Simon Fischer

Wiki Contributions


I don't believe these "practical" problems ("can't try long enough") generalize enough to support your much more general initial statement. This doesn't feel like a true rejection to me, but maybe I'm misunderstanding your point.

I think I mostly agree with this, but from my perspective it hints that you're framing the problem slightly wrong. Roughly, the problem with the outsourcing-approaches is our inability to specify/verify solutions to the alignment problem, not that specifying is not in general easier than solving yourself.

(Because of the difficulty of specifying the alignment problem, I restricted myself to speculating about pivotal acts in the post linked above.)

But you don't need to be able to code to recognize that a software is slow and buggy!?

About the terrible UI part I agree a bit more, but even there one can think of relatively objective measures to check usability without being able to speak python.

In cases where outsourcing succeeds (to various degrees), I think the primary load-bearing mechanism of success in practice is usually not "it is easier to be confident that work has been done correctly than to actually do the work", at least for non-experts.

I find this statement very surprising. Isn't almost all of software development like this?
E.g., the client asks the developer for a certain feature and then clicks around the UI to check if it's implemented / works as expected.

"This is what it looks like in practice, by default, when someone tries to outsource some cognitive labor which they could not themselves perform."
This proves way too much.

I agree, I think this even proves P=NP.

Maybe a more reasonable statement would be: You can not outsource cognitive labor if you don't know how to verify the solution. But I think that's still not completely true, given that interactive proofs are a thing. (Plug: I wrote a post exploring the idea of applying interactive proofs to AI safety.)

No, that's not quite right. What you are describing is the NP-Oracle.

On the other hand, with the IP-Oracle we can (in principle, limited by the power of the prover/AI) solve all problems in the PSPACE complexity class.

Of course, PSPACE is again a class of decision problems, but using binary search it's straightforward to extract complete answers like the designs mentioned later in the article.

Your reasoning here relies on the assumption that the learning mostly takes place during the individual organisms lifetime. But I think it's widely accepted that brains are not "blank slates" at birth of the organism, but contain significant amount of information, akin to a pre-trained neural network. Thus, if we consider evolution as the training process, we might reach the opposite conclusion: Data quantity and training compute are extremely high, while parameter count (~brain size) and brain compute is restricted and selected against.

Thank you for writing about this! A minor point: I don't think aerosolizing monkeypox suspensions using a nebulizer can be counted as gain of function research, not even "at least kind of". (Or do I lack reading comprehension and misunderstood something?)

Hypothesis: If a part of the computation that you want your trained system to compute "factorizes", it might be easier to evolve a modular system for this computation. By factorization I just mean that (part of) the computation can be performed using mostly independent parts / modules.

Reasoning: Training independent parts to each perform some specific sub-calculation should be easier than training the whole system at once. E.g. training n neural networks of size N/n should be easier (in terms of compute or data needed) than training one of size N, given the exponential size of the parameter space.

This hypothesis might explain the appearance of modularity if the necessary initial conditions for this selective advantage to be used are regularly present.

(I've talked about this idea with Lblack already but wanted to spell it out a bit more and post it here for reference.)

Yes, this mask is more of a symbolic pic, perhaps Simon can briefly explain why he chose this one (copyright issues I think).

Yep, it's simply the first one in open domain that I found. I hope it's not too misleading; it should get the general idea across.

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