Demis Hassabis

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And Meta right now are not at the frontier. Maybe they'll manage to get back on there and it's probably rational what they're doing from their perspective because they're behind and they need to do something. But I think there's more important things than just money.

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So my betting is that it's mostly it is just classical computing that's going on in the brain, which suggests that all the phenomena are modelable or mimicable by a classical computer.

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Well, look, I don't have a P-Doom number. The reason I don't is because I think it would imply a level of precision that is not there. So I don't know how people are getting their P-Doom numbers. I think it's a little bit of ridiculous notion because what I would say is it's definitely non-zero and it's probably non-negligible.

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Including things like universal basic provision or something like that where a lot of the increased productivity gets shared out and distributed to society and maybe in the form of services and other things where if you want more than that, you still go and get some incredibly rare skills and things like that and make yourself unique. But there's a basic provision that is provided.

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And the thing that's going to be harder to deal with this time around is that I think what we're going to see is something like probably 10 times the impact the Industrial Revolution had, but 10 times faster as well. So instead of a 100 years, it takes 10 years and so that's going to make, it's like a 100X, the impact and the speed combined.

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I think fusion and solar are the two that I would bet on. Solar, I mean it's the fusion reactor in the sky of course, and I think really the problem there is batteries and transmission.

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Do you have enough data to make simulations, so that you can create more synthetic data that are from the right distribution? Obviously that's the key. So you need enough real-world data in order to be able to create those kinds of data generators, and I think that we're at that step at the moment.

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And so we don't know, I would say it's kind of 50/50 whether new things are needed or whether the scaling the existing stuff is going to be enough.

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I mean potentially that's possible. I would say I'm not sure it's even desirable because that's a kind of hard takeoff scenario.

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And maybe also make the system available to a few hundred of the world's top experts, the Terence Taos of each subject area and give them a month or two and see if they can find an obvious flaw in the system. And if they can't, then I think you can be pretty confident we have a fully general system.

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My estimate is sort of 50% chance by in the next five years, so by 2030 let's say. So I think there's a good chance that that could happen.

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So picking the right question is the hardest part of science and making the right hypothesis. And that's what today's systems definitely they can't do. So I often say it's harder to come up with a conjecture, a really good conjecture than it is to solve it.

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But it seems like you can understand it through passive observation, which is pretty surprising to me. And again, I think hints at something underlying about the nature of reality in my opinion, beyond just the cool videos that it generates.

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I think to the extent that it can predict the next frames in a coherent way, that is a form of understanding, not in the anthropomorphic version of, it's not some kind of deep philosophical understanding of what's going on, I don't think these systems have that, but they certainly have modeled enough of the dynamics, put it that way

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I think we've proven, and the AI community in general that classical systems, Turing machines can go a lot further than we previously thought. They can do things like model the structures of proteins and play go to better than world champion level. And a lot of people would've thought maybe 10, 20 years ago that was decades away, or maybe you would need some sort of quantum machines to quantum systems to be able to do things like protein folding. And so I think we haven't really even sort of scratched the surface yet of what classical systems so-called could do.

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And it sort of fits with the way I think about physics in general, which is that I think information is primary, information is the most sort of fundamental unit of the universe, more fundamental than energy and matter.

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And I think the reason that's possible is that in nature, natural systems have structure because they were subject to evolutionary processes that shape them. And if that's true, then you can maybe learn what that structure is.

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Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics.