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Speaker 1: Hey, am I speaking to the real Joorge today?

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Speaker 2: Who else could it be?

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Speaker 1: I don't know. It could be the simulated Jorge or

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Speaker 1: an AI generated horheage GPT.

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Speaker 2: Yes, chat Jorge. Would it make a difference.

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Speaker 1: That sounds like something the simulated Jorge would say?

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Speaker 2: Mmm. I kind of wish I had simulated Horges. Then

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Speaker 2: I might avoid a lot of mistakes I make. Or

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Speaker 2: they could do all the work while I sleep in.

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Speaker 1: Why do you think simulated Hojoges are less likely to

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Speaker 1: make mistakes?

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Speaker 2: No? I mean they would do the mistakes, and then

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Speaker 2: I would learn from them. That's the idea.

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Speaker 3: Right.

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Speaker 1: That does sound useful, But I think you have to

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Speaker 1: be careful about the sim Jorges organizing and rising up

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Speaker 1: against you.

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Speaker 3: Oh do you think.

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Speaker 2: They would form their own union or a revolt?

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Speaker 1: Do you mean, yeah, either mutiny or fair wages. Either one.

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Speaker 2: Well, I could just pay them in simulated money.

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Speaker 1: I guess as long as they can use that to

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Speaker 1: feed their simulated children, I bet they'd be happy. Oh.

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Speaker 2: No, I definitely provide simulated benefits too. The whole simulated

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Speaker 2: family gets a bonus.

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Speaker 1: You're not a good employer, but you can simulate one.

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Speaker 2: Hi, I'm Jory mccartoonists and the author of Oliver's Great

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Speaker 2: Big Universe. Hi.

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Speaker 3: I'm Daniel.

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Speaker 1: I'm a particle physicist and a professor at UC Irvine,

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Speaker 1: and I am constantly simulating crazy conditions.

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Speaker 2: You mean in your life or in your work.

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Speaker 1: Well, work is a big part of my life. But yeah,

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Speaker 1: my job involves simulating collisions at very high energies all

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Speaker 1: the time.

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Speaker 2: And you also actually do them, right, You actually collide

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Speaker 2: things at the Large Hadron Collider.

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Speaker 1: That's right. We both collide particles together in real life,

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Speaker 1: and we simulate what would happen if we collided particles

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Speaker 1: under various different potential laws of the universe to see

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Speaker 1: what might happen. Will the Earth get gobbled up with

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Speaker 1: to create a black hole that destroys the Earth or not?

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Speaker 1: Let's find out.

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Speaker 2: Does that mean your whole career is a simulation or

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Speaker 2: your whole life?

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Speaker 1: You know, our computers are not fast enough to keep

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Speaker 1: up with reality. So while we generate lots and lots

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Speaker 1: of simulated collisions, the real collider has generated more collisions

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Speaker 1: than we could ever simulate.

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Speaker 2: But how do you know, Daniel, that we're not in

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Speaker 2: a simulation right now, Like you might think you're doing experiments,

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Speaker 2: but really you're just inside of a video game somewhere.

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Speaker 1: Well, I want to find the cheap goods, then, well I.

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Speaker 2: Think if you had found them by now, you probably

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Speaker 2: would have a noble price.

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Speaker 1: Right, I'm hoping smashing particles together gives me the cheek goods.

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Speaker 2: And then that opens up the real boss level.

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Speaker 1: Where I fight the simulated army of Jorges.

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Speaker 2: No, you fight the real Jegeo. That's the real boss.

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Speaker 2: But anyways, welcome to our podcast Daniel and Jorge Explain

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Speaker 2: the Universe, a production of iHeartRadio.

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Speaker 1: In which we use our tiny little minds to try

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Speaker 1: to understand the vast universe. We hope to build in

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Speaker 1: your head a simulation of sorts, one that describes the

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Speaker 1: way the real universe works out there. We hope to

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Speaker 1: encode into your brain some laws of physics that will

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Speaker 1: help you understand how the real universe out there is

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Speaker 1: smashing and bashing to create our Bonker's reality.

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Speaker 2: That's right, the universe are doing all kinds of amazing

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Speaker 2: and awe sometimes and saying things out there in reality.

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Speaker 2: And so it's our job as humans and as scientists

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Speaker 2: to understand what's going on and to ask questions, to

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Speaker 2: probe into the true answers to why things are the

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Speaker 2: way they are.

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Speaker 1: And the classical way that science does this is with

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Speaker 1: theories and experiments, hypotheses and tests. You have an idea,

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Speaker 1: you go and see out there in the universe. If

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Speaker 1: it works, you predict something happens, and you go and

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Speaker 1: check to see if it does. But the modern scientific

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Speaker 1: method has a third way, which lives sort of uncomfortably

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Speaker 1: between theory and experiments.

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Speaker 2: Oh, why is it uncomfortable? Is it like uncomfortable, awkward

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Speaker 2: or uncomfortable, like physically uncomfortable.

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Speaker 1: It's a little bit uncomfortable for those of us who

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Speaker 1: specialize in not to know where we fit into the

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Speaker 1: picture of science. Some people consider me experimental physicists. Some

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Speaker 1: people are like, nah, he mostly runs simulation, so he's

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Speaker 1: really a theorist. So you can be sort of like

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Speaker 1: uncomfortably between two different communities. If you do a lot

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Speaker 1: of simulations.

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Speaker 2: You start your own simulated community. Can you be like

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Speaker 2: a simulating physicist.

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Speaker 1: A simulator a simulate trist. That sounds not safe for work.

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Speaker 2: Actually, I don't know what you mean, but I'll take

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Speaker 2: your word for it.

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Speaker 1: You know, academic communities change pretty slowly, and for example,

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Speaker 1: in departments of physics, people tend to hire people that

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Speaker 1: are like them. The experimentalists get to hire somebody. They

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Speaker 1: want to hire somebody who's a blue blooded experimentalist down

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Speaker 1: to the core. So if you work at the intersection

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Speaker 1: of fields, you do some experiments, you do some theory,

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Speaker 1: maybe even do some computer science and machine learning, then

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Speaker 1: you don't necessarily have a home, you don't have a

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Speaker 1: tribe that's going to go to bat for you to

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Speaker 1: get hired. So it's sort of about the sociology of

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Speaker 1: science as a real practice.

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Speaker 2: Well, I think that kind of makes sense, right, Like

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Speaker 2: why hire a simulating physicist when you can just simulate one?

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Speaker 2: Why go to all the trouble?

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Speaker 1: You know, I think that's true. If we could simulate physicists,

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Speaker 1: we could get a lot more done. But we're not

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Speaker 1: quite there yet. Human physicists still have a little bit

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Speaker 1: of an edge.

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Speaker 2: Yeah, maybe till next week when chat GPT catches up

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Speaker 2: and starts doing physics.

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Speaker 1: I mean, have you ever asked chat GPT a physics question.

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Speaker 1: You don't get physics out, that's for sure.

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Speaker 2: But anyways, it is an interesting universe because I guess

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Speaker 2: sometimes there are questions. You can't just go out there

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Speaker 2: and try for yourself in the universe, right, that's right.

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Speaker 1: Simulation has emerged in the past fifty years as an

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Speaker 1: extraordinarily powerful tool as a little bit of experiment and

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Speaker 1: a little bit of theory, and it lets us answer

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Speaker 1: questions that we otherwise could not answer. It really is

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Speaker 1: a completely new tool in the science tool.

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Speaker 2: Belt, although I would argue it's maybe one of the

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Speaker 2: oldest tools in science. And so today on the podcast,

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Speaker 2: we'll be asking the question why do scientists do simulations?

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Speaker 2: Why do scientists do anything.

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Speaker 1: Other than the obvious that simulations are so much fun.

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Speaker 1: You get to build your own little universe. You are

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Speaker 1: the creator and god of that simulated universe.

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Speaker 2: Oh boy, is that that the ultimate goal? There? To

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Speaker 2: be gods? No, you know, you don't have to get

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Speaker 2: a degree for that. You could just buy some legos.

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Speaker 1: Oh, I've been doing that since I was a little kid.

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Speaker 1: I just want more and more powerful simulations.

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Speaker 2: You want more powerful legos, smaller legos.

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Speaker 1: Jokes aside, There is a real sense of power when

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Speaker 1: you create a simulated universe, because you are deciding what

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Speaker 1: the laws of physics are in that universe, what particles

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Speaker 1: do they have, how do they interact? And then you

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Speaker 1: get to see how it all plays out.

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Speaker 2: Mmmm yeah, I sort of get that. I mean I

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Speaker 2: write a lot, I create characters, and I sort of

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Speaker 2: build my own world. What's the difference.

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Speaker 1: Yeah, you could think of fiction as simulated human interaction

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Speaker 1: and lives. Right, we're exploring what it would be like

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Speaker 1: to be in those situations.

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Speaker 2: Yeah. Wait, did you just say your work.

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Speaker 1: Is simulations are definitely fiction. Sometimes they align with reality,

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Speaker 1: and one deep question is how well they align? What

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Speaker 1: lessons you can learn from your simulated fiction that carry

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Speaker 1: over into the real world.

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Speaker 2: Interesting, So your research is science fiction is what you're saying.

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Speaker 1: You know, I've always argued that there's a strong connection

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Speaker 1: between science and science fiction.

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Speaker 3: Right.

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Speaker 1: One aspect of science is like, well, what are the laws?

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Speaker 3: Could they be?

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Speaker 1: This?

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Speaker 3: Could they be?

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Speaker 2: That?

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Speaker 1: There's an element of creativity and exploration there, absolutely so. Yeah,

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Speaker 1: I'm constantly creating science fiction universes and trying to see

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Speaker 1: if they line up with ours.

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Speaker 2: And then you wonder why the other physicis don't want

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Speaker 2: to play with you.

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Speaker 1: Fortunately I got tenured before I revealed all of these

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Speaker 1: crazy instincts. That's the game.

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Speaker 2: Well, this is an interesting question, and so as usual,

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Speaker 2: we were wondering how many people out there had thought

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Speaker 2: about why scientists do the things they do, and in particular,

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Speaker 2: why they do simulations in their work.

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Speaker 1: So thanks very much to everybody who answers these questions

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Speaker 1: for this fun segment of the podcast, one of my favorites.

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Speaker 1: If you like to join the team or just answer

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Speaker 1: one or two questions right to us to questions at

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Speaker 1: Danielandhorge dot com.

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Speaker 2: So think about it for a second. If someone asks

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Speaker 2: you why scientists do simulations, what would you say.

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Speaker 4: Well, using simulations, we can observe scenarios in our models

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Speaker 4: that we can't necessarily observe in real life, and see

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Speaker 4: what can happen in certain situations like in a black

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Speaker 4: hole or when galaxies collide or something like that.

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Speaker 5: Scientists do simulations because the universe is really old, really big,

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Speaker 5: sometimes really destructive. Frankly, I'm happy they do a lot

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Speaker 5: of that modeling and simulations and don't necessarily try to

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Speaker 5: create big bang conditions on a big scale, or the

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Speaker 5: explosion of stars or something.

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Speaker 2: Couple of interesting answers did anyone look at you funny

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Speaker 2: when you ask them the question.

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Speaker 1: I don't know. These were all on the internet, so

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Speaker 1: I couldn't capture their facial expressions. Did they send an emoji?

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Speaker 1: But I do sense some relief in there that, for example,

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Speaker 1: we are trying to simulate galaxy collisions rather than trying

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Speaker 1: to arrange galaxy collisions.

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Speaker 2: Well, if we could do that, that'd be pretty cool.

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Speaker 2: I mean, not for those galaxies, but just to have

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Speaker 2: that power.

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Speaker 1: Yeah, you'd have to have like sign offs from every

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Speaker 1: alien civilization in both galaxies before you could even begin.

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Speaker 2: I guess that would be the polite thing to do. Yes.

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Speaker 2: But anyways, as you were saying, this is a big

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Speaker 2: part of how science is done these days, and so Daniel,

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Speaker 2: I guess let's start from the basics. What is a

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Speaker 2: simulation in your view as a physicists.

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Speaker 1: So, a simulation, or more specifically, a computer simulation, is

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Speaker 1: a specific program that involves a scientific model. A model

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Speaker 1: is like our picture of how the world might work.

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Speaker 1: It's like a simplified version of the real universe. Let's

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Speaker 1: us explore a specific question, and a simulation is usually

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Speaker 1: a program on a computer that uses like step by

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Speaker 1: step methods to explore the behavior of that model.

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Speaker 2: And usually this model has the form of an equation, right, Like,

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Speaker 2: for example, F equalsma is a model of the world, right,

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Speaker 2: and how things move in the world.

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Speaker 1: Yeah, the science we do is mathematical, and the way

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Speaker 1: we describe things is mathematical, and so usually that involves equations,

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Speaker 1: equations that represent constraints on the model, like the way

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Speaker 1: things have to happen. And as you say, F equals

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Speaker 1: ma is a model. If I want to toss a

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Speaker 1: baseball across my backyard, I want to answer the question

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Speaker 1: where is it going to land? Then I have lots

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Speaker 1: of possible ways to answer that question, but the most

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Speaker 1: appropriate ways to make the simplest model possible that still

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Speaker 1: captures everything that I'm interested in, and so often in

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Speaker 1: our world, like when we're tossing baseballs, we can do

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Speaker 1: something pretty simple just F equals ma, which ignores all

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Speaker 1: sorts of swarming quantum details about what's happening inside at

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Speaker 1: the baseball and just describes simple motion of a parabole.

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Speaker 2: Yeah, it's almost like you. I mean, as a scientist,

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Speaker 2: you're trying to come up with the rules of the universe, right,

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Speaker 2: that's sort of the goal of science, right, And what

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Speaker 2: sometimes that rule looks like is in a question that says,

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Speaker 2: you know, if you have a mass and you apply

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Speaker 2: a force to it, then it's going to start moving

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Speaker 2: with a certain acceleration.

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Speaker 1: Exactly in your words, these are all science fictions. We're

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Speaker 1: living in this world and we're wondering what are the rules,

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Speaker 1: and so we're trying a bunch of different rules, saying

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Speaker 1: does this rule describe our universe? Does that rule describe

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Speaker 1: our universe? So every sort of theoretical exploration of the

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Speaker 1: universe involves building a model and then asking the question

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Speaker 1: does that model align with the reality that we see.

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Speaker 1: Computer simulations are a special kind of model or a

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Speaker 1: special what a test? Really complicated models that we can't

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Speaker 1: otherwise test, Like the model F equals M a pretty simple.

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Speaker 1: I can use pencil and paper to make predictions, and

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Speaker 1: then I can throw a ball in my backyard to

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Speaker 1: confirm those predictions.

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Speaker 2: Right, because I guess F equals A has like a

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Speaker 2: mathematical But I think the idea is that you take

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Speaker 2: in a question like F equals in May and you

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Speaker 2: basically program that into a computer and say, you know,

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Speaker 2: any masses in this program, they have to move according

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Speaker 2: to this law.

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Speaker 1: That's right. If, for example, I don't just want to

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Speaker 1: describe one ball, but I want to describe like ten

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Speaker 1: to the twenty five balls, right, some huge number of balls.

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Speaker 1: Maybe I'm modeling an ideal gas, or like a swimming

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Speaker 1: pool full of ping pong balls or something, and I

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Speaker 1: want to describe that. Then I can no longer use

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Speaker 1: pencil and paper. But you're in. I can take those

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Speaker 1: equations and put them into a computer and ask the

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Speaker 1: computer to force those balls to follow that equation, and

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Speaker 1: then I could see what happens. It's sort of like

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Speaker 1: a virtual experiment.

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Speaker 2: Yeah, it's like you're creating your own little universe.

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Speaker 1: Right exactly. And this becomes super essential when we don't

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Speaker 1: have like a single equation that describes everything, Like we

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Speaker 1: don't have a solution to what happens when you put

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Speaker 1: ten to the twenty five ping pong balls into a

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Speaker 1: swimming pool. We just don't know how to do that

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Speaker 1: calculation to come up with some nice summary of the results.

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Speaker 1: What we can do is put it into a computer

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Speaker 1: and have the computer step it forward in time very carefully,

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Speaker 1: and we can see what happens without ever actually having

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Speaker 1: to buy that number of ping bomb balls.

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Speaker 2: Right. That's sort of the power of the computer, right,

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Speaker 2: Like you can simulate one ball, which is a calculator, right,

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Speaker 2: Like you can say after one second, it's going to

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Speaker 2: be here, after two seconds, it is going to be

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Speaker 2: here by following these rules. But if you have, like

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Speaker 2: you said, a whole bunch of balls, or a more

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Speaker 2: complicated system, then a computer can sort of do all

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Speaker 2: those calculations for you faster, exactly.

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Speaker 1: One huge advantage is tackling a very large number of objects,

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Speaker 1: and the other is when we don't have the equations,

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Speaker 1: we don't know how to solve them, Like for f equals,

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Speaker 1: I may we know how to solve that. Technically, that

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Speaker 1: is a differential equation because A is a second derivative

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Speaker 1: of position, right, there's derivatives on both sides. And in

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Speaker 1: general and mathematics, differential equations are very very hard to solve.

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Speaker 1: This a small number that we actually know how to solve.

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Speaker 1: So sometimes you have a system that's described by a

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Speaker 1: differential equation you don't know how to solve, Like fluid flow,

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Speaker 1: for example, described by the Navier Stokes equation. We don't

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Speaker 1: know how to solve that. In general. But what we

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Speaker 1: can do on a computer is approximated. You can say,

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Speaker 1: you know, let's just move it forward in time, not

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Speaker 1: a year or a minute or some long period of time,

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Speaker 1: but just like a microsecond, and across a microsecond, we

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Speaker 1: can make some approximations. We can say, let's not use

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Speaker 1: the full equation, let's simplify it and take some like

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Speaker 1: linear approximation of it, and then if we take a

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Speaker 1: lot of tiny little steps, we hope that we roughly

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Speaker 1: get the right answer.

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Speaker 2: Right, because I think, as you were saying, like something

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Speaker 2: like f EQUOSM has the solution, meaning that you can

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Speaker 2: derive a formula for like the precision of your ball

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Speaker 2: at all times, where you can just like after three

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Speaker 2: seconds you just put the time in and it gives

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Speaker 2: you the position of the ball, right, because you can

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Speaker 2: integrate that equation and find the solution. But some equations

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Speaker 2: you can, like they're so complex you can get a

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Speaker 2: formally that will tell you what's going to happen ten

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Speaker 2: years from now or twenty years from now. Right, those

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Speaker 2: you need to do little steps by little steps exactly.

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Speaker 1: And the crucial idea there is that you're making a

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Speaker 1: linear approximation you're taking the full equation which you don't

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Speaker 1: know how to solve, and you're saying, well, let's replace

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Speaker 1: it with an approximate version of it, which is not

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Speaker 1: going to be correct, but it might be correct for

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Speaker 1: like a microsecond. And so we'll use the approximate linear

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Speaker 1: version of that that we do know how to solve

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Speaker 1: from a tiny little step, and then we'll start again,

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Speaker 1: and we'll make another tiny little step, and we hope

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Speaker 1: them little mistakes cancel out and don't build up into

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Speaker 1: some big overall mistake.

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Speaker 2: Right Yeah. It's almost like if you take small enough steps,

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Speaker 2: then you're less likely to deviate from the reality of.

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Speaker 1: It, right, Yeah, exactly. And people who do approximations know

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Speaker 1: that there's lots of times this is useful. Like you

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Speaker 1: want to calculate Trigg function like sign sign is really

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Speaker 1: hard to calculate like sort of from scratch, but for

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Speaker 1: very small values of the angle, sign of x is

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Speaker 1: just equal to x, you can like approximate this complicated

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Speaker 1: function with a simple one. It mostly gets the right answer.

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Speaker 1: That's just one example.

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Speaker 2: I'm not sure you're going to Trigonometry usually makes things

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Speaker 2: you understand, but I think I think we get the idea,

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Speaker 2: which is that you know, if you take small enough

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Speaker 2: steps and you you sort of a simplified version of

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Speaker 2: your model, then you're less likely to make mistakes.

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Speaker 1: Exactly. You can't trust those approximations forward a second or

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Speaker 1: a minute or a year, but you could trust them

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Speaker 1: like a microsecond. And so you have the simulated universe

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Speaker 1: in your computer. You feed in the initial conditions, and

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Speaker 1: then you ask it to take a step forward in time,

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Speaker 1: and you ask it to take another step forward, and

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Speaker 1: if you have enough computing power, you can run it

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Speaker 1: for a while and you can see what happens to

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Speaker 1: all my ping pong balls in my simulated swimming pool,

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Speaker 1: or what happens to my galaxy as the stars all

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Speaker 1: swirl around each other.

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Speaker 2: Right, Like you were saying, like fluids are notoriously really

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Speaker 2: hard to solve as an equation, right, These are really

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Speaker 2: complex equations that govern what's going on because they sort

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Speaker 2: of like depend on a lot of things. Like there's

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Speaker 2: a lot going on, right, there's time, and then there's

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Speaker 2: distance and the velocity of things and all those factor

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Speaker 2: in that's hard or impossible to like predict exactly what's

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Speaker 2: going to happen in the future exactly.

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Speaker 1: And the big complication there is the interactions. Like back

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Speaker 1: to the ping pong balls. If you just had a

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Speaker 1: lot of ping pong balls and they're all flying around

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Speaker 1: but not touching each other, it wouldn't be that hard

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Speaker 1: to calculate what's going to happen to each one, But

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Speaker 1: as soon as they start banging against each other, becomes

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Speaker 1: much much more complicated because the solution of ping pong

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Speaker 1: ball number six hundred and forty two now depends on

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Speaker 1: ping pong ball number one, one hundred and eleven and

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Speaker 1: every other ping pong ball, so becomes much more complicated.

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Speaker 1: And that's why fluids are so complicated, because every sheet

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Speaker 1: of the fluid depends on the friction with the other

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Speaker 1: sheet of the fluid. And that's what makes the Navier

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Speaker 1: Stokes equation, for example, so intractable.

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Speaker 2: Right, And so you take it little by little, and

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Speaker 2: so you say, okay, this time, I'm going to ignore

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Speaker 2: some of these effects and just take one small step

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Speaker 2: to see where all those little molecules go. And then

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Speaker 2: you keep repeating that and hopefully it sort of looks

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Speaker 2: like the real thing.

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Speaker 1: Exactly, and it gives you this incredible power that you

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Speaker 1: can hopefully identify emergent behavior. The way we do science

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Speaker 1: in our universe is that we like focus on one

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Speaker 1: level where we understand things we can describe, like the

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Speaker 1: microphysics of how particles being against each other. But sometimes

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Speaker 1: we're interested in things at another level, Like you understand

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Speaker 1: how rain drops move through the wind, but your real

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Speaker 1: question is like is this hurricane going to hit Florida

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Speaker 1: or Alabama? And so even if you don't have like

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Speaker 1: an equation that describes hurricanes, if you have an equation

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Speaker 1: that describes the rain drops, you can feed that all

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Speaker 1: into your computer, run simulations, and then get answers to

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Speaker 1: your higher level question. You can see like the emergent

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Speaker 1: phenomena of the hurricane in simulation.

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Speaker 2: Well, it's dig a little bit deeper into how simulation

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Speaker 2: works and the things like weather and why scientists use

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Speaker 2: simulations to try to learn things about the real universe.

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Speaker 2: But first let's take a quick break. All right, we're

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Speaker 2: having a simulation of a podcast here, right, We're not

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Speaker 2: really having a podcast, right, We're just pretending to have

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Speaker 2: a podcast.

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Speaker 1: We're simulating the process of injecting ideas into listen in our.

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Speaker 2: Minds, and so we're talking about why scientists use simulations,

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Speaker 2: and it's kind of, I guess a philosophical question, perhaps

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Speaker 2: because doing a simulation of reality is not really reality, right,

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Speaker 2: and so, and you're not really experimenting on reality. So

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Speaker 2: it's kind of a I guess, a funny thing for

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Speaker 2: scientists to do it because you're not really doing experiments

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Speaker 2: in the real world. But it's at the same time

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Speaker 2: really helpful. Right.

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Speaker 1: That's true, But that same criticism could be applied to

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Speaker 1: basically everything in science. When we do science, we never

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Speaker 1: use all of the full gory details of the universe

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Speaker 1: to answer a question. We're always using some stripped down

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Speaker 1: version because otherwise it's totally intractable. Like when we do

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Speaker 1: F equals M, even for a single ball flying through

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Speaker 1: the air, we're ignoring lots of stuff. We're ignoring air resistance,

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Speaker 1: we're ignoring quantum effects of the particles inside of it.

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Speaker 1: We're ignoring all sorts of things because we don't think

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Speaker 1: that they are important. And so every time you build

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Speaker 1: a model of the universe, theoretical or simulation, you're always

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Speaker 1: making a choice about what to ignore and what to include.

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Speaker 2: Well, we talked a lot about what a simulation is right.

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Speaker 2: It's a computer program where you program in the rules

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Speaker 2: that you think that the world follows the rules of

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Speaker 2: the universe, at least in your simulated universe, and then

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Speaker 2: you sort of let the computer kind of run this world,

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Speaker 2: and then it sort of tells you what may or

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Speaker 2: may will sort of happen.

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Speaker 1: Exactly, and it lets you examine all sorts of universes

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Speaker 1: you don't otherwise have access to, like in my work,

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Speaker 1: and let's me answer questions like what would I see

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Speaker 1: in our particle detectors if the Higgs boson was this

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Speaker 1: kind of particle, or what if there was no Higgs boson,

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Speaker 1: or what if it had twice the mass that it had?

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Speaker 1: What would we see in our detectors? What would that

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Speaker 1: universe be?

442
00:20:37,600 --> 00:20:37,719
Speaker 2: Like?

443
00:20:37,840 --> 00:20:39,600
Speaker 1: What would those experiments result in?

444
00:20:39,880 --> 00:20:42,439
Speaker 2: So it gives you ideas for experiments, or it's a

445
00:20:42,560 --> 00:20:45,480
Speaker 2: sort it can guide your real experiments. Right, that's part

446
00:20:45,520 --> 00:20:46,000
Speaker 2: of the idea.

447
00:20:46,040 --> 00:20:49,880
Speaker 1: Right, It's actually crucial for interpreting our experiments. When we

448
00:20:49,920 --> 00:20:52,040
Speaker 1: look at data from the actual collider and we see

449
00:20:52,040 --> 00:20:54,600
Speaker 1: these splashes of energy here and splashes of energy there,

450
00:20:54,600 --> 00:20:57,160
Speaker 1: and we look at the patterns the correlations. The way

451
00:20:57,200 --> 00:21:01,160
Speaker 1: we interpret those is by comparing them to simulations, we say,

452
00:21:01,720 --> 00:21:04,440
Speaker 1: is this consistent with the higgs boson with these properties

453
00:21:04,520 --> 00:21:06,240
Speaker 1: or is it more consistent with the higgs boson with

454
00:21:06,320 --> 00:21:09,760
Speaker 1: some other properties. So the simulation in some sense defines

455
00:21:09,800 --> 00:21:12,800
Speaker 1: the ideas that we're considering, the various hypotheses that we're

456
00:21:12,800 --> 00:21:14,000
Speaker 1: trying to distinguish between.

457
00:21:14,160 --> 00:21:17,120
Speaker 2: Right, It lets you explore the possibilities. That's the idea

458
00:21:17,119 --> 00:21:19,360
Speaker 2: of a simulation. Right, Let's you maybe make mistakes.

459
00:21:19,440 --> 00:21:22,200
Speaker 1: Absolutely, and before we build a detector, we simulated to

460
00:21:22,200 --> 00:21:24,880
Speaker 1: see like is this going to work or how well

461
00:21:24,960 --> 00:21:27,280
Speaker 1: is it going to perform? Or oops, turns out we

462
00:21:27,400 --> 00:21:29,439
Speaker 1: need to swap the order these two things or nothing's

463
00:21:29,480 --> 00:21:31,840
Speaker 1: going to work. So yeah, making mistakes and simulation is

464
00:21:31,920 --> 00:21:33,760
Speaker 1: much cheaper than making them in reality.

465
00:21:33,880 --> 00:21:36,040
Speaker 2: Yeah, And as you were saying, simulations play a big

466
00:21:36,080 --> 00:21:38,720
Speaker 2: part in weather prediction, right, I mean that's how weather

467
00:21:38,840 --> 00:21:41,160
Speaker 2: predictions work. Like when you look at the weather forecast

468
00:21:41,359 --> 00:21:43,640
Speaker 2: and says it's going to rain tomorrow, it's because some

469
00:21:43,680 --> 00:21:46,600
Speaker 2: big computer out there has basically taken the data from

470
00:21:46,640 --> 00:21:49,840
Speaker 2: today and simulated what's going to happen tomorrow exactly.

471
00:21:49,920 --> 00:21:54,360
Speaker 1: And that's really the origin of computer simulations. People wanted

472
00:21:54,359 --> 00:21:57,200
Speaker 1: to predict the weather, to understand what's going to happen

473
00:21:57,240 --> 00:21:59,600
Speaker 1: to these cloud patterns. But nobody could really do it

474
00:21:59,600 --> 00:22:02,720
Speaker 1: with pen and papers. Too complicated, too many pieces of information,

475
00:22:02,800 --> 00:22:05,960
Speaker 1: and the equations are really just a mess. So meteorology

476
00:22:06,000 --> 00:22:08,160
Speaker 1: is one of the first places where people decided, let's

477
00:22:08,160 --> 00:22:10,400
Speaker 1: code this up on the computer to try to grapple

478
00:22:10,480 --> 00:22:12,919
Speaker 1: with this complexity and see if we can get anything

479
00:22:13,000 --> 00:22:15,119
Speaker 1: right now, just after World War Two when computers were

480
00:22:15,160 --> 00:22:18,880
Speaker 1: first displaying like computational power, and it was the weather

481
00:22:18,920 --> 00:22:22,359
Speaker 1: forecasters and the nuclear physicists that first really jumped on

482
00:22:22,359 --> 00:22:22,800
Speaker 1: this train.

483
00:22:23,320 --> 00:22:25,359
Speaker 2: Yeah, because I think the way the weather works is

484
00:22:25,400 --> 00:22:30,119
Speaker 2: that you have all this data mateiological data, weather data

485
00:22:30,680 --> 00:22:33,480
Speaker 2: across let's say the United States, that tells you the

486
00:22:33,520 --> 00:22:36,120
Speaker 2: wind speeds and the clouds and the pressures and all that,

487
00:22:36,600 --> 00:22:38,240
Speaker 2: and then you can use it and put it into

488
00:22:38,880 --> 00:22:41,960
Speaker 2: basically your computer, which has a model of what should

489
00:22:41,960 --> 00:22:45,040
Speaker 2: happen next if that's you have all these pressures and

490
00:22:45,119 --> 00:22:46,800
Speaker 2: wind patterns exactly.

491
00:22:47,000 --> 00:22:50,160
Speaker 1: And those models are not perfect, they don't describe everything,

492
00:22:50,280 --> 00:22:53,479
Speaker 1: and so they're most reliable over short times because that's

493
00:22:53,480 --> 00:22:55,160
Speaker 1: when the errors are not going to compound as much,

494
00:22:55,320 --> 00:22:57,080
Speaker 1: which is why I like the prediction for how hot

495
00:22:57,080 --> 00:22:59,439
Speaker 1: it's going to be tomorrow is much more reliable than

496
00:22:59,440 --> 00:23:01,679
Speaker 1: the prediction for how hot it's going to be in

497
00:23:01,840 --> 00:23:05,000
Speaker 1: ten years, which you basically have no information about. Or

498
00:23:05,040 --> 00:23:07,040
Speaker 1: if you look at those projections for like where's the

499
00:23:07,119 --> 00:23:10,280
Speaker 1: hurricane going to be, the potential path of the hurricane

500
00:23:10,320 --> 00:23:13,480
Speaker 1: gets wider as the prediction gets further out because there's

501
00:23:13,480 --> 00:23:15,919
Speaker 1: more uncertainty, like is it going to hit Alabama? We

502
00:23:16,000 --> 00:23:16,400
Speaker 1: don't know.

503
00:23:16,720 --> 00:23:18,840
Speaker 2: Yeah, it's pretty cool and actually an interesting fact. I

504
00:23:18,840 --> 00:23:22,359
Speaker 2: would just talk to a hurricane scientist a couple of

505
00:23:22,400 --> 00:23:25,080
Speaker 2: months ago, and he was saying that we're still at

506
00:23:25,080 --> 00:23:30,920
Speaker 2: the point apparently where humans outperform simulations, even supercomputer simulations.

507
00:23:30,480 --> 00:23:33,080
Speaker 1: Humans using pencil and paper, or just humans like with

508
00:23:33,119 --> 00:23:34,200
Speaker 1: their intuition.

509
00:23:34,000 --> 00:23:36,800
Speaker 2: Humans with their intuition. So like, apparently we're still at

510
00:23:36,800 --> 00:23:39,560
Speaker 2: the point where if if you're seeing a hurricane move,

511
00:23:39,840 --> 00:23:41,879
Speaker 2: you run a computer simulation about where it's going to

512
00:23:41,920 --> 00:23:45,240
Speaker 2: go next. A human or like a season experienced hurricane

513
00:23:45,240 --> 00:23:48,560
Speaker 2: watcher will still today better at predicting what the hurricane

514
00:23:48,640 --> 00:23:50,240
Speaker 2: is going to do, just from like what's going on

515
00:23:50,280 --> 00:23:53,200
Speaker 2: inside their brain and the history of what they've seen before.

516
00:23:53,320 --> 00:23:56,159
Speaker 2: But it's getting apparently closer and closer, so maybe in

517
00:23:56,200 --> 00:24:00,240
Speaker 2: the near future computers will make those hurricane watchers. Is

518
00:24:00,280 --> 00:24:01,040
Speaker 2: totally obsleete.

519
00:24:01,080 --> 00:24:03,600
Speaker 1: That's super fascinating and it's fun to think about what's

520
00:24:03,640 --> 00:24:06,960
Speaker 1: going on inside that person's brain. They have built in

521
00:24:07,000 --> 00:24:11,239
Speaker 1: their head some neural network with literal biological neurons, right

522
00:24:11,240 --> 00:24:14,440
Speaker 1: and not your typical artificial neural network that models hurricanes,

523
00:24:14,480 --> 00:24:16,520
Speaker 1: and they've trained it on a bunch of real hurricanes.

524
00:24:16,800 --> 00:24:17,760
Speaker 1: So that's pretty cool.

525
00:24:18,000 --> 00:24:20,639
Speaker 2: Yeah, it makes you wonder if maybe, like in the future,

526
00:24:20,640 --> 00:24:23,800
Speaker 2: they're going to use AIS to predict the weather, maybe

527
00:24:23,800 --> 00:24:26,120
Speaker 2: you don't need a scientific model of what's going on.

528
00:24:26,240 --> 00:24:29,560
Speaker 1: That's a really fascinating question because AIS are already being

529
00:24:29,680 --> 00:24:33,840
Speaker 1: used to help boost simulations. One problem with simulations is

530
00:24:33,840 --> 00:24:37,440
Speaker 1: that they can be very expensive computationally. You have lots

531
00:24:37,440 --> 00:24:39,160
Speaker 1: and lots of rain jops and you want to model

532
00:24:39,160 --> 00:24:41,760
Speaker 1: it very, very accurately. It takes a computer a long

533
00:24:41,800 --> 00:24:44,280
Speaker 1: time to calculate every rain job and move it forward

534
00:24:44,320 --> 00:24:46,840
Speaker 1: in time. You want to predict something a few days out,

535
00:24:47,000 --> 00:24:49,600
Speaker 1: it can be very expensive computationally. We run into this

536
00:24:49,640 --> 00:24:51,640
Speaker 1: problem in particle physics all the time because we want

537
00:24:51,680 --> 00:24:54,439
Speaker 1: to simulate billions and billions of potential collisions, and the

538
00:24:54,480 --> 00:24:57,920
Speaker 1: interactions with the detector are very complicated. So to generate

539
00:24:58,000 --> 00:25:01,840
Speaker 1: one simulated collision, for example, like thirty minutes, even on

540
00:25:01,880 --> 00:25:04,639
Speaker 1: a modern computer, we use AI to boost those to

541
00:25:04,680 --> 00:25:08,840
Speaker 1: make them faster. Essentially, we train machine learning algorithms to

542
00:25:08,960 --> 00:25:13,479
Speaker 1: reproduce what the careful calculations have done. They don't understand it,

543
00:25:13,520 --> 00:25:15,880
Speaker 1: they don't like have the same equations built in. It's

544
00:25:15,880 --> 00:25:18,320
Speaker 1: just sort of like those people watching the examples and

545
00:25:18,359 --> 00:25:21,600
Speaker 1: getting an intuition. This is like a machine learning intuition.

546
00:25:22,720 --> 00:25:26,840
Speaker 2: So now you're not just similar working in a Meida world.

547
00:25:26,920 --> 00:25:29,280
Speaker 2: Now you're in twitting your way through a Meida world.

548
00:25:29,400 --> 00:25:31,520
Speaker 1: Yeah. And one problem is that we don't always know

549
00:25:31,680 --> 00:25:34,280
Speaker 1: if their predictions are accurate or why they make them.

550
00:25:34,280 --> 00:25:36,920
Speaker 1: You can't ask them like why did this go left

551
00:25:36,960 --> 00:25:39,560
Speaker 1: instead of right? They just have an internal model, the

552
00:25:39,600 --> 00:25:42,920
Speaker 1: same way your hurricane watchers probably can't answer detailed questions

553
00:25:42,960 --> 00:25:45,359
Speaker 1: about why they feel it's going this way. They just

554
00:25:45,400 --> 00:25:45,840
Speaker 1: feel it.

555
00:25:46,119 --> 00:25:50,040
Speaker 2: Yeah. And so it also raises these interesting philosophical questions

556
00:25:50,040 --> 00:25:52,720
Speaker 2: about what science is right, Like is it still science

557
00:25:52,760 --> 00:25:54,600
Speaker 2: if you get an AI to predict what's going on.

558
00:25:54,800 --> 00:25:56,680
Speaker 2: Even if you don't understand what the AI did.

559
00:25:56,840 --> 00:25:59,440
Speaker 1: It's a deep question that we're struggling with all the time.

560
00:25:59,520 --> 00:26:02,360
Speaker 1: But with no controversial is that it gives us extraordinary

561
00:26:02,400 --> 00:26:05,240
Speaker 1: power to do things we just couldn't do otherwise. We

562
00:26:05,320 --> 00:26:07,879
Speaker 1: can now run our simulations for much much longer and

563
00:26:07,920 --> 00:26:09,959
Speaker 1: in much more depth. You want to know what's going

564
00:26:10,000 --> 00:26:12,439
Speaker 1: to happen when the Milky Way collides with Andromeda or

565
00:26:12,440 --> 00:26:16,320
Speaker 1: the far future of our universe. Simulations give you that power.

566
00:26:16,560 --> 00:26:18,439
Speaker 1: You don't have to sit around and wait for the

567
00:26:18,560 --> 00:26:21,280
Speaker 1: events to play out. We can test it in simulation.

568
00:26:21,800 --> 00:26:24,439
Speaker 2: Right. That's pretty cool. And so what are the different

569
00:26:24,480 --> 00:26:26,320
Speaker 2: types of simulations that scientists use.

570
00:26:26,680 --> 00:26:29,520
Speaker 1: I would say that the simulation is almost everywhere in science.

571
00:26:29,600 --> 00:26:31,560
Speaker 1: You know, it used to be limited to a few

572
00:26:31,600 --> 00:26:36,080
Speaker 1: computationally complex fields, but now everybody sees how useful it is.

573
00:26:36,600 --> 00:26:39,119
Speaker 1: You know, even big companies like you want to design

574
00:26:39,119 --> 00:26:41,840
Speaker 1: a new airplane and you're considering a few different wing shapes.

575
00:26:42,080 --> 00:26:44,440
Speaker 1: It used to be you have to build prototypes of

576
00:26:44,480 --> 00:26:46,760
Speaker 1: those wing shapes and put them in a real huge

577
00:26:46,880 --> 00:26:50,439
Speaker 1: wind tunnel, very time consuming expensive. Now you can just

578
00:26:50,600 --> 00:26:53,440
Speaker 1: simulate the wind tunnel and get an idea for which

579
00:26:53,480 --> 00:26:55,879
Speaker 1: wing shape is going to work. You can explore thousands

580
00:26:55,880 --> 00:26:59,960
Speaker 1: of different shapes simultaneously, so that can be very very powerful.

581
00:27:00,119 --> 00:27:03,160
Speaker 1: So I think simulations are essentially everywhere in science now.

582
00:27:03,320 --> 00:27:05,760
Speaker 2: Well, I think they've been using simulations in things like

583
00:27:05,800 --> 00:27:08,720
Speaker 2: aerospace for a long time, right, Like even I'm thinking

584
00:27:08,720 --> 00:27:11,159
Speaker 2: in the space program in the fifties and sixties. I mean,

585
00:27:11,200 --> 00:27:14,560
Speaker 2: they didn't use physical computers, but they use people computers

586
00:27:14,560 --> 00:27:17,560
Speaker 2: to sort of simulate what the trajectories of the spacecraft

587
00:27:17,840 --> 00:27:18,840
Speaker 2: were going to be, right.

588
00:27:18,880 --> 00:27:22,480
Speaker 1: They definitely used human brains to do those calculations. Whether

589
00:27:22,520 --> 00:27:25,399
Speaker 1: you consider that a simulation, I think as a tricky point.

590
00:27:25,680 --> 00:27:28,960
Speaker 1: Is that just a theoretical calculation which people have been doing,

591
00:27:29,000 --> 00:27:32,320
Speaker 1: you know since Galileo or Francis Bacon or whatever. Is

592
00:27:32,359 --> 00:27:33,480
Speaker 1: it actually a simulation?

593
00:27:34,000 --> 00:27:35,879
Speaker 2: I don't know that. That's a tough question, all right,

594
00:27:35,920 --> 00:27:37,520
Speaker 2: So then what are some of the other types of

595
00:27:37,520 --> 00:27:40,639
Speaker 2: simulations people do, or what are some other ways that

596
00:27:40,720 --> 00:27:42,080
Speaker 2: physicists use simulations.

597
00:27:42,200 --> 00:27:44,960
Speaker 1: Another way they use them is to observe things that

598
00:27:45,000 --> 00:27:48,000
Speaker 1: they otherwise couldn't see, Like we want to know what's

599
00:27:48,040 --> 00:27:50,520
Speaker 1: going on inside the sun. Well, we have really no

600
00:27:50,720 --> 00:27:53,920
Speaker 1: prospects for actually seeing what's going on inside the Sun.

601
00:27:54,400 --> 00:27:56,840
Speaker 1: But we can build a simulation of the inside of

602
00:27:56,880 --> 00:27:59,360
Speaker 1: the Sun, and that's going to make predictions for things

603
00:27:59,359 --> 00:28:01,720
Speaker 1: that we can see, things happening on the surface of

604
00:28:01,760 --> 00:28:04,200
Speaker 1: the Sun, or the number of neutrinos coming to Earth,

605
00:28:04,280 --> 00:28:06,719
Speaker 1: and that helps us get an understanding for what's really

606
00:28:06,760 --> 00:28:10,600
Speaker 1: happening inside the Sun. And in the simulation, you're not limited, right,

607
00:28:10,640 --> 00:28:12,959
Speaker 1: you can ask questions about anything that's happening, like what

608
00:28:13,040 --> 00:28:14,840
Speaker 1: is the temperature or the core of the sun, what

609
00:28:15,000 --> 00:28:18,320
Speaker 1: is the velocity of the plasma. So often simulations always

610
00:28:18,400 --> 00:28:21,040
Speaker 1: checked by real experiments in places where we can observe

611
00:28:21,080 --> 00:28:24,320
Speaker 1: them give us access to things that we can't otherwise observe.

612
00:28:24,520 --> 00:28:27,200
Speaker 2: I think that's a crucial step in this process, right,

613
00:28:27,240 --> 00:28:29,439
Speaker 2: Like you can come up with this imaginary world in

614
00:28:29,480 --> 00:28:31,800
Speaker 2: your theater, but it has to match sort of what

615
00:28:31,840 --> 00:28:34,880
Speaker 2: you see at the end with reality, right absolutely.

616
00:28:34,920 --> 00:28:37,520
Speaker 1: Otherwise it's just science fiction, which you know has its

617
00:28:37,520 --> 00:28:40,600
Speaker 1: own value. But there is a special interest in our

618
00:28:40,760 --> 00:28:43,080
Speaker 1: universe and that's led to all sorts of deep understanding.

619
00:28:43,120 --> 00:28:45,440
Speaker 1: You know, the original simulations of the Sun predicted a

620
00:28:45,480 --> 00:28:48,040
Speaker 1: huge number of neutrinos landing on the surface of the Earth,

621
00:28:48,080 --> 00:28:50,120
Speaker 1: and they went out and measured them and the answer

622
00:28:50,280 --> 00:28:52,600
Speaker 1: was wrong, and they thought, did we get the sun wrong?

623
00:28:52,720 --> 00:28:55,080
Speaker 1: Or is there something going on with neutrinos? And it

624
00:28:55,120 --> 00:28:57,200
Speaker 1: turned out the simulation of the sun was correct and

625
00:28:57,280 --> 00:28:59,960
Speaker 1: neutrinos were doing something wonky between there and.

626
00:29:00,160 --> 00:29:01,920
Speaker 2: Here, right. I think the idea is that, you know,

627
00:29:01,960 --> 00:29:04,840
Speaker 2: if you create a simulation and you tweak the parameters

628
00:29:04,880 --> 00:29:07,120
Speaker 2: of it, right, like the numbers in it, so that

629
00:29:07,240 --> 00:29:09,920
Speaker 2: it matches what you see coming, for example, out of

630
00:29:09,960 --> 00:29:13,840
Speaker 2: the real Sun, then the idea is that maybe what

631
00:29:13,920 --> 00:29:15,880
Speaker 2: do you think is going on inside the sun is

632
00:29:15,920 --> 00:29:17,720
Speaker 2: actually what is going on inside the sun?

633
00:29:17,920 --> 00:29:20,680
Speaker 1: Yeah, that's exactly right. And somebody else might come up

634
00:29:20,680 --> 00:29:24,160
Speaker 1: with another simulation saying, actually, I think something else is happening.

635
00:29:24,520 --> 00:29:26,400
Speaker 1: And then you can ask, well, what's the difference between

636
00:29:26,400 --> 00:29:29,280
Speaker 1: these two simulations. Do they predict any different things that

637
00:29:29,320 --> 00:29:32,200
Speaker 1: we actually can measure that you can go off and

638
00:29:32,320 --> 00:29:35,360
Speaker 1: use that to distinguish between two various ideas. And we

639
00:29:35,440 --> 00:29:37,760
Speaker 1: talk about this all the time on the podcast. Sometimes

640
00:29:37,760 --> 00:29:40,680
Speaker 1: we have like two different possible ideas for what's happening

641
00:29:40,720 --> 00:29:43,400
Speaker 1: in near black holes. Remember we once talked about the

642
00:29:43,440 --> 00:29:46,840
Speaker 1: magnetic field near black holes something we could definitely not measure.

643
00:29:47,120 --> 00:29:49,480
Speaker 1: And there were two different models. One was called mad,

644
00:29:49,600 --> 00:29:52,680
Speaker 1: one was called sane, and they made slightly different predictions.

645
00:29:52,720 --> 00:29:55,360
Speaker 1: And then the recent picture of the black hole helped

646
00:29:55,400 --> 00:29:59,120
Speaker 1: us distinguish between these two models, these two simulations for

647
00:29:59,240 --> 00:30:00,680
Speaker 1: black hole magnet fields.

648
00:30:00,960 --> 00:30:03,120
Speaker 2: Right. But I guess that's the tricky thing, is like

649
00:30:03,400 --> 00:30:05,680
Speaker 2: just because the simulation matches what you see at the end,

650
00:30:05,840 --> 00:30:08,560
Speaker 2: it may not necessarily be what's going on inside, Right,

651
00:30:08,600 --> 00:30:11,480
Speaker 2: It could just be sort of a coincidence that it matches.

652
00:30:11,600 --> 00:30:13,520
Speaker 1: It certainly could be, And you always have to be

653
00:30:13,560 --> 00:30:16,880
Speaker 1: careful trusting your simulation. You always need ways to validate

654
00:30:16,920 --> 00:30:18,920
Speaker 1: it and to ensure that the bits that are important

655
00:30:18,920 --> 00:30:20,720
Speaker 1: to your science question are accurate.

656
00:30:20,920 --> 00:30:24,400
Speaker 2: Because I guess sometimes that's the only option that we have, right,

657
00:30:24,520 --> 00:30:27,160
Speaker 2: Like you're saying you can't just stick a stick inside

658
00:30:27,200 --> 00:30:29,959
Speaker 2: the science and see what's going on and things like

659
00:30:30,000 --> 00:30:32,640
Speaker 2: maybe black holes or the Big Bang, Like there's no

660
00:30:32,680 --> 00:30:34,720
Speaker 2: way where we can go back in time and do

661
00:30:34,760 --> 00:30:36,920
Speaker 2: an experiment on the Big Bang, Right, So we sort

662
00:30:36,920 --> 00:30:39,840
Speaker 2: of have to rely on these simulations to try to

663
00:30:39,920 --> 00:30:41,040
Speaker 2: understand what was going on.

664
00:30:41,240 --> 00:30:43,880
Speaker 1: Yeah, And they've turned out to be extraordinarily powerful tools

665
00:30:43,880 --> 00:30:45,720
Speaker 1: that give us insight into what might have happened in

666
00:30:45,760 --> 00:30:47,920
Speaker 1: the early universe or what's going on in the hearts

667
00:30:47,920 --> 00:30:50,920
Speaker 1: of black holes or neutron stars. I can't really imagine

668
00:30:50,960 --> 00:30:51,960
Speaker 1: doing science without them.

669
00:30:52,120 --> 00:30:54,360
Speaker 2: All right, Well, that's sort of what I guess a

670
00:30:54,400 --> 00:30:58,640
Speaker 2: pretty good answer for why scientists use simulations. And surprise, twist,

671
00:30:58,640 --> 00:31:02,360
Speaker 2: this whole conversation was just a simulation of our discussion

672
00:31:02,400 --> 00:31:04,840
Speaker 2: of the topic. This is like a sixth sense. I

673
00:31:04,920 --> 00:31:08,120
Speaker 2: only see simulated people. This was not the real podcast, right, Daniel.

674
00:31:08,200 --> 00:31:11,280
Speaker 1: That's right? Yeah, and hopefully this answer is also true

675
00:31:11,320 --> 00:31:12,240
Speaker 1: in the real universe.

676
00:31:12,360 --> 00:31:15,720
Speaker 2: But Daniel, you got to interview a scientist who does

677
00:31:15,760 --> 00:31:19,680
Speaker 2: physics and actually also wrote a book about simulating things

678
00:31:19,720 --> 00:31:20,440
Speaker 2: in the universe.

679
00:31:20,680 --> 00:31:23,760
Speaker 1: That's right. I had a fun chat with Professor Andrew Pnsen.

680
00:31:24,080 --> 00:31:27,520
Speaker 1: He's a cosmologist and a professor at University of College London,

681
00:31:27,560 --> 00:31:30,160
Speaker 1: and he wrote a new fun book called Universe in

682
00:31:30,200 --> 00:31:34,200
Speaker 1: a Box, which explores the role of simulation in cosmology

683
00:31:34,280 --> 00:31:36,959
Speaker 1: and in science in general. And he does have an

684
00:31:36,960 --> 00:31:39,760
Speaker 1: answer for the question is our universe a simulation?

685
00:31:40,120 --> 00:31:42,200
Speaker 2: Now? If I order Universe in a Box, do I

686
00:31:42,240 --> 00:31:43,440
Speaker 2: get a universe in a box.

687
00:31:45,680 --> 00:31:47,320
Speaker 1: Maybe you get a recipe for how to put a

688
00:31:47,400 --> 00:31:48,400
Speaker 1: universe into a box.

689
00:31:48,840 --> 00:31:51,800
Speaker 2: That's a that's not the universe in a box? And

690
00:31:51,880 --> 00:31:55,000
Speaker 2: also why a box? Why not? I don't know, as

691
00:31:55,000 --> 00:31:55,960
Speaker 2: spherical container.

692
00:31:56,040 --> 00:31:57,840
Speaker 1: I thought you were going to ask, if the universe

693
00:31:57,880 --> 00:32:00,320
Speaker 1: is in a box, what universe is the box in?

694
00:32:00,760 --> 00:32:03,120
Speaker 2: Hmmm, No, I wasn't going to ask that.

695
00:32:05,480 --> 00:32:07,840
Speaker 1: Dang it will my simulated Jorge wind Riven.

696
00:32:07,760 --> 00:32:10,360
Speaker 2: It's in the multiverse. I don't know. Aren't they like

697
00:32:10,480 --> 00:32:13,640
Speaker 2: meta universes outside of our universe? Isn't that the idea?

698
00:32:13,800 --> 00:32:16,479
Speaker 1: Click on the multiverse box option on Amazon Shipping.

699
00:32:16,560 --> 00:32:19,200
Speaker 2: The question then is can you have a multiverse in

700
00:32:19,240 --> 00:32:22,680
Speaker 2: a box? Anyways, you had a great conversation with Andrew.

701
00:32:22,840 --> 00:32:23,560
Speaker 1: I certainly did.

702
00:32:23,680 --> 00:32:25,240
Speaker 2: What motivated him to write this book.

703
00:32:25,280 --> 00:32:27,600
Speaker 1: You felt like the role of simulation in science was

704
00:32:27,760 --> 00:32:31,280
Speaker 1: super important and yet hadn't really been explored by any

705
00:32:31,320 --> 00:32:33,479
Speaker 1: pop side book, and so he wanted to share his

706
00:32:33,600 --> 00:32:36,040
Speaker 1: love for simulations with everybody.

707
00:32:36,040 --> 00:32:39,240
Speaker 2: Cool. Well, here is Daniel's interview with Professor Andrew Ponson

708
00:32:39,440 --> 00:32:42,120
Speaker 2: about his new book, Universe in a Box.

709
00:32:44,480 --> 00:32:47,480
Speaker 1: So then it's my pleasure to welcome to the program.

710
00:32:47,520 --> 00:32:52,560
Speaker 1: Andrew Ponson, a cosmologist and professor at University College London,

711
00:32:52,680 --> 00:32:54,560
Speaker 1: and youw thank you very much for joining us today.

712
00:32:54,760 --> 00:32:55,400
Speaker 3: Oh, thank you.

713
00:32:55,880 --> 00:32:59,040
Speaker 1: So your book is called Universe in a Box. It's

714
00:32:59,040 --> 00:33:02,360
Speaker 1: a fascinating and telling history and sort of definition of

715
00:33:02,400 --> 00:33:05,640
Speaker 1: what is a simulation and why it's important in science.

716
00:33:06,000 --> 00:33:09,560
Speaker 1: So let's start off with very very basics. What is

717
00:33:09,680 --> 00:33:11,600
Speaker 1: a simulation in your point of view?

718
00:33:11,720 --> 00:33:13,680
Speaker 6: There are different definitions you can give, but I think

719
00:33:13,720 --> 00:33:16,480
Speaker 6: a good place to start is by thinking it's trying

720
00:33:16,520 --> 00:33:21,600
Speaker 6: to capture some element of the real world inside a computer,

721
00:33:21,880 --> 00:33:24,640
Speaker 6: and that can take many different forms. It doesn't even

722
00:33:24,720 --> 00:33:27,480
Speaker 6: have to be a physics right, it's we can have

723
00:33:27,520 --> 00:33:31,680
Speaker 6: simulations of something like human behavior. There are simulations of

724
00:33:31,720 --> 00:33:34,840
Speaker 6: the way that crowds might behave that architects use to

725
00:33:35,120 --> 00:33:38,360
Speaker 6: make safer buildings by making the passageways the right kind

726
00:33:38,400 --> 00:33:41,400
Speaker 6: of size and shape that if there's an emergency situation,

727
00:33:41,640 --> 00:33:45,360
Speaker 6: then humans will evacuate the building in the most efficient,

728
00:33:45,440 --> 00:33:48,520
Speaker 6: safe way. But I think what that already teaches you

729
00:33:48,680 --> 00:33:51,000
Speaker 6: is that it's possible to do a simulation of something

730
00:33:51,560 --> 00:33:56,160
Speaker 6: without necessarily understanding everything about that thing before you start.

731
00:33:56,840 --> 00:34:00,240
Speaker 6: Because if you think of crowds, they're made out of people.

732
00:34:00,720 --> 00:34:04,920
Speaker 6: We can't actually predict everything about how an individual human's

733
00:34:04,960 --> 00:34:08,640
Speaker 6: going to react in any given scenario, And yet we

734
00:34:08,760 --> 00:34:12,040
Speaker 6: can make simulations that are useful. They might not be perfect,

735
00:34:12,080 --> 00:34:15,200
Speaker 6: but they're useful for giving us some insight into the

736
00:34:15,200 --> 00:34:18,319
Speaker 6: way that crowds might behave. So when you take that

737
00:34:18,360 --> 00:34:22,919
Speaker 6: across to the physics environment, and in particular my area cosmology,

738
00:34:23,560 --> 00:34:28,040
Speaker 6: it's about trying to capture something about how the universe behaves.

739
00:34:28,480 --> 00:34:30,680
Speaker 6: But we know from the outset we're never going to

740
00:34:30,719 --> 00:34:31,480
Speaker 6: get that perfect.

741
00:34:31,719 --> 00:34:34,799
Speaker 1: So then where is the value in a simulation if

742
00:34:34,840 --> 00:34:37,920
Speaker 1: you have to, like encode in already what's going to

743
00:34:37,960 --> 00:34:40,960
Speaker 1: happen when people bump into each other, or the purchasing

744
00:34:41,040 --> 00:34:44,400
Speaker 1: choices of people, or how galaxies interact. If you have

745
00:34:44,440 --> 00:34:47,000
Speaker 1: to already build in the physics, what are you learning

746
00:34:47,040 --> 00:34:49,759
Speaker 1: from the simulation? How do you get any information out

747
00:34:49,800 --> 00:34:50,000
Speaker 1: of it?

748
00:34:50,800 --> 00:34:53,879
Speaker 6: Well, the point is that you code in some things

749
00:34:53,920 --> 00:34:57,799
Speaker 6: about how the individual bits within your simulation behave. I

750
00:34:57,800 --> 00:34:59,520
Speaker 6: guess you know, in the case of the crowd that

751
00:34:59,560 --> 00:35:03,080
Speaker 6: would be how an individual human might behave under a

752
00:35:03,160 --> 00:35:06,440
Speaker 6: variety of circumstances. But in the case of physics, it

753
00:35:06,520 --> 00:35:10,239
Speaker 6: might be how we think dark matter particles flow through

754
00:35:10,239 --> 00:35:13,120
Speaker 6: the universe and interact with each other through gravity, and

755
00:35:13,160 --> 00:35:16,760
Speaker 6: what the simulation does is take a very large number

756
00:35:17,160 --> 00:35:21,120
Speaker 6: of those elements and kind of have them all individually

757
00:35:21,160 --> 00:35:25,359
Speaker 6: doing their thing. But the behavior that then emerges can

758
00:35:25,440 --> 00:35:29,280
Speaker 6: be very hard to anticipate in advance. And that's the point.

759
00:35:29,440 --> 00:35:33,120
Speaker 6: Understanding how a crowd behaves is not at all the

760
00:35:33,160 --> 00:35:36,279
Speaker 6: same thing as understanding how a human behaves, and in

761
00:35:36,320 --> 00:35:40,319
Speaker 6: the same way, understanding how dark matter behaves through our

762
00:35:40,360 --> 00:35:43,640
Speaker 6: whole cosmos is not at all the same thing as

763
00:35:43,719 --> 00:35:47,640
Speaker 6: understanding what an individual particle of dark matter might do.

764
00:35:47,920 --> 00:35:51,480
Speaker 1: This principle of emergent phenomena is something I'm super fascinated by.

765
00:35:51,920 --> 00:35:54,400
Speaker 1: It's incredible to me that sometimes we have laws of

766
00:35:54,400 --> 00:35:58,720
Speaker 1: physics at one scale, which you know, causally determine different

767
00:35:58,760 --> 00:36:01,880
Speaker 1: sort of laws of physics at another scale, which we

768
00:36:01,920 --> 00:36:04,120
Speaker 1: can't always easily predict. But as you say, we can

769
00:36:04,280 --> 00:36:07,600
Speaker 1: observe in action if we can, you know, construct the

770
00:36:07,680 --> 00:36:10,879
Speaker 1: right setup to you is simulation. Is it a kind

771
00:36:10,880 --> 00:36:13,560
Speaker 1: of experiment, Is it a kind of theory, or is

772
00:36:13,560 --> 00:36:15,720
Speaker 1: it sort of a new branch of science.

773
00:36:16,080 --> 00:36:17,839
Speaker 6: I think it's a new branch, but I think it's

774
00:36:17,880 --> 00:36:20,799
Speaker 6: got something in common with theory and with experiments, and

775
00:36:20,840 --> 00:36:23,799
Speaker 6: I guess I tilt mainly towards thinking of it as

776
00:36:23,840 --> 00:36:28,239
Speaker 6: an experiment. Now that's a little bit controversial. Sometimes people say, well,

777
00:36:28,280 --> 00:36:31,200
Speaker 6: it can't be an experiment. You've told the computer what

778
00:36:31,320 --> 00:36:34,680
Speaker 6: to do, Whereas in an experiment, you're supposed to go

779
00:36:34,719 --> 00:36:37,600
Speaker 6: and ask nature. You know, you're supposed to put things

780
00:36:37,640 --> 00:36:39,960
Speaker 6: to the test and confront them with the reality of

781
00:36:40,000 --> 00:36:41,200
Speaker 6: how things really work.

782
00:36:41,960 --> 00:36:43,759
Speaker 3: But you know, I'm not.

783
00:36:43,680 --> 00:36:46,560
Speaker 6: Sure that that distinction is always so clear. So an

784
00:36:46,600 --> 00:36:49,520
Speaker 6: example that I give in the book is, let's say

785
00:36:49,560 --> 00:36:53,680
Speaker 6: you're just trying to build an aircraft and you have

786
00:36:53,760 --> 00:36:55,919
Speaker 6: some idea about how you want to shape the wing,

787
00:36:56,400 --> 00:36:58,399
Speaker 6: but you don't know exactly how that wing is going

788
00:36:58,400 --> 00:37:01,839
Speaker 6: to perform. Now you now have a choice. You could

789
00:37:01,880 --> 00:37:05,200
Speaker 6: build a scale model of your wing and put it

790
00:37:05,239 --> 00:37:08,680
Speaker 6: inside a wind tunnel and see how it performs inside

791
00:37:08,680 --> 00:37:11,200
Speaker 6: a wind tunnel, and that's kind of an experiment. Or

792
00:37:11,640 --> 00:37:14,920
Speaker 6: you could make a digital version of your wing and

793
00:37:15,040 --> 00:37:19,000
Speaker 6: put it inside an airflow inside a computer that's a

794
00:37:19,080 --> 00:37:21,879
Speaker 6: kind of simulated airflow, and see how it behaves there.

795
00:37:22,560 --> 00:37:25,080
Speaker 6: And both of those are going to have limitations. There's

796
00:37:25,120 --> 00:37:27,920
Speaker 6: definitely limitations on what you can achieve inside the computer,

797
00:37:28,040 --> 00:37:31,440
Speaker 6: but there's also limitations in what you can achieve in

798
00:37:31,640 --> 00:37:35,360
Speaker 6: a wind tunnel. You can't make an infinitely big wind tunnel.

799
00:37:35,400 --> 00:37:37,560
Speaker 6: It's going to have edges, things are going to be

800
00:37:37,680 --> 00:37:41,799
Speaker 6: the wrong scale. So when you do experiments, you are

801
00:37:42,120 --> 00:37:46,000
Speaker 6: making some set of assumptions about the real world and

802
00:37:46,120 --> 00:37:49,400
Speaker 6: how what you're doing applies to the real world. And

803
00:37:49,480 --> 00:37:51,799
Speaker 6: I think that's just that's true in simulations as well.

804
00:37:51,920 --> 00:37:54,319
Speaker 6: So overall, this is why I start to think more

805
00:37:54,360 --> 00:37:57,440
Speaker 6: and more of simulations as types of experiment.

806
00:37:57,640 --> 00:38:00,480
Speaker 1: I have an argument with my brother who's a computer scientist,

807
00:38:00,920 --> 00:38:04,160
Speaker 1: and he runs what he calls experiments on his machine

808
00:38:04,239 --> 00:38:07,560
Speaker 1: learning models. I'm like, that's not an experiment. You're just

809
00:38:07,640 --> 00:38:10,680
Speaker 1: doing it in your computer. But you're absolutely right that

810
00:38:10,800 --> 00:38:12,560
Speaker 1: if you don't know the outcome and you're learning something,

811
00:38:12,600 --> 00:38:15,280
Speaker 1: it can be considered also an experiment. But you mentioned

812
00:38:15,280 --> 00:38:17,040
Speaker 1: something which I wanted to ask you about anyway, which

813
00:38:17,080 --> 00:38:21,239
Speaker 1: is the limitation of simulation. You're concocting sort of an

814
00:38:21,360 --> 00:38:25,319
Speaker 1: artificial universe, and you're learning something about that universe. If

815
00:38:25,320 --> 00:38:28,440
Speaker 1: that universe doesn't follow the same rules as ours, then

816
00:38:28,480 --> 00:38:31,880
Speaker 1: obviously we're not learning something about reality, which usually is

817
00:38:31,960 --> 00:38:34,799
Speaker 1: the goal. Can you say something about how we know

818
00:38:35,160 --> 00:38:39,760
Speaker 1: when to trust our simulations with the fundamental limitations of simulation.

819
00:38:39,400 --> 00:38:42,720
Speaker 6: Are yeah, I mean that is the hardest, most difficult question.

820
00:38:42,800 --> 00:38:45,799
Speaker 6: At the heart of doing good simulation is knowing what

821
00:38:45,880 --> 00:38:48,879
Speaker 6: to trust and what not to trust, and often it's hard,

822
00:38:49,080 --> 00:38:52,400
Speaker 6: you know, it can be really hard to know. I mean, fundamentally,

823
00:38:52,440 --> 00:38:57,319
Speaker 6: the limitation is just computational power that even if you're

824
00:38:57,320 --> 00:38:59,680
Speaker 6: doing something like a weather forecast, which I talk about

825
00:38:59,719 --> 00:39:03,040
Speaker 6: a bit in the book, you know, just the atmosphere

826
00:39:03,080 --> 00:39:06,360
Speaker 6: of the Earth has so many molecules in it that

827
00:39:06,440 --> 00:39:09,319
Speaker 6: you're never going to track each individual molecule, right, So

828
00:39:09,360 --> 00:39:12,160
Speaker 6: you're going to have to make some kind of approximation.

829
00:39:12,200 --> 00:39:14,920
Speaker 6: You're going to parcel up the air into almost like

830
00:39:15,000 --> 00:39:18,680
Speaker 6: big hypothetical bags of air that move around through our

831
00:39:18,719 --> 00:39:23,399
Speaker 6: atmosphere and use some laws to describe that. But then

832
00:39:23,440 --> 00:39:25,759
Speaker 6: you're going to have to go and say, well, you know,

833
00:39:25,800 --> 00:39:28,319
Speaker 6: we're not getting all the small scale details right, We're

834
00:39:28,320 --> 00:39:31,080
Speaker 6: going to have to put in some corrections. In the

835
00:39:31,120 --> 00:39:35,480
Speaker 6: case of meteorology, even clouds can be quite hard to

836
00:39:35,520 --> 00:39:38,200
Speaker 6: predict because you're just not getting all of those tiny

837
00:39:38,280 --> 00:39:41,960
Speaker 6: details that contribute to the way that a cloud forms

838
00:39:42,000 --> 00:39:44,720
Speaker 6: in reality. So you need to go in and put

839
00:39:44,920 --> 00:39:48,000
Speaker 6: into your simulation some kind of correction almost by hand.

840
00:39:48,600 --> 00:39:52,800
Speaker 6: You say, under these circumstances, clouds must start to form.

841
00:39:53,040 --> 00:39:56,520
Speaker 6: And you know, if you're a weather forecaster, you see

842
00:39:56,920 --> 00:39:59,960
Speaker 6: how well did I do by making that assumption about

843
00:40:00,120 --> 00:40:03,359
Speaker 6: how clouds form? And over time you sort of incrementally

844
00:40:03,400 --> 00:40:07,800
Speaker 6: improve by comparing how your simulation did with the reality

845
00:40:07,800 --> 00:40:10,840
Speaker 6: of how the weather unfolded. So we can do something

846
00:40:11,000 --> 00:40:13,560
Speaker 6: a bit similar in cosmology. It's not quite the same

847
00:40:13,600 --> 00:40:16,080
Speaker 6: because we don't get to kind of do the repeat

848
00:40:16,160 --> 00:40:18,320
Speaker 6: experiments in quite the same way as you do in

849
00:40:18,640 --> 00:40:21,680
Speaker 6: weather forecasting, for example. In some level, it's the same

850
00:40:21,760 --> 00:40:25,120
Speaker 6: kind of iterative process that we're getting better over time.

851
00:40:25,200 --> 00:40:28,040
Speaker 6: We're understanding the way that we have to put in

852
00:40:28,120 --> 00:40:32,440
Speaker 6: corrections to our simulations to account for things like the

853
00:40:32,480 --> 00:40:35,880
Speaker 6: way that stars evolve and change over time and dump

854
00:40:36,000 --> 00:40:39,000
Speaker 6: energy into the universe, and what black holes are up to,

855
00:40:39,080 --> 00:40:41,719
Speaker 6: and all of these things that we actually have to

856
00:40:41,960 --> 00:40:43,799
Speaker 6: help the computer along the way, if you like.

857
00:40:44,000 --> 00:40:47,080
Speaker 1: So why is it that we need to make these corrections?

858
00:40:47,160 --> 00:40:50,960
Speaker 1: We have these limitations is it purely just computational power.

859
00:40:51,320 --> 00:40:55,200
Speaker 1: In the limit of infinite computing power, could we predict

860
00:40:55,239 --> 00:40:58,560
Speaker 1: the weather tomorrow starting from particle physics and modeling every

861
00:40:58,560 --> 00:41:01,200
Speaker 1: single quirk in the atmosphe sphere, or is there a

862
00:41:01,239 --> 00:41:04,600
Speaker 1: conceptual limit there some obstacle that we can't overcome even

863
00:41:04,640 --> 00:41:05,560
Speaker 1: with infinite computing.

864
00:41:06,120 --> 00:41:07,600
Speaker 3: I think both are a problem.

865
00:41:07,760 --> 00:41:10,040
Speaker 6: So, first of all, we are very far from having

866
00:41:10,120 --> 00:41:13,400
Speaker 6: infinite computer power, a very very long way away from that.

867
00:41:13,520 --> 00:41:16,200
Speaker 6: But secondly, you're right, I mean there are more fundamental

868
00:41:16,200 --> 00:41:20,320
Speaker 6: limitations as well. In particular, we do not know exactly

869
00:41:20,360 --> 00:41:23,279
Speaker 6: where every molecule is in the atmosphere to start with.

870
00:41:24,120 --> 00:41:27,640
Speaker 6: So even if you had a computer powerful enough to

871
00:41:27,760 --> 00:41:31,160
Speaker 6: track at the molecular level what our atmosphere is doing,

872
00:41:31,400 --> 00:41:34,840
Speaker 6: you wouldn't know how to start the simulation. You wouldn't

873
00:41:34,880 --> 00:41:37,960
Speaker 6: have enough data to tell it what the atmosphere looks

874
00:41:38,000 --> 00:41:41,719
Speaker 6: like today. So there's an inaccuracy that's sort of just

875
00:41:41,800 --> 00:41:45,240
Speaker 6: coming from not having that perfect data, and an effect

876
00:41:45,560 --> 00:41:50,920
Speaker 6: known as chaos means that imperfect initial data very quickly

877
00:41:50,960 --> 00:41:55,200
Speaker 6: turns into big errors. So there's a kind of famous

878
00:41:55,320 --> 00:41:58,319
Speaker 6: example of this. It was Edward Lorenz who sort of

879
00:41:58,520 --> 00:42:03,000
Speaker 6: gave the thought experiment of butterfly flapping its wings somewhere

880
00:42:03,040 --> 00:42:06,040
Speaker 6: in Europe, say, and it has a sort of series

881
00:42:06,120 --> 00:42:09,400
Speaker 6: of knock on effects that over time just amplify and amplify,

882
00:42:09,440 --> 00:42:13,520
Speaker 6: and eventually the tiny little gust from the butterfly's wings

883
00:42:14,080 --> 00:42:19,000
Speaker 6: actually stimulates the formation of a hurricane. And you know,

884
00:42:19,080 --> 00:42:21,640
Speaker 6: these kind of effects, we know they're there, and physics

885
00:42:21,880 --> 00:42:25,359
Speaker 6: we call them chaos. And so you know, the slightest

886
00:42:26,120 --> 00:42:31,080
Speaker 6: inaccuracy in how you set things up will eventually make

887
00:42:31,120 --> 00:42:33,759
Speaker 6: the simulation depart from reality.

888
00:42:34,040 --> 00:42:35,919
Speaker 3: So we know that's true in cosmology as well.

889
00:42:36,440 --> 00:42:40,400
Speaker 6: And we don't have, you know, perfect information about the

890
00:42:40,400 --> 00:42:43,440
Speaker 6: early universe. We have quite good ideas for what was

891
00:42:43,480 --> 00:42:48,200
Speaker 6: going on there, but it's imperfect, and that means because

892
00:42:48,239 --> 00:42:51,359
Speaker 6: of chaos and the way that those imperfections are amplified

893
00:42:51,400 --> 00:42:55,239
Speaker 6: over time, what we end up with is, in some sense,

894
00:42:55,280 --> 00:42:57,480
Speaker 6: it's like a statistical.

895
00:42:56,960 --> 00:42:59,080
Speaker 3: Recreation of the universe.

896
00:42:59,120 --> 00:43:03,399
Speaker 6: It's telling us to hisstically what sorts of things should

897
00:43:03,440 --> 00:43:05,680
Speaker 6: be in the universe and what sorts of mixtures and

898
00:43:05,719 --> 00:43:09,920
Speaker 6: what kind of patterns, rather than literally recreating the universe.

899
00:43:10,000 --> 00:43:14,120
Speaker 6: So it's almost more like sort of climates, like a

900
00:43:14,200 --> 00:43:17,759
Speaker 6: climate simulation almost rather than a weather simulation.

901
00:43:18,280 --> 00:43:20,839
Speaker 1: I'm very interested in the history of simulations as well.

902
00:43:20,880 --> 00:43:24,200
Speaker 1: I mean, theoretical science is like thousands of years old.

903
00:43:24,239 --> 00:43:27,320
Speaker 1: Experimental science people argue about might be hundreds of years old.

904
00:43:27,480 --> 00:43:30,879
Speaker 1: Simulation based science seems like decades old. Can you take

905
00:43:30,960 --> 00:43:33,360
Speaker 1: us back to the root of it? Where does it begin? Really?

906
00:43:33,480 --> 00:43:33,640
Speaker 4: Well?

907
00:43:33,680 --> 00:43:35,920
Speaker 6: I think you know, the very earliest route you can

908
00:43:35,960 --> 00:43:39,320
Speaker 6: find is in the nineteenth century, where Charles Babbage and

909
00:43:39,360 --> 00:43:42,520
Speaker 6: Ada Lovelace were working on the idea of a computer

910
00:43:43,000 --> 00:43:45,960
Speaker 6: very similar to our modern computers. It was the first

911
00:43:46,000 --> 00:43:49,080
Speaker 6: time really anybody expressed the idea of having a machine

912
00:43:49,760 --> 00:43:54,800
Speaker 6: that could be told to perform any calculation. So before

913
00:43:54,880 --> 00:43:58,200
Speaker 6: that there were machines that did specific calculations, but this

914
00:43:58,360 --> 00:44:01,359
Speaker 6: was the first time somebody envisage a machine where you

915
00:44:01,360 --> 00:44:03,719
Speaker 6: could just give it instructions and it would carry out

916
00:44:04,120 --> 00:44:09,080
Speaker 6: calculations to your specifications. And Ada Lovelace actually wrote at

917
00:44:09,080 --> 00:44:12,080
Speaker 6: that time that one of the applications of being able

918
00:44:12,120 --> 00:44:15,359
Speaker 6: to do that was to be able to take what

919
00:44:15,400 --> 00:44:19,600
Speaker 6: we think are the governing laws of our physics and

920
00:44:19,680 --> 00:44:22,600
Speaker 6: make them kind of practical, you know, get the computer

921
00:44:23,280 --> 00:44:27,080
Speaker 6: to do all of the calculations that turns those abstract

922
00:44:27,120 --> 00:44:32,919
Speaker 6: equations into concrete, specific predictions for different scenarios.

923
00:44:33,160 --> 00:44:34,960
Speaker 3: So that's probably the first.

924
00:44:34,800 --> 00:44:40,040
Speaker 6: Time anyone expressed what we would recognize as a modern simulation. Then,

925
00:44:40,080 --> 00:44:44,839
Speaker 6: in terms of actually performing simulations, remarkably, some people tried

926
00:44:44,880 --> 00:44:48,600
Speaker 6: to do this in the twentieth century before digital computers

927
00:44:49,320 --> 00:44:54,680
Speaker 6: were actually made. So there are some beautiful stories like

928
00:44:56,040 --> 00:44:59,520
Speaker 6: a crazy character called Lewis Fry Richardson who was actually

929
00:45:00,080 --> 00:45:03,120
Speaker 6: on the front line of World War One trying to

930
00:45:03,719 --> 00:45:09,600
Speaker 6: calculate weather forecasts using pen and paper, very very repetitive

931
00:45:09,640 --> 00:45:12,680
Speaker 6: calculations he was doing that would be exactly what a

932
00:45:12,680 --> 00:45:15,960
Speaker 6: computer does today to do a weather forecast, but he

933
00:45:16,080 --> 00:45:19,319
Speaker 6: was doing it just with pen and paper and taking him,

934
00:45:19,320 --> 00:45:22,680
Speaker 6: you know, weeks stretching out into years just to do

935
00:45:22,760 --> 00:45:25,160
Speaker 6: one forecast. He wasn't trying to be practical about it.

936
00:45:25,160 --> 00:45:27,319
Speaker 6: He was just trying to prove a point that this

937
00:45:27,480 --> 00:45:31,680
Speaker 6: is actually doable in practice. And then you know, by

938
00:45:31,680 --> 00:45:35,680
Speaker 6: the end of World War Two there were actual computers available,

939
00:45:35,760 --> 00:45:39,280
Speaker 6: and very quickly from there the whole business of simulating

940
00:45:39,400 --> 00:45:42,760
Speaker 6: all sorts of different things, but ultimately the entire universe

941
00:45:43,080 --> 00:45:44,640
Speaker 6: kind of grew up quite quickly from there.

942
00:45:57,680 --> 00:46:00,200
Speaker 1: Tell us more about the role of simulation in or

943
00:46:00,280 --> 00:46:03,280
Speaker 1: personal research. Is this something you explore because you're fascinated

944
00:46:03,320 --> 00:46:05,400
Speaker 1: by the computer science of it, or to use it

945
00:46:05,480 --> 00:46:07,840
Speaker 1: just a tool that helps answer your physics questions.

946
00:46:08,280 --> 00:46:10,319
Speaker 6: I think it depends on the day you ask me.

947
00:46:10,400 --> 00:46:14,520
Speaker 6: I mean, some days I really enjoy the computer science

948
00:46:14,560 --> 00:46:17,440
Speaker 6: of all this, and you know, it's undeniably cool to

949
00:46:17,560 --> 00:46:20,840
Speaker 6: get to work with some of the world's biggest supercomputers

950
00:46:21,400 --> 00:46:24,319
Speaker 6: and be able to instruct them to carry out these

951
00:46:24,440 --> 00:46:28,400
Speaker 6: kind of simulations, and the results are enormous fun to

952
00:46:28,480 --> 00:46:30,400
Speaker 6: work with as well. So there is a bit of

953
00:46:30,800 --> 00:46:33,040
Speaker 6: there's a bit of that kind of nerdery in it.

954
00:46:33,080 --> 00:46:36,279
Speaker 6: But I think ultimately the thing that really keeps me

955
00:46:36,360 --> 00:46:40,440
Speaker 6: hooked is the idea that we are contributing to a

956
00:46:40,480 --> 00:46:44,760
Speaker 6: bigger picture of how our universe evolved, of the role

957
00:46:44,960 --> 00:46:49,000
Speaker 6: for materials in it that we as yet don't understand.

958
00:46:49,560 --> 00:46:51,440
Speaker 3: Things like dark matter and dark.

959
00:46:51,320 --> 00:46:54,400
Speaker 6: Energy that we know they're out there, they seem to

960
00:46:54,400 --> 00:46:57,160
Speaker 6: be having a profound effect on our universe, but we

961
00:46:57,239 --> 00:46:59,759
Speaker 6: really don't know what they are, and we're trying to

962
00:46:59,840 --> 00:47:03,279
Speaker 6: learn more about that. And then I suppose ultimately what

963
00:47:03,360 --> 00:47:06,120
Speaker 6: we're building towards is a better understanding of where we

964
00:47:06,239 --> 00:47:10,080
Speaker 6: came from. You know, the existence of us carbon based

965
00:47:10,160 --> 00:47:14,000
Speaker 6: life forms on this rocky planet is part of the

966
00:47:14,080 --> 00:47:17,680
Speaker 6: story that we're telling. Because the chemical elements from which

967
00:47:17,760 --> 00:47:21,719
Speaker 6: our planet and life are constructed weren't there in the

968
00:47:21,719 --> 00:47:25,399
Speaker 6: Big Bang. They've been manufactured over time. They need very

969
00:47:25,440 --> 00:47:30,239
Speaker 6: specific conditions to be manufactured and then concentrated enough to

970
00:47:30,920 --> 00:47:34,600
Speaker 6: start forming planets and enabling life and so on. So

971
00:47:34,800 --> 00:47:37,000
Speaker 6: I think, you know, ultimately that's the thing I'm most

972
00:47:37,000 --> 00:47:41,279
Speaker 6: excited by that we are telling this bigger story that

973
00:47:41,360 --> 00:47:43,840
Speaker 6: in the end speaks to a kind of deep question

974
00:47:43,960 --> 00:47:46,080
Speaker 6: within all of us about where did we come from?

975
00:47:46,120 --> 00:47:50,120
Speaker 1: Have you seen yet machine learning being used to amplify

976
00:47:50,400 --> 00:47:54,720
Speaker 1: or speed up, or just overall enhance simulations in your research.

977
00:47:55,480 --> 00:47:58,400
Speaker 6: Yeah, I mean machine learning is more and more important

978
00:47:58,680 --> 00:48:02,080
Speaker 6: throughout astrophysics. So it's being used in a variety of

979
00:48:02,120 --> 00:48:06,359
Speaker 6: different ways. One is to try and improve on some

980
00:48:06,440 --> 00:48:08,760
Speaker 6: of these things that we were talking about a moment

981
00:48:08,800 --> 00:48:12,399
Speaker 6: ago about you know, what do you do about the

982
00:48:12,400 --> 00:48:16,239
Speaker 6: things that you can't quite get right, Like the way

983
00:48:16,280 --> 00:48:19,759
Speaker 6: that stars form out of gas just such a complicated

984
00:48:20,440 --> 00:48:23,759
Speaker 6: process that we can't capture it perfectly the way that

985
00:48:23,760 --> 00:48:27,040
Speaker 6: those stars then put energy back into the galaxy that

986
00:48:27,080 --> 00:48:30,160
Speaker 6: they're forming within the roles of black holes, all of

987
00:48:30,200 --> 00:48:34,600
Speaker 6: these things that are very complicated and multifaceted.

988
00:48:35,239 --> 00:48:37,520
Speaker 3: We can use machine learning.

989
00:48:37,600 --> 00:48:40,760
Speaker 6: To do some of the hard work for us to

990
00:48:40,880 --> 00:48:46,440
Speaker 6: learn from examples of individual simulated stars, say about how

991
00:48:46,480 --> 00:48:49,359
Speaker 6: they behave, and kind of learn the lessons from those

992
00:48:49,400 --> 00:48:51,920
Speaker 6: and then take them and put them in the bigger

993
00:48:51,960 --> 00:48:55,120
Speaker 6: setting of trying to simulate then hundreds of billions of

994
00:48:55,160 --> 00:48:58,040
Speaker 6: stars across a galaxy or maybe you know, even out

995
00:48:58,080 --> 00:49:02,160
Speaker 6: into the universe. Sochine learning can kind of help us

996
00:49:02,400 --> 00:49:06,399
Speaker 6: take lessons from one bit of our simulations or one

997
00:49:06,440 --> 00:49:10,160
Speaker 6: bit of physics and insert them in an efficient way

998
00:49:10,280 --> 00:49:15,000
Speaker 6: into other simulations. That's one role for them, But they're

999
00:49:15,000 --> 00:49:18,400
Speaker 6: also crucial in interpreting the data we get from the

1000
00:49:18,440 --> 00:49:24,120
Speaker 6: real universe. So the data that is coming from telescopes

1001
00:49:24,360 --> 00:49:28,640
Speaker 6: and especially big new survey telescopes, things like EUCLID and

1002
00:49:29,040 --> 00:49:33,560
Speaker 6: the Verra Rubin Observatory, these giant efforts to scan the

1003
00:49:33,600 --> 00:49:37,840
Speaker 6: sky and build maps of our universe. They need machine

1004
00:49:37,880 --> 00:49:41,520
Speaker 6: learning because the machine learning can kind of do a

1005
00:49:41,560 --> 00:49:46,719
Speaker 6: lot of the initial data processing figuring out what's interesting,

1006
00:49:46,880 --> 00:49:50,319
Speaker 6: what we're actually looking at, where it is in three

1007
00:49:50,440 --> 00:49:54,320
Speaker 6: D space, and what needs to be flagged for further

1008
00:49:54,400 --> 00:49:57,320
Speaker 6: human follow up. All of these things, machine learning is

1009
00:49:57,360 --> 00:49:59,640
Speaker 6: playing an increasingly important role.

1010
00:50:00,040 --> 00:50:01,960
Speaker 1: And in your view, what does a future hold for

1011
00:50:02,160 --> 00:50:05,439
Speaker 1: simulation based science? Are there fields of science that don't

1012
00:50:05,520 --> 00:50:08,200
Speaker 1: yet use any simulation and are on the cusp of

1013
00:50:08,200 --> 00:50:10,319
Speaker 1: being revolutionized by this powerful new tool.

1014
00:50:10,600 --> 00:50:13,320
Speaker 6: I mean, I'm not aware of fields that have shied

1015
00:50:13,360 --> 00:50:16,719
Speaker 6: away from simulation. I think it is such a powerful

1016
00:50:16,760 --> 00:50:19,000
Speaker 6: tool that when you start looking for it, you do

1017
00:50:19,080 --> 00:50:22,920
Speaker 6: find it in use absolutely everywhere. But I think what

1018
00:50:22,960 --> 00:50:25,400
Speaker 6: we can say about simulation is that we're still a

1019
00:50:25,480 --> 00:50:30,080
Speaker 6: very early stage of understanding how to use simulation and

1020
00:50:30,239 --> 00:50:35,000
Speaker 6: what roles it can play within the overall scientific progress. So,

1021
00:50:35,280 --> 00:50:37,120
Speaker 6: you know, it goes all the way back to what

1022
00:50:37,200 --> 00:50:39,759
Speaker 6: really is a simulation? Is it an experiment or is

1023
00:50:39,800 --> 00:50:43,759
Speaker 6: it a calculation? How should we think about it? And

1024
00:50:43,840 --> 00:50:46,600
Speaker 6: tied up with that is this question of how can

1025
00:50:46,640 --> 00:50:49,640
Speaker 6: we improve our simulations? How do we get past that

1026
00:50:49,800 --> 00:50:53,080
Speaker 6: stage of well, we just have to kind of play

1027
00:50:53,120 --> 00:50:56,200
Speaker 6: around with things and tweak them till they fit because

1028
00:50:56,520 --> 00:51:00,719
Speaker 6: of the intrinsic limitations. So I think we're at a

1029
00:51:00,840 --> 00:51:03,600
Speaker 6: very early stage in understanding all of these things. So

1030
00:51:04,520 --> 00:51:07,640
Speaker 6: for certain the role that simulations play is going to

1031
00:51:07,920 --> 00:51:12,240
Speaker 6: change and evolve and I hope improve over the coming years.

1032
00:51:12,600 --> 00:51:15,800
Speaker 1: So if you use the words universe and simulation together

1033
00:51:15,840 --> 00:51:18,200
Speaker 1: in a sentence, that of course evokes in people's minds

1034
00:51:18,239 --> 00:51:21,320
Speaker 1: this conversation that seems to be omnipresent, which is, you

1035
00:51:21,360 --> 00:51:24,080
Speaker 1: know whether or not our universe could be a simulation

1036
00:51:24,400 --> 00:51:27,759
Speaker 1: or the same question sort of when we build artificial

1037
00:51:27,840 --> 00:51:31,360
Speaker 1: universes that have bits and pieces in it, could those

1038
00:51:31,480 --> 00:51:34,799
Speaker 1: universes feel real to those occupants. So in your book,

1039
00:51:34,840 --> 00:51:36,919
Speaker 1: you take a sort of skeptical view of the question

1040
00:51:37,000 --> 00:51:39,600
Speaker 1: of whether we could be living in a simulation. Since

1041
00:51:39,600 --> 00:51:42,440
Speaker 1: you're an expert on simulating universes, what are your arguments

1042
00:51:42,480 --> 00:51:44,800
Speaker 1: against the concept that we could be living in a simulation.

1043
00:51:45,160 --> 00:51:47,560
Speaker 6: Yeah, I think the primary argument is just to look

1044
00:51:47,560 --> 00:51:51,040
Speaker 6: at the complexity of the universe that we're in. So

1045
00:51:51,120 --> 00:51:54,440
Speaker 6: you can actually calculate something called the number of cubits,

1046
00:51:54,600 --> 00:51:57,680
Speaker 6: so basically the number of quantum bits that you would

1047
00:51:57,719 --> 00:52:00,120
Speaker 6: need in a quantum computer if you wanted to do

1048
00:52:00,640 --> 00:52:03,680
Speaker 6: according to all the physics we know so far, if

1049
00:52:03,680 --> 00:52:07,400
Speaker 6: you wanted to do a perfect simulation of our universe,

1050
00:52:07,760 --> 00:52:10,120
Speaker 6: and that is a vast number. I forget it off

1051
00:52:10,160 --> 00:52:11,400
Speaker 6: the top of my head. I think it's something like

1052
00:52:11,440 --> 00:52:13,920
Speaker 6: ten to the one hundred and twenty four cubits.

1053
00:52:13,920 --> 00:52:16,960
Speaker 3: It's something like that. It's a very very large number.

1054
00:52:17,280 --> 00:52:19,879
Speaker 6: And right now, you know, we struggle even to make

1055
00:52:19,920 --> 00:52:23,600
Speaker 6: a single cubit in a quantum computer. It's a vast

1056
00:52:23,640 --> 00:52:26,879
Speaker 6: extrapolation from where we are now to the idea that

1057
00:52:27,400 --> 00:52:31,440
Speaker 6: we will routinely be able to run simulations that have

1058
00:52:31,560 --> 00:52:35,480
Speaker 6: the same kind of richness as the reality that we

1059
00:52:35,560 --> 00:52:36,359
Speaker 6: currently live in.

1060
00:52:36,640 --> 00:52:40,360
Speaker 3: But even more than that. If you ask, well, you know, what.

1061
00:52:40,280 --> 00:52:44,600
Speaker 6: Resources would you need to build a computer that really

1062
00:52:44,640 --> 00:52:49,200
Speaker 6: had that level of capability, Well, it turns out you

1063
00:52:49,239 --> 00:52:53,120
Speaker 6: would need to make use of the entire universe just

1064
00:52:53,160 --> 00:52:59,000
Speaker 6: to simulate one universe. Because physics puts limitations on information processing.

1065
00:52:59,080 --> 00:53:02,960
Speaker 6: You can't process as much information as you like. There

1066
00:53:02,960 --> 00:53:06,080
Speaker 6: are limitations placed on it according to the physical system

1067
00:53:06,120 --> 00:53:08,719
Speaker 6: that it's being processed by, and so these come back

1068
00:53:08,760 --> 00:53:12,120
Speaker 6: to bite you. So and that's what gives rise to

1069
00:53:13,120 --> 00:53:16,080
Speaker 6: this claim that you can only simulate the whole universe

1070
00:53:16,520 --> 00:53:20,280
Speaker 6: perfectly if you have access to the entire physical resources

1071
00:53:20,560 --> 00:53:24,480
Speaker 6: of that universe. Now, there are lots of further objections

1072
00:53:24,520 --> 00:53:27,120
Speaker 6: you can raise. You can say, well, what about, for example,

1073
00:53:27,400 --> 00:53:32,000
Speaker 6: if the higher up universe where we're being simulated is

1074
00:53:32,160 --> 00:53:35,960
Speaker 6: just a much bigger universe with many more cubits at

1075
00:53:35,960 --> 00:53:40,240
Speaker 6: their disposal, and so these resources seem terribly trivial maybe

1076
00:53:40,280 --> 00:53:43,120
Speaker 6: to the people in the higher up universe. But at

1077
00:53:43,120 --> 00:53:45,720
Speaker 6: that point I kind of lose patience with the argument,

1078
00:53:45,880 --> 00:53:48,839
Speaker 6: because it seems to me, at that point we it's

1079
00:53:48,920 --> 00:53:52,040
Speaker 6: no longer a sort of obvious extrapolation from where we are.

1080
00:53:52,080 --> 00:53:56,479
Speaker 6: Now you're now talking about hypothesizing beings with so much

1081
00:53:56,520 --> 00:54:00,640
Speaker 6: more power and so much more technological capability than we have,

1082
00:54:01,160 --> 00:54:03,920
Speaker 6: that we might as well just go and talk about religion,

1083
00:54:04,120 --> 00:54:07,080
Speaker 6: because at this point it's lost contact, in my view,

1084
00:54:07,200 --> 00:54:08,320
Speaker 6: with any science.

1085
00:54:09,760 --> 00:54:12,880
Speaker 1: Wonderful. Well, I really enjoyed your book, And a question

1086
00:54:12,960 --> 00:54:15,839
Speaker 1: I always have for folks who write popular science books

1087
00:54:16,000 --> 00:54:20,080
Speaker 1: about very technical topics is why what do you think

1088
00:54:20,120 --> 00:54:22,840
Speaker 1: that the general public, folks out there who are not

1089
00:54:23,320 --> 00:54:26,480
Speaker 1: simulation experts need to know about simulation?

1090
00:54:27,120 --> 00:54:28,560
Speaker 3: I think there were two reasons.

1091
00:54:28,640 --> 00:54:31,360
Speaker 6: The first was it was amazing to me that nobody

1092
00:54:31,440 --> 00:54:34,760
Speaker 6: had written about this topic before because it's so central

1093
00:54:34,960 --> 00:54:38,239
Speaker 6: in our field of cosmology, and you know, cosmology is

1094
00:54:38,280 --> 00:54:40,719
Speaker 6: something that people do talk about a lot. There's a

1095
00:54:40,760 --> 00:54:43,120
Speaker 6: lot of it out there in the media and in books,

1096
00:54:43,680 --> 00:54:46,120
Speaker 6: because it's genuinely you know, it's exciting, and I think

1097
00:54:46,239 --> 00:54:48,840
Speaker 6: it speaks to all of us about where we came from.

1098
00:54:49,239 --> 00:54:51,799
Speaker 6: And so it seems a real surprise to me that

1099
00:54:51,920 --> 00:54:55,280
Speaker 6: this central tool in how we're learning about the universe

1100
00:54:55,760 --> 00:54:57,880
Speaker 6: was not being written about, and so it felt to

1101
00:54:57,880 --> 00:55:00,160
Speaker 6: me like it needs to be rectified. We need to

1102
00:55:00,160 --> 00:55:04,000
Speaker 6: be talking about this because it offers immense strengths, you know,

1103
00:55:04,400 --> 00:55:08,880
Speaker 6: real enormous strengths that I think are kind of hidden

1104
00:55:08,880 --> 00:55:12,040
Speaker 6: away sometimes. But it also comes with lots of caveats,

1105
00:55:12,040 --> 00:55:14,960
Speaker 6: some of them we've discussed. You know that we can't

1106
00:55:15,040 --> 00:55:18,040
Speaker 6: do these perfect recreations of the universe. There are lots

1107
00:55:18,080 --> 00:55:21,319
Speaker 6: of approximations. We might be making mistakes, and you know,

1108
00:55:21,600 --> 00:55:23,439
Speaker 6: there's no way to rule that out. It's just part

1109
00:55:23,440 --> 00:55:26,440
Speaker 6: of the scientific process that we have to keep an

1110
00:55:26,440 --> 00:55:29,399
Speaker 6: open mind about these things. So I wanted to write

1111
00:55:29,440 --> 00:55:32,120
Speaker 6: about that in a kind of open and honest way,

1112
00:55:32,640 --> 00:55:36,600
Speaker 6: rather than sort of leaving the simulations as black boxes

1113
00:55:36,640 --> 00:55:40,759
Speaker 6: that seemingly recreate our universe through some process of magic. No,

1114
00:55:40,880 --> 00:55:44,840
Speaker 6: it's a very human process and it's got all of

1115
00:55:44,840 --> 00:55:47,719
Speaker 6: the strengths and weaknesses that come with being that kind

1116
00:55:47,760 --> 00:55:48,680
Speaker 6: of human process.

1117
00:55:49,080 --> 00:55:53,000
Speaker 1: Wonderful. Well, thank you very much again. Professor Andrew Hanson

1118
00:55:53,000 --> 00:55:55,479
Speaker 1: at University of College London and author of the book

1119
00:55:55,680 --> 00:55:59,080
Speaker 1: The Universe in a Box Out now encourage everyone to

1120
00:55:59,160 --> 00:56:03,239
Speaker 1: go out and about how science is actually done. Thanks

1121
00:56:03,280 --> 00:56:05,040
Speaker 1: again very much Andrew for joining us today.

1122
00:56:05,719 --> 00:56:07,600
Speaker 2: All Right, did you feel you had a real conversation

1123
00:56:07,680 --> 00:56:10,520
Speaker 2: with him, Like, did things get real or was it

1124
00:56:10,560 --> 00:56:12,279
Speaker 2: all just simulated pleasantries?

1125
00:56:12,480 --> 00:56:17,520
Speaker 1: I was able to simulate enjoying a conversation. Yeah. Oh

1126
00:56:17,680 --> 00:56:20,040
Speaker 1: that's sort of always my situation though, because I'm kind

1127
00:56:20,080 --> 00:56:23,200
Speaker 1: of an introvert, so I have to simulate enjoying human interactions.

1128
00:56:23,480 --> 00:56:26,359
Speaker 2: Yet you have to simulate being engaging human being.

1129
00:56:27,040 --> 00:56:29,800
Speaker 1: I'm trying to quietly slip unnoticed into human society.

1130
00:56:29,920 --> 00:56:31,960
Speaker 2: So you are a simulation, Daniel.

1131
00:56:32,719 --> 00:56:34,040
Speaker 1: I'm simulating being a human.

1132
00:56:34,200 --> 00:56:37,000
Speaker 2: But anyways, that was a great conversation. What's your main

1133
00:56:37,040 --> 00:56:37,719
Speaker 2: takeaway from it?

1134
00:56:37,760 --> 00:56:40,440
Speaker 1: To me, it's fascinating that so recently in the history

1135
00:56:40,480 --> 00:56:43,120
Speaker 1: of science, just the last few decades, we've developed this

1136
00:56:43,200 --> 00:56:46,560
Speaker 1: crucial new tool that we now think is indispensable. It

1137
00:56:46,600 --> 00:56:49,160
Speaker 1: makes me wonder in fifty years and one hundred years,

1138
00:56:49,200 --> 00:56:52,040
Speaker 1: what new branch of science or what new tool we're

1139
00:56:52,080 --> 00:56:55,480
Speaker 1: going to add to our toolbox which future scientists will

1140
00:56:55,480 --> 00:56:58,600
Speaker 1: think is indispensable, And we'll wonder, like, how did Daniel

1141
00:56:58,680 --> 00:57:01,360
Speaker 1: and folks who lived way back then do any science

1142
00:57:01,400 --> 00:57:01,839
Speaker 1: without it?

1143
00:57:02,080 --> 00:57:05,040
Speaker 2: Or maybe even like why did Daniel do science? Why

1144
00:57:05,040 --> 00:57:09,480
Speaker 2: didn't they choose use the AI physicists to answer all

1145
00:57:09,480 --> 00:57:09,960
Speaker 2: the questions?

1146
00:57:10,040 --> 00:57:12,080
Speaker 1: Yeah, stay in bed and let the AIS do the work.

1147
00:57:12,480 --> 00:57:14,560
Speaker 1: The answer to that is the AIS wan't answer questions

1148
00:57:14,560 --> 00:57:17,800
Speaker 1: the AIS are interested in and science is for people,

1149
00:57:17,880 --> 00:57:20,080
Speaker 1: by people, and of people, So you got to have

1150
00:57:20,160 --> 00:57:21,360
Speaker 1: people asking questions.

1151
00:57:21,480 --> 00:57:23,200
Speaker 2: Wait, wait, do you mean the AIS won't always do

1152
00:57:23,240 --> 00:57:24,080
Speaker 2: what we ask them to do.

1153
00:57:25,920 --> 00:57:28,880
Speaker 1: They'll always do exactly what we ask them to do,

1154
00:57:28,920 --> 00:57:30,640
Speaker 1: just not and maybe in the way that we want

1155
00:57:30,680 --> 00:57:30,920
Speaker 1: them to.

1156
00:57:31,320 --> 00:57:34,680
Speaker 2: All right, Well, stay tuned to see if we are

1157
00:57:34,680 --> 00:57:37,680
Speaker 2: in a simulation right now? Maybe I guess some people

1158
00:57:37,760 --> 00:57:39,480
Speaker 2: consider the whole universe to be service.

1159
00:57:39,240 --> 00:57:41,280
Speaker 1: Simulation, right, how can we possibly know?

1160
00:57:41,640 --> 00:57:43,360
Speaker 2: Yeah, Like, what's the difference if.

1161
00:57:43,200 --> 00:57:45,960
Speaker 1: We are in a vast alien simulation. Then I definitely

1162
00:57:46,000 --> 00:57:47,880
Speaker 1: want to talk to those aliens because they have some

1163
00:57:48,040 --> 00:57:49,600
Speaker 1: awesome computers.

1164
00:57:49,360 --> 00:57:52,200
Speaker 2: And also to stay tuned about what new developments humans

1165
00:57:52,200 --> 00:57:56,240
Speaker 2: are going to discover using simulations and or artificial intelligence.

1166
00:57:56,480 --> 00:57:58,600
Speaker 2: So we hope you enjoyed that. Thanks for joining us,

1167
00:57:59,280 --> 00:58:00,000
Speaker 2: see you next time.

1168
00:58:07,800 --> 00:58:10,600
Speaker 1: Thanks for listening, and remember that Daniel and Jorge Explain

1169
00:58:10,680 --> 00:58:14,680
Speaker 1: the Universe is a production of iHeartRadio. For more podcasts

1170
00:58:14,680 --> 00:58:19,320
Speaker 1: from iHeartRadio, visit the iHeartRadio app, Apple Podcasts, or wherever

1171
00:58:19,400 --> 00:58:21,120
Speaker 1: you listen to your favorite shows.