The brain evolved as a collection of highly specialized modules that learned to communicate with each other, and this same principle — collective specialized intelligences working together rather than a single general intelligence — is what we should actually be aiming for in AI, making the concept of AGI something of a misnomer.

34:00>> Collective intelligence is a bit different. We we have this plasticity, right? We can adapt our behavior day by day. We might see some kind of metalarning or some kind of change in our organization dynamics. You know, maybe some agents will specialize and it might be an existence proof of this kind of recursive, you know, super intelligence that we're talking about. >> Yeah, I do. I I I think that's

34:20absolutely correct. Right. is that you know so the specialization is great in fact I would argue that specialization is how we got all of this right and this was I'm pointing at London in case you there was some confusion there um right it was it was really about you know the interconnected highly specialized intelligences that are people and their ability to learn how to to to work

34:43together that that that you know gave rise to the technological revolution the brain is the same way right it's in my view it's highly specializ ized little modules or agents that are capable of of of of um being repurposed, reused, um capable of communicating with one another in order to solve really complicated problems. But there's always a benefit to specialization. I don't believe in like like AGI. AGI seems like a bit of a

35:09a misnomer to me. What we really want is not artificial general intelligent. We want collect we want collective specialized intelligences. >> What about scientific discovery? Do you think that we could, you know, what would the world look like when we could discover new drugs? We could discover new knowledge in science. >> You know, right now the way that we're doing that is is um largely focused on

35:30summarizing vast troves of data and looking for correlations that are present in it. Um I think the next major milestone um in this trajectory is is experimental design, right? Not just oh well here's here's some correlations you you may not have seen because they're really small and this is what computers are good at. They're really good at identifying small but highly relevant correlations. Um and uh the next step of

35:53course is design is is constructing a system that tests these hypotheses explicitly right and generates the experiments that will identify like that will they'll fill in the gaps of our knowledge and all of this I believe can in fact be automated in a very sensible way. I I you know I I don't see any like major obstacles to automating empirical inquiry other than we probably want to

36:16place some safety constraints when we start letting them work when we start letting the AIs run the labs right because you never know. So you always have this AI was like well you know the most effective experiment to determine if this is correct is to set off a nuke and that that would be bad. >> Yes. >> Right. So pure empirical inquiry right does run risks like that but I think