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Carl Shulman: evaluation

14 Jun 2023 Dwarkesh Podcast Carl Shulman (Pt 1) — Intelligence explosion, primate evolution, robot doublings, & alignment

“Some people have an intuition that what matters is time, that it's not how many people working on a problem at a given point.”

— Carl Shulman

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Speaker
Carl Shulman
Attribution
Verified speaker
Claim type
evaluation
Recorded
14 Jun 2023
Publisher
Dwarkesh Podcast

Transcript context

…Maybe this is a good way to describe what happens when more humans enter a field but does it even make sense to say that a greater population of AIs is doing AI research if there's like more GPUs running a copy of GPT-6 doing AI research. How applicable are these economic models of the quantity of humans working on a problem to the magnitude of AIs working on a problem? If you have AIs that are directly automating particular jobs that humans were doing before then we say, well with additional compute we can run more copies of them to do more of those tasks simultaneously. We can also run them at greater speed. Some people have an intuition that what matters is time, that it's not how many people working on a problem at a given point. I think that doesn't bear out super well but AI can also run faster than humans. If you have a set of AIs that can do the work of the individual human researchers and run at 10 times or 100 times the speed. And we ask well, could the human research community have solved these algorithm problems, do things like invent transformers over 100 years, if we have AIs with a population effective population similar to the humans but running 100 times as fast and so. You have to tell a story where no, the AI can't really do the same things as the humans and we're talking about what happens when the AIs are more capable of in fact doing that. Although they become more capable as lesser capable versions of themselves help us make themselves more capable, right? You have to kickstart that at some point. Is there an example in analogous situations? Is intelligence unique in the sense that you have a feedback loop of — with a learning curve or something else, a system’s outputs are feeding into its own inputs. Because if we're talking about something like Moore's law or the cost of solar, you do have this way where we're throwing more people with the problem and we're making a lot of progress, but we don't have this additional part of the model where Moore's law leads to more humans somehow and more humans are becoming researchers.…

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