AI simulations could make social sciences more experimental

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 12:44

   Do you think that at some point, AI will create new sciences, a la Industrial Revolution and thermodynamics? Will there be something net new taught fundamentally in our education system? And if so, what would it be like? Well, I think there's several things along those lines that I think is going to happen. So first of all, the understanding and the analysis of AI systems themselves, I think, is going to become a whole science, a kind of engineering science.

2. 13:16

   These are incredibly interesting artifacts that we are building. And they're incredibly complex as well. Eventually, they'll be as complex as the human mind. the brain and so they'll need to be studied so we can understand fully way beyond where we are today how these systems work so i think there's a whole kind of field mechinterp is part of that but there's a lot more i think that we can do to analyze uh these systems so that would be a science but i think also um ai itself will maybe unlock new sciences which is maybe what you're getting at um the one i'm particularly excited about is ai for simulations so i i love simulations all my all the games i wrote not only had ai but they were simulations and i think simulations is the way we can address some of the um what we maybe think of social sciences uh like economics

3. 14:09

   um and and other more humanistic subjects because um it's very difficult to do control studies in that you know why aren't they just sciences like physics today because the problem is their emergent systems just like biology actually and it's very hard to do repeated controlled experiments you know if you're raising stress rates by half a percent you have to do it in the real world and then see what happens you can have theories but you can't run it thousands of times but if you could simulate things really accurately then maybe there's sort of new sciences to be done where you can sort of rigorously sample from a very accurate simulator and then I think that will turn, that allow us to make much better decisions in these today, very uncertain domains.

4. 14:53

   What will it take to get to those extremely accurate simulations? World models, what kind of science is necessary? and engineering. Yeah, well, look, I mean, I'm thinking a lot about that in we do a ton of that work, like learning simulators, basically what it be. So, you know, these are in domains where you can't, we don't know the mathematics of it well enough, or it's perhaps too complex. We can't just write, we can't just write a directly down a special case simulator. It's just not accurate enough, doesn't capture all the variables. We're doing that, we've done it with weather. We have the most accurate kind of weather simulator in the world, Weather Next, and it's far faster than what the meteorologists use. Can you change the weather yet? No, we can't. We can't, no. And I'm not sure that would be a good idea. But the first step is to understand it better. But then even

5. 15:41

   biology, you know, we're working on a kind of what I call a virtual cell. So a hugely dynamical emergent system. And I think biology is perfect, sort of machine learning is perfect description language for biology in the same way maths is for physics because i think in biology and in lots of these natural systems you have loads of weak signals weak correlations tons of data far too much that any the human mind can analyze but there are connections and correlations and interesting causalities within that mass of data so i think it's sort of it's always struck me that machine learning is the perfect tool uh to describe those kinds of systems where until today you know mathematics hasn't been able to do that, either because we can't manage it as top mathematicians because it's too complex, or the expressive power of math is not enough to understand

6. 16:35

   these sort of highly emergent dynamical systems. Is it also because of the messiness and stochastic nature? Yeah, sure. And I mean, eventually you could, by the way, once you learn these simulators, it may be there's another, this is maybe another branch of new branch of science, you could maybe extract some equations from once you have the simulator. So you have this sort of implicit simulator or intuitive simulator, and then maybe you could extract explicit equations from that, partly because you could also sample it as many times as you want.

7. 17:07

   As fundamental as Maxwell's or something like that. Maybe. I don't know if that exists for such emergent systems, but if they do exist, I don't see why we won't be able to find them with these methods. That would be amazing.