Pedro Domingos: commitment

Transcript context

…in in Rosanderson. Exactly. Which I'm a very strong believer in. So doesn't that contradict what I just said? Actually no, right? I I would say the following is, and you know, from physics all the way to AI with biology in the middle. The universe is basically composed of 2 things: Symmetries and spontaneous symmetry breakings. Right? God made the symmetries. The symmetries are the laws. As far as we can tell, none of these systems at any level violate the laws. Right? Those symmetries are there. I mean you can go into that, there's a lot to be said there, but essentially, you know, most people, the great majority of people may be accepting, you know, some there are some exceptions, but they believe that the laws of physics apply to everything. Like my brain abase the laws of physics, society abase the laws of physics. The problem is that the laws of physics are useless at some point in understanding, you know, even biology, let alone psychology or sociology or AI. Why are they useless? Because we have inherited from the beginning of the universe a series of sym of spontaneous symmetry breakings, right? And my brain is doing spontaneous symmetry breakings 1 after another continuously. And those sym like, those then, some of them die out, right, or become irrelevant, stay the same. But others balloon into very big things. And that's actually what evolution is, is 1 of these things after another. And once you have that, so so the the computational disability problem is that at some level, it is true that although in principle this is all predictable and reducible, in pricing it isn't. Right? But now here's here's the point, it's like, how do we handle that? Our brains know how to handle this in a way that AI doesn't. And the way they handle this is like, you predict you computationally reduce everything you can to begin with. And I'm actually I've talked with Steve, you know, at some length about this and I'm actually more much more optimistic about how much is reducible than he is. And the thing is that like, your overall universe is not reducible, but it's full of these irreducible of these reducible pieces. And in a way, evolution is a our brain is an accumulation of these reducible pieces. So you do that, you want the machine learning to discover it, you want the inference to exploit it. But then, after that you have to have no choice but to just keep gathering data and using that to inform your predictions. Right? In a way, the physics goal of like, give you the initial conditions and then I just predict like the, you know, the the Laplace's demon dream. It is a dream, but but the I think that the problem that, you know, some of the complex systems people have not realized is that we don't have to do that. Ask any, you know, engineer, any aerospace engineer using a Kalman filter. think that the problem that, you know, some of the complex systems people have not realized is that we don't have to do that. Ask any, you know, engineer, any aerospace engineer using a Kalman filter. What you do is you predict just what's gonna you know, or reinforcement learning, right, is like you wanna have a sense of where you're going, but at at every step of time, you you you you recalibrate your predictions with the new data that comes in. So you actually only need to predict things well enough to control them to make them predictable. Right? We humans are always controlling the world to make it more predictable, and this is what robots need to do as well. And this is sort of like what I'm trying to, you know, support with a language like light and so logic.…

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