to tell whether I actually do need to do that or I’ve just conditioned myself to, because of the metaphor.
If you’re reading the text in a two-dimensional visual display, you are giving yourself an advantage over the LLM. You should actually be reading it in a one-dimensional format with new-line symbols.
(disclosure, I only skimmed your COT for like a few seconds)
the real odds would be less about the ELO and more on whether he was drunk while playing me
not sure if that would help :)
I don’t think this will work because we are already using subscripts to denote which environment’s list we are referring to
I’m not. I guess this is the part that makes it confusing
for readability we define and to be the accessible and outer action spaces of respectively
Do you have a suggestion for alternate notation? I use this because we often need to refer to the action space corresponding to a state. I think this would be needed even with the language framing.
(I also assigned to make it more readable)
Do you have any predictions about the first year when AI assistance will give a 2x/10x/100x factor “productivity boost” to AI research?
Hey, I’ve been reading stuff from this community since about 2017. I’m now in the SERI MATS program where I’m working with Vanessa Kosoy. Looking forward to contributing something back after lurking for so long :P
Epistemic status: Quick dump of something that might be useful to someone. o3 and Opus 4 independently agree on the numerical calculations for the bolded result below, but I didn’t check the calculations myself in any detail.
Let Y∼Ber(p). With probability r, set Z:=X, and otherwise draw Z∼Ber(p). Let Y∼Ber(1/2). Let A=X⊕Y and B=Y⊕Z. We will investigate latents for (A,B).
Set Λ:=Y, then note that the stochastic error ϵ:=I(A;Y|B)) because Y induces perfect conditional independence and symmetry of A and B. Now compute the deterministic errors of Λ:=Y, Λ:=0, Λ:=A, which are equal to H(Y∣A),I(A;B),H(A|B) respectively.
Then it turns out that with p:=0.9,r:=0.44, all of these latents have error greater than 5ϵ, if you believe this claude opus 4 artifact (full chat here, corroboration by o3 here). Conditional on there not being some other kind of latent that gets better deterministic error, and the calculations being correct, I would expect that a bit more fiddling around could produce much better bounds, say 10ϵ or more, since I think I’ve explored very little of the search space.
e.g. one could create more As and Bs by either adding more Ys, or more Xs and Zs. Or one could pick the probabilities p,r out of some discrete set of possibilities instead of having them be fixed.