Richard Korzekwa

Director at AI Impacts.

Wiki Contributions


  • Neurons' dynamics looks very different from the dynamics of bits.
  • Maybe these differences are important for some of the things brains can do.

This seems very reasonable to me, but I think it's easy to get the impression from your writing that you think it's very likely that:

  1. The differences in dynamics between neurons and bits are important for the things brains do
  2. The relevant differences will cause anything that does what brains do to be subject to the chaos-related difficulties of simulating a brain at a very low level.

I think Steven has done a good job of trying to identify a bit more specifically what it might look like for these differences in dynamics to matter. I think your case might be stronger if you had a bit more of an object level description of what, specifically, is going on in brains that's relevant to doing things like "learning rocket engineering", that's also hard to replicate in a digital computer.

(To be clear, I think this is difficult and I don't have much of an object level take on any of this, but I think I can empathize with Steven's position here)

The Trinity test was preceded by a full test with the Pu replaced by some other material. The inert test was designed to test whether they were getting the needed compression. (My impression is this was not publicly known until relatively recently)

Regardless, most definitions [of compute overhang] are not very analytically useful or decision-relevant. As of April 2023, the cost of compute for an LLM's final training run is around $40M. This is tiny relative to the value of big technology companies, around $1T. I expect compute for training models to increase dramatically in the next few years; this would cause how much more compute labs could use if they chose to to decrease.

I think this is just another way of saying there is a very large compute overhang now and it is likely to get at least somewhat smaller over the next few years.

Keep in mind that "hardware overhang" first came about when we had no idea if we would figure out how to make AGI before or after we had the compute to implement it.

Drug development is notably different because, like AI, it's a case where the thing we want to regulate is an R&D process, not just the eventual product

I agree, and I think I used "development" and "deployment" in this sort of vague way that didn't highlight this very well.

But even if we did have a good way of measuring those capabilities during training, would we want them written into regulation? Or should we have simpler and broader restrictions on what counts as good AI development practices?

I think one strength of some IRB-ish models of regulation is that you don't rely so heavily on a careful specification of the thing that's not allowed, because instead of meshing directly with all the other bureaucratic gears, it has a layer of human judgment in between. Of course, this does pass the problem to "can you have regulatory boards that know what to look for?", which has its own problems.

I put a lid on the pot because it saves energy/cooks faster. Or maybe it doesn't, I don't know, I never checked.

I checked and it does work.

Seems like the answer with pinball is to avoid the unstable processes, not control them.

Regarding the rent for sex thing: The statistics I've been able to find are all over the place, but it looks like men are much more likely to not have a proper place to sleep than women. My impression is this is caused by lots of things (I think there are more ways for a woman to be eligible for government/non-profit assistance, for example), but it does seems like evidence that women are exchanging sex for shelter anyway (either directly/explicitly or less directly, like staying in a relationship where the main thing she gets is shelter and the main thing the other person gets is sex).

Wow, thanks for doing this!

I'm very curious to know how this is received by the general public, AI researchers, people making decisions, etc. Does anyone know how to figure that out?

With the caveats that this is just my very subjective experience, I'm not sure what you mean by "moderately active" or "an athlete", and I'm probably taking your 80/20 more literally than you intended:

I agree there's a lot of improvement from that first 20% of effort (or change in habits or time or whatever), but I think it's much less than than 80% of the value. Like, say 0% effort is the 1-2 hours/week of walking I need do to get to work and buy groceries and stuff, 20% is 2-3 hours of walking + 1-2 hours at the gym or riding a bike, and 100% is 12 hours/week of structured training on a bicycle. I think 20% gets me maybe 40-50% of the benefit for doing stuff that requires thinking clearly that 100% gets me. Where the diminishing returns really kick in is around 6-8 hours/week of structured training (so 60%?), which seems to get me about 80-90% of the benefit.

That said: Anecdotally, I seem to need more intense exercise than a lot of people. Low-to-moderate intensity exercise, even in significant quantity, has a weirdly small effect on my mood and my (subjectively judged by me) cognitive ability.

Right, but being more popular than the insanely popular thing would be pretty notable (I suppose this is the intuition behind the "most important chart of the last 100 years" post), and that's not what happened.

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