Speaker 1: Hey, am I speaking to the real Joorge today?
Speaker 2: Who else could it be?
Speaker 1: I don't know. It could be the simulated Jorge or
Speaker 1: an AI generated horheage GPT.
Speaker 2: Yes, chat Jorge. Would it make a difference.
Speaker 1: That sounds like something the simulated Jorge would say?
Speaker 2: Mmm. I kind of wish I had simulated Horges. Then
Speaker 2: I might avoid a lot of mistakes I make. Or
Speaker 2: they could do all the work while I sleep in.
Speaker 1: Why do you think simulated Hojoges are less likely to
Speaker 1: make mistakes?
Speaker 2: No? I mean they would do the mistakes, and then
Speaker 2: I would learn from them. That's the idea.
Speaker 3: Right.
Speaker 1: That does sound useful, But I think you have to
Speaker 1: be careful about the sim Jorges organizing and rising up
Speaker 1: against you.
Speaker 3: Oh do you think.
Speaker 2: They would form their own union or a revolt?
Speaker 1: Do you mean, yeah, either mutiny or fair wages. Either one.
Speaker 2: Well, I could just pay them in simulated money.
Speaker 1: I guess as long as they can use that to
Speaker 1: feed their simulated children, I bet they'd be happy. Oh.
Speaker 2: No, I definitely provide simulated benefits too. The whole simulated
Speaker 2: family gets a bonus.
Speaker 1: You're not a good employer, but you can simulate one.
Speaker 2: Hi, I'm Jory mccartoonists and the author of Oliver's Great
Speaker 2: Big Universe. Hi.
Speaker 3: I'm Daniel.
Speaker 1: I'm a particle physicist and a professor at UC Irvine,
Speaker 1: and I am constantly simulating crazy conditions.
Speaker 2: You mean in your life or in your work.
Speaker 1: Well, work is a big part of my life. But yeah,
Speaker 1: my job involves simulating collisions at very high energies all
Speaker 1: the time.
Speaker 2: And you also actually do them, right, You actually collide
Speaker 2: things at the Large Hadron Collider.
Speaker 1: That's right. We both collide particles together in real life,
Speaker 1: and we simulate what would happen if we collided particles
Speaker 1: under various different potential laws of the universe to see
Speaker 1: what might happen. Will the Earth get gobbled up with
Speaker 1: to create a black hole that destroys the Earth or not?
Speaker 1: Let's find out.
Speaker 2: Does that mean your whole career is a simulation or
Speaker 2: your whole life?
Speaker 1: You know, our computers are not fast enough to keep
Speaker 1: up with reality. So while we generate lots and lots
Speaker 1: of simulated collisions, the real collider has generated more collisions
Speaker 1: than we could ever simulate.
Speaker 2: But how do you know, Daniel, that we're not in
Speaker 2: a simulation right now, Like you might think you're doing experiments,
Speaker 2: but really you're just inside of a video game somewhere.
Speaker 1: Well, I want to find the cheap goods, then, well I.
Speaker 2: Think if you had found them by now, you probably
Speaker 2: would have a noble price.
Speaker 1: Right, I'm hoping smashing particles together gives me the cheek goods.
Speaker 2: And then that opens up the real boss level.
Speaker 1: Where I fight the simulated army of Jorges.
Speaker 2: No, you fight the real Jegeo. That's the real boss.
Speaker 2: But anyways, welcome to our podcast Daniel and Jorge Explain
Speaker 2: the Universe, a production of iHeartRadio.
Speaker 1: In which we use our tiny little minds to try
Speaker 1: to understand the vast universe. We hope to build in
Speaker 1: your head a simulation of sorts, one that describes the
Speaker 1: way the real universe works out there. We hope to
Speaker 1: encode into your brain some laws of physics that will
Speaker 1: help you understand how the real universe out there is
Speaker 1: smashing and bashing to create our Bonker's reality.
Speaker 2: That's right, the universe are doing all kinds of amazing
Speaker 2: and awe sometimes and saying things out there in reality.
Speaker 2: And so it's our job as humans and as scientists
Speaker 2: to understand what's going on and to ask questions, to
Speaker 2: probe into the true answers to why things are the
Speaker 2: way they are.
Speaker 1: And the classical way that science does this is with
Speaker 1: theories and experiments, hypotheses and tests. You have an idea,
Speaker 1: you go and see out there in the universe. If
Speaker 1: it works, you predict something happens, and you go and
Speaker 1: check to see if it does. But the modern scientific
Speaker 1: method has a third way, which lives sort of uncomfortably
Speaker 1: between theory and experiments.
Speaker 2: Oh, why is it uncomfortable? Is it like uncomfortable, awkward
Speaker 2: or uncomfortable, like physically uncomfortable.
Speaker 1: It's a little bit uncomfortable for those of us who
Speaker 1: specialize in not to know where we fit into the
Speaker 1: picture of science. Some people consider me experimental physicists. Some
Speaker 1: people are like, nah, he mostly runs simulation, so he's
Speaker 1: really a theorist. So you can be sort of like
Speaker 1: uncomfortably between two different communities. If you do a lot
Speaker 1: of simulations.
Speaker 2: You start your own simulated community. Can you be like
Speaker 2: a simulating physicist.
Speaker 1: A simulator a simulate trist. That sounds not safe for work.
Speaker 2: Actually, I don't know what you mean, but I'll take
Speaker 2: your word for it.
Speaker 1: You know, academic communities change pretty slowly, and for example,
Speaker 1: in departments of physics, people tend to hire people that
Speaker 1: are like them. The experimentalists get to hire somebody. They
Speaker 1: want to hire somebody who's a blue blooded experimentalist down
Speaker 1: to the core. So if you work at the intersection
Speaker 1: of fields, you do some experiments, you do some theory,
Speaker 1: maybe even do some computer science and machine learning, then
Speaker 1: you don't necessarily have a home, you don't have a
Speaker 1: tribe that's going to go to bat for you to
Speaker 1: get hired. So it's sort of about the sociology of
Speaker 1: science as a real practice.
Speaker 2: Well, I think that kind of makes sense, right, Like
Speaker 2: why hire a simulating physicist when you can just simulate one?
Speaker 2: Why go to all the trouble?
Speaker 1: You know, I think that's true. If we could simulate physicists,
Speaker 1: we could get a lot more done. But we're not
Speaker 1: quite there yet. Human physicists still have a little bit
Speaker 1: of an edge.
Speaker 2: Yeah, maybe till next week when chat GPT catches up
Speaker 2: and starts doing physics.
Speaker 1: I mean, have you ever asked chat GPT a physics question.
Speaker 1: You don't get physics out, that's for sure.
Speaker 2: But anyways, it is an interesting universe because I guess
Speaker 2: sometimes there are questions. You can't just go out there
Speaker 2: and try for yourself in the universe, right, that's right.
Speaker 1: Simulation has emerged in the past fifty years as an
Speaker 1: extraordinarily powerful tool as a little bit of experiment and
Speaker 1: a little bit of theory, and it lets us answer
Speaker 1: questions that we otherwise could not answer. It really is
Speaker 1: a completely new tool in the science tool.
Speaker 2: Belt, although I would argue it's maybe one of the
Speaker 2: oldest tools in science. And so today on the podcast,
Speaker 2: we'll be asking the question why do scientists do simulations?
Speaker 2: Why do scientists do anything.
Speaker 1: Other than the obvious that simulations are so much fun.
Speaker 1: You get to build your own little universe. You are
Speaker 1: the creator and god of that simulated universe.
Speaker 2: Oh boy, is that that the ultimate goal? There? To
Speaker 2: be gods? No, you know, you don't have to get
Speaker 2: a degree for that. You could just buy some legos.
Speaker 1: Oh, I've been doing that since I was a little kid.
Speaker 1: I just want more and more powerful simulations.
Speaker 2: You want more powerful legos, smaller legos.
Speaker 1: Jokes aside, There is a real sense of power when
Speaker 1: you create a simulated universe, because you are deciding what
Speaker 1: the laws of physics are in that universe, what particles
Speaker 1: do they have, how do they interact? And then you
Speaker 1: get to see how it all plays out.
Speaker 2: Mmmm yeah, I sort of get that. I mean I
Speaker 2: write a lot, I create characters, and I sort of
Speaker 2: build my own world. What's the difference.
Speaker 1: Yeah, you could think of fiction as simulated human interaction
Speaker 1: and lives. Right, we're exploring what it would be like
Speaker 1: to be in those situations.
Speaker 2: Yeah. Wait, did you just say your work.
Speaker 1: Is simulations are definitely fiction. Sometimes they align with reality,
Speaker 1: and one deep question is how well they align? What
Speaker 1: lessons you can learn from your simulated fiction that carry
Speaker 1: over into the real world.
Speaker 2: Interesting, So your research is science fiction is what you're saying.
Speaker 1: You know, I've always argued that there's a strong connection
Speaker 1: between science and science fiction.
Speaker 3: Right.
Speaker 1: One aspect of science is like, well, what are the laws?
Speaker 3: Could they be?
Speaker 1: This?
Speaker 3: Could they be?
Speaker 2: That?
Speaker 1: There's an element of creativity and exploration there, absolutely so. Yeah,
Speaker 1: I'm constantly creating science fiction universes and trying to see
Speaker 1: if they line up with ours.
Speaker 2: And then you wonder why the other physicis don't want
Speaker 2: to play with you.
Speaker 1: Fortunately I got tenured before I revealed all of these
Speaker 1: crazy instincts. That's the game.
Speaker 2: Well, this is an interesting question, and so as usual,
Speaker 2: we were wondering how many people out there had thought
Speaker 2: about why scientists do the things they do, and in particular,
Speaker 2: why they do simulations in their work.
Speaker 1: So thanks very much to everybody who answers these questions
Speaker 1: for this fun segment of the podcast, one of my favorites.
Speaker 1: If you like to join the team or just answer
Speaker 1: one or two questions right to us to questions at
Speaker 1: Danielandhorge dot com.
Speaker 2: So think about it for a second. If someone asks
Speaker 2: you why scientists do simulations, what would you say.
Speaker 4: Well, using simulations, we can observe scenarios in our models
Speaker 4: that we can't necessarily observe in real life, and see
Speaker 4: what can happen in certain situations like in a black
Speaker 4: hole or when galaxies collide or something like that.
Speaker 5: Scientists do simulations because the universe is really old, really big,
Speaker 5: sometimes really destructive. Frankly, I'm happy they do a lot
Speaker 5: of that modeling and simulations and don't necessarily try to
Speaker 5: create big bang conditions on a big scale, or the
Speaker 5: explosion of stars or something.
Speaker 2: Couple of interesting answers did anyone look at you funny
Speaker 2: when you ask them the question.
Speaker 1: I don't know. These were all on the internet, so
Speaker 1: I couldn't capture their facial expressions. Did they send an emoji?
Speaker 1: But I do sense some relief in there that, for example,
Speaker 1: we are trying to simulate galaxy collisions rather than trying
Speaker 1: to arrange galaxy collisions.
Speaker 2: Well, if we could do that, that'd be pretty cool.
Speaker 2: I mean, not for those galaxies, but just to have
Speaker 2: that power.
Speaker 1: Yeah, you'd have to have like sign offs from every
Speaker 1: alien civilization in both galaxies before you could even begin.
Speaker 2: I guess that would be the polite thing to do. Yes.
Speaker 2: But anyways, as you were saying, this is a big
Speaker 2: part of how science is done these days, and so Daniel,
Speaker 2: I guess let's start from the basics. What is a
Speaker 2: simulation in your view as a physicists.
Speaker 1: So, a simulation, or more specifically, a computer simulation, is
Speaker 1: a specific program that involves a scientific model. A model
Speaker 1: is like our picture of how the world might work.
Speaker 1: It's like a simplified version of the real universe. Let's
Speaker 1: us explore a specific question, and a simulation is usually
Speaker 1: a program on a computer that uses like step by
Speaker 1: step methods to explore the behavior of that model.
Speaker 2: And usually this model has the form of an equation, right, Like,
Speaker 2: for example, F equalsma is a model of the world, right,
Speaker 2: and how things move in the world.
Speaker 1: Yeah, the science we do is mathematical, and the way
Speaker 1: we describe things is mathematical, and so usually that involves equations,
Speaker 1: equations that represent constraints on the model, like the way
Speaker 1: things have to happen. And as you say, F equals
Speaker 1: ma is a model. If I want to toss a
Speaker 1: baseball across my backyard, I want to answer the question
Speaker 1: where is it going to land? Then I have lots
Speaker 1: of possible ways to answer that question, but the most
Speaker 1: appropriate ways to make the simplest model possible that still
Speaker 1: captures everything that I'm interested in, and so often in
Speaker 1: our world, like when we're tossing baseballs, we can do
Speaker 1: something pretty simple just F equals ma, which ignores all
Speaker 1: sorts of swarming quantum details about what's happening inside at
Speaker 1: the baseball and just describes simple motion of a parabole.
Speaker 2: Yeah, it's almost like you. I mean, as a scientist,
Speaker 2: you're trying to come up with the rules of the universe, right,
Speaker 2: that's sort of the goal of science, right, And what
Speaker 2: sometimes that rule looks like is in a question that says,
Speaker 2: you know, if you have a mass and you apply
Speaker 2: a force to it, then it's going to start moving
Speaker 2: with a certain acceleration.
Speaker 1: Exactly in your words, these are all science fictions. We're
Speaker 1: living in this world and we're wondering what are the rules,
Speaker 1: and so we're trying a bunch of different rules, saying
Speaker 1: does this rule describe our universe? Does that rule describe
Speaker 1: our universe? So every sort of theoretical exploration of the
Speaker 1: universe involves building a model and then asking the question
Speaker 1: does that model align with the reality that we see.
Speaker 1: Computer simulations are a special kind of model or a
Speaker 1: special what a test? Really complicated models that we can't
Speaker 1: otherwise test, Like the model F equals M a pretty simple.
Speaker 1: I can use pencil and paper to make predictions, and
Speaker 1: then I can throw a ball in my backyard to
Speaker 1: confirm those predictions.
Speaker 2: Right, because I guess F equals A has like a
Speaker 2: mathematical But I think the idea is that you take
Speaker 2: in a question like F equals in May and you
Speaker 2: basically program that into a computer and say, you know,
Speaker 2: any masses in this program, they have to move according
Speaker 2: to this law.
Speaker 1: That's right. If, for example, I don't just want to
Speaker 1: describe one ball, but I want to describe like ten
Speaker 1: to the twenty five balls, right, some huge number of balls.
Speaker 1: Maybe I'm modeling an ideal gas, or like a swimming
Speaker 1: pool full of ping pong balls or something, and I
Speaker 1: want to describe that. Then I can no longer use
Speaker 1: pencil and paper. But you're in. I can take those
Speaker 1: equations and put them into a computer and ask the
Speaker 1: computer to force those balls to follow that equation, and
Speaker 1: then I could see what happens. It's sort of like
Speaker 1: a virtual experiment.
Speaker 2: Yeah, it's like you're creating your own little universe.
Speaker 1: Right exactly. And this becomes super essential when we don't
Speaker 1: have like a single equation that describes everything, Like we
Speaker 1: don't have a solution to what happens when you put
Speaker 1: ten to the twenty five ping pong balls into a
Speaker 1: swimming pool. We just don't know how to do that
Speaker 1: calculation to come up with some nice summary of the results.
Speaker 1: What we can do is put it into a computer
Speaker 1: and have the computer step it forward in time very carefully,
Speaker 1: and we can see what happens without ever actually having
Speaker 1: to buy that number of ping bomb balls.
Speaker 2: Right. That's sort of the power of the computer, right,
Speaker 2: Like you can simulate one ball, which is a calculator, right,
Speaker 2: Like you can say after one second, it's going to
Speaker 2: be here, after two seconds, it is going to be
Speaker 2: here by following these rules. But if you have, like
Speaker 2: you said, a whole bunch of balls, or a more
Speaker 2: complicated system, then a computer can sort of do all
Speaker 2: those calculations for you faster, exactly.
Speaker 1: One huge advantage is tackling a very large number of objects,
Speaker 1: and the other is when we don't have the equations,
Speaker 1: we don't know how to solve them, Like for f equals,
Speaker 1: I may we know how to solve that. Technically, that
Speaker 1: is a differential equation because A is a second derivative
Speaker 1: of position, right, there's derivatives on both sides. And in
Speaker 1: general and mathematics, differential equations are very very hard to solve.
Speaker 1: This a small number that we actually know how to solve.
Speaker 1: So sometimes you have a system that's described by a
Speaker 1: differential equation you don't know how to solve, Like fluid flow,
Speaker 1: for example, described by the Navier Stokes equation. We don't
Speaker 1: know how to solve that. In general. But what we
Speaker 1: can do on a computer is approximated. You can say,
Speaker 1: you know, let's just move it forward in time, not
Speaker 1: a year or a minute or some long period of time,
Speaker 1: but just like a microsecond, and across a microsecond, we
Speaker 1: can make some approximations. We can say, let's not use
Speaker 1: the full equation, let's simplify it and take some like
Speaker 1: linear approximation of it, and then if we take a
Speaker 1: lot of tiny little steps, we hope that we roughly
Speaker 1: get the right answer.
Speaker 2: Right, because I think, as you were saying, like something
Speaker 2: like f EQUOSM has the solution, meaning that you can
Speaker 2: derive a formula for like the precision of your ball
Speaker 2: at all times, where you can just like after three
Speaker 2: seconds you just put the time in and it gives
Speaker 2: you the position of the ball, right, because you can
Speaker 2: integrate that equation and find the solution. But some equations
Speaker 2: you can, like they're so complex you can get a
Speaker 2: formally that will tell you what's going to happen ten
Speaker 2: years from now or twenty years from now. Right, those
Speaker 2: you need to do little steps by little steps exactly.
Speaker 1: And the crucial idea there is that you're making a
Speaker 1: linear approximation you're taking the full equation which you don't
Speaker 1: know how to solve, and you're saying, well, let's replace
Speaker 1: it with an approximate version of it, which is not
Speaker 1: going to be correct, but it might be correct for
Speaker 1: like a microsecond. And so we'll use the approximate linear
Speaker 1: version of that that we do know how to solve
Speaker 1: from a tiny little step, and then we'll start again,
Speaker 1: and we'll make another tiny little step, and we hope
Speaker 1: them little mistakes cancel out and don't build up into
Speaker 1: some big overall mistake.
Speaker 2: Right Yeah. It's almost like if you take small enough steps,
Speaker 2: then you're less likely to deviate from the reality of.
Speaker 1: It, right, Yeah, exactly. And people who do approximations know
Speaker 1: that there's lots of times this is useful. Like you
Speaker 1: want to calculate Trigg function like sign sign is really
Speaker 1: hard to calculate like sort of from scratch, but for
Speaker 1: very small values of the angle, sign of x is
Speaker 1: just equal to x, you can like approximate this complicated
Speaker 1: function with a simple one. It mostly gets the right answer.
Speaker 1: That's just one example.
Speaker 2: I'm not sure you're going to Trigonometry usually makes things
Speaker 2: you understand, but I think I think we get the idea,
Speaker 2: which is that you know, if you take small enough
Speaker 2: steps and you you sort of a simplified version of
Speaker 2: your model, then you're less likely to make mistakes.
Speaker 1: Exactly. You can't trust those approximations forward a second or
Speaker 1: a minute or a year, but you could trust them
Speaker 1: like a microsecond. And so you have the simulated universe
Speaker 1: in your computer. You feed in the initial conditions, and
Speaker 1: then you ask it to take a step forward in time,
Speaker 1: and you ask it to take another step forward, and
Speaker 1: if you have enough computing power, you can run it
Speaker 1: for a while and you can see what happens to
Speaker 1: all my ping pong balls in my simulated swimming pool,
Speaker 1: or what happens to my galaxy as the stars all
Speaker 1: swirl around each other.
Speaker 2: Right, Like you were saying, like fluids are notoriously really
Speaker 2: hard to solve as an equation, right, These are really
Speaker 2: complex equations that govern what's going on because they sort
Speaker 2: of like depend on a lot of things. Like there's
Speaker 2: a lot going on, right, there's time, and then there's
Speaker 2: distance and the velocity of things and all those factor
Speaker 2: in that's hard or impossible to like predict exactly what's
Speaker 2: going to happen in the future exactly.
Speaker 1: And the big complication there is the interactions. Like back
Speaker 1: to the ping pong balls. If you just had a
Speaker 1: lot of ping pong balls and they're all flying around
Speaker 1: but not touching each other, it wouldn't be that hard
Speaker 1: to calculate what's going to happen to each one, But
Speaker 1: as soon as they start banging against each other, becomes
Speaker 1: much much more complicated because the solution of ping pong
Speaker 1: ball number six hundred and forty two now depends on
Speaker 1: ping pong ball number one, one hundred and eleven and
Speaker 1: every other ping pong ball, so becomes much more complicated.
Speaker 1: And that's why fluids are so complicated, because every sheet
Speaker 1: of the fluid depends on the friction with the other
Speaker 1: sheet of the fluid. And that's what makes the Navier
Speaker 1: Stokes equation, for example, so intractable.
Speaker 2: Right, And so you take it little by little, and
Speaker 2: so you say, okay, this time, I'm going to ignore
Speaker 2: some of these effects and just take one small step
Speaker 2: to see where all those little molecules go. And then
Speaker 2: you keep repeating that and hopefully it sort of looks
Speaker 2: like the real thing.
Speaker 1: Exactly, and it gives you this incredible power that you
Speaker 1: can hopefully identify emergent behavior. The way we do science
Speaker 1: in our universe is that we like focus on one
Speaker 1: level where we understand things we can describe, like the
Speaker 1: microphysics of how particles being against each other. But sometimes
Speaker 1: we're interested in things at another level, Like you understand
Speaker 1: how rain drops move through the wind, but your real
Speaker 1: question is like is this hurricane going to hit Florida
Speaker 1: or Alabama? And so even if you don't have like
Speaker 1: an equation that describes hurricanes, if you have an equation
Speaker 1: that describes the rain drops, you can feed that all
Speaker 1: into your computer, run simulations, and then get answers to
Speaker 1: your higher level question. You can see like the emergent
Speaker 1: phenomena of the hurricane in simulation.
Speaker 2: Well, it's dig a little bit deeper into how simulation
Speaker 2: works and the things like weather and why scientists use
Speaker 2: simulations to try to learn things about the real universe.
Speaker 2: But first let's take a quick break. All right, we're
Speaker 2: having a simulation of a podcast here, right, We're not
Speaker 2: really having a podcast, right, We're just pretending to have
Speaker 2: a podcast.
Speaker 1: We're simulating the process of injecting ideas into listen in our.
Speaker 2: Minds, and so we're talking about why scientists use simulations,
Speaker 2: and it's kind of, I guess a philosophical question, perhaps
Speaker 2: because doing a simulation of reality is not really reality, right,
Speaker 2: and so, and you're not really experimenting on reality. So
Speaker 2: it's kind of a I guess, a funny thing for
Speaker 2: scientists to do it because you're not really doing experiments
Speaker 2: in the real world. But it's at the same time
Speaker 2: really helpful. Right.
Speaker 1: That's true, But that same criticism could be applied to
Speaker 1: basically everything in science. When we do science, we never
Speaker 1: use all of the full gory details of the universe
Speaker 1: to answer a question. We're always using some stripped down
Speaker 1: version because otherwise it's totally intractable. Like when we do
Speaker 1: F equals M, even for a single ball flying through
Speaker 1: the air, we're ignoring lots of stuff. We're ignoring air resistance,
Speaker 1: we're ignoring quantum effects of the particles inside of it.
Speaker 1: We're ignoring all sorts of things because we don't think
Speaker 1: that they are important. And so every time you build
Speaker 1: a model of the universe, theoretical or simulation, you're always
Speaker 1: making a choice about what to ignore and what to include.
Speaker 2: Well, we talked a lot about what a simulation is right.
Speaker 2: It's a computer program where you program in the rules
Speaker 2: that you think that the world follows the rules of
Speaker 2: the universe, at least in your simulated universe, and then
Speaker 2: you sort of let the computer kind of run this world,
Speaker 2: and then it sort of tells you what may or
Speaker 2: may will sort of happen.
Speaker 1: Exactly, and it lets you examine all sorts of universes
Speaker 1: you don't otherwise have access to, like in my work,
Speaker 1: and let's me answer questions like what would I see
Speaker 1: in our particle detectors if the Higgs boson was this
Speaker 1: kind of particle, or what if there was no Higgs boson,
Speaker 1: or what if it had twice the mass that it had?
Speaker 1: What would we see in our detectors? What would that
Speaker 1: universe be?
Speaker 2: Like?
Speaker 1: What would those experiments result in?
Speaker 2: So it gives you ideas for experiments, or it's a
Speaker 2: sort it can guide your real experiments. Right, that's part
Speaker 2: of the idea.
Speaker 1: Right, It's actually crucial for interpreting our experiments. When we
Speaker 1: look at data from the actual collider and we see
Speaker 1: these splashes of energy here and splashes of energy there,
Speaker 1: and we look at the patterns the correlations. The way
Speaker 1: we interpret those is by comparing them to simulations, we say,
Speaker 1: is this consistent with the higgs boson with these properties
Speaker 1: or is it more consistent with the higgs boson with
Speaker 1: some other properties. So the simulation in some sense defines
Speaker 1: the ideas that we're considering, the various hypotheses that we're
Speaker 1: trying to distinguish between.
Speaker 2: Right, It lets you explore the possibilities. That's the idea
Speaker 2: of a simulation. Right, Let's you maybe make mistakes.
Speaker 1: Absolutely, and before we build a detector, we simulated to
Speaker 1: see like is this going to work or how well
Speaker 1: is it going to perform? Or oops, turns out we
Speaker 1: need to swap the order these two things or nothing's
Speaker 1: going to work. So yeah, making mistakes and simulation is
Speaker 1: much cheaper than making them in reality.
Speaker 2: Yeah, And as you were saying, simulations play a big
Speaker 2: part in weather prediction, right, I mean that's how weather
Speaker 2: predictions work. Like when you look at the weather forecast
Speaker 2: and says it's going to rain tomorrow, it's because some
Speaker 2: big computer out there has basically taken the data from
Speaker 2: today and simulated what's going to happen tomorrow exactly.
Speaker 1: And that's really the origin of computer simulations. People wanted
Speaker 1: to predict the weather, to understand what's going to happen
Speaker 1: to these cloud patterns. But nobody could really do it
Speaker 1: with pen and papers. Too complicated, too many pieces of information,
Speaker 1: and the equations are really just a mess. So meteorology
Speaker 1: is one of the first places where people decided, let's
Speaker 1: code this up on the computer to try to grapple
Speaker 1: with this complexity and see if we can get anything
Speaker 1: right now, just after World War Two when computers were
Speaker 1: first displaying like computational power, and it was the weather
Speaker 1: forecasters and the nuclear physicists that first really jumped on
Speaker 1: this train.
Speaker 2: Yeah, because I think the way the weather works is
Speaker 2: that you have all this data mateiological data, weather data
Speaker 2: across let's say the United States, that tells you the
Speaker 2: wind speeds and the clouds and the pressures and all that,
Speaker 2: and then you can use it and put it into
Speaker 2: basically your computer, which has a model of what should
Speaker 2: happen next if that's you have all these pressures and
Speaker 2: wind patterns exactly.
Speaker 1: And those models are not perfect, they don't describe everything,
Speaker 1: and so they're most reliable over short times because that's
Speaker 1: when the errors are not going to compound as much,
Speaker 1: which is why I like the prediction for how hot
Speaker 1: it's going to be tomorrow is much more reliable than
Speaker 1: the prediction for how hot it's going to be in
Speaker 1: ten years, which you basically have no information about. Or
Speaker 1: if you look at those projections for like where's the
Speaker 1: hurricane going to be, the potential path of the hurricane
Speaker 1: gets wider as the prediction gets further out because there's
Speaker 1: more uncertainty, like is it going to hit Alabama? We
Speaker 1: don't know.
Speaker 2: Yeah, it's pretty cool and actually an interesting fact. I
Speaker 2: would just talk to a hurricane scientist a couple of
Speaker 2: months ago, and he was saying that we're still at
Speaker 2: the point apparently where humans outperform simulations, even supercomputer simulations.
Speaker 1: Humans using pencil and paper, or just humans like with
Speaker 1: their intuition.
Speaker 2: Humans with their intuition. So like, apparently we're still at
Speaker 2: the point where if if you're seeing a hurricane move,
Speaker 2: you run a computer simulation about where it's going to
Speaker 2: go next. A human or like a season experienced hurricane
Speaker 2: watcher will still today better at predicting what the hurricane
Speaker 2: is going to do, just from like what's going on
Speaker 2: inside their brain and the history of what they've seen before.
Speaker 2: But it's getting apparently closer and closer, so maybe in
Speaker 2: the near future computers will make those hurricane watchers. Is
Speaker 2: totally obsleete.
Speaker 1: That's super fascinating and it's fun to think about what's
Speaker 1: going on inside that person's brain. They have built in
Speaker 1: their head some neural network with literal biological neurons, right
Speaker 1: and not your typical artificial neural network that models hurricanes,
Speaker 1: and they've trained it on a bunch of real hurricanes.
Speaker 1: So that's pretty cool.
Speaker 2: Yeah, it makes you wonder if maybe, like in the future,
Speaker 2: they're going to use AIS to predict the weather, maybe
Speaker 2: you don't need a scientific model of what's going on.
Speaker 1: That's a really fascinating question because AIS are already being
Speaker 1: used to help boost simulations. One problem with simulations is
Speaker 1: that they can be very expensive computationally. You have lots
Speaker 1: and lots of rain jops and you want to model
Speaker 1: it very, very accurately. It takes a computer a long
Speaker 1: time to calculate every rain job and move it forward
Speaker 1: in time. You want to predict something a few days out,
Speaker 1: it can be very expensive computationally. We run into this
Speaker 1: problem in particle physics all the time because we want
Speaker 1: to simulate billions and billions of potential collisions, and the
Speaker 1: interactions with the detector are very complicated. So to generate
Speaker 1: one simulated collision, for example, like thirty minutes, even on
Speaker 1: a modern computer, we use AI to boost those to
Speaker 1: make them faster. Essentially, we train machine learning algorithms to
Speaker 1: reproduce what the careful calculations have done. They don't understand it,
Speaker 1: they don't like have the same equations built in. It's
Speaker 1: just sort of like those people watching the examples and
Speaker 1: getting an intuition. This is like a machine learning intuition.
Speaker 2: So now you're not just similar working in a Meida world.
Speaker 2: Now you're in twitting your way through a Meida world.
Speaker 1: Yeah. And one problem is that we don't always know
Speaker 1: if their predictions are accurate or why they make them.
Speaker 1: You can't ask them like why did this go left
Speaker 1: instead of right? They just have an internal model, the
Speaker 1: same way your hurricane watchers probably can't answer detailed questions
Speaker 1: about why they feel it's going this way. They just
Speaker 1: feel it.
Speaker 2: Yeah. And so it also raises these interesting philosophical questions
Speaker 2: about what science is right, Like is it still science
Speaker 2: if you get an AI to predict what's going on.
Speaker 2: Even if you don't understand what the AI did.
Speaker 1: It's a deep question that we're struggling with all the time.
Speaker 1: But with no controversial is that it gives us extraordinary
Speaker 1: power to do things we just couldn't do otherwise. We
Speaker 1: can now run our simulations for much much longer and
Speaker 1: in much more depth. You want to know what's going
Speaker 1: to happen when the Milky Way collides with Andromeda or
Speaker 1: the far future of our universe. Simulations give you that power.
Speaker 1: You don't have to sit around and wait for the
Speaker 1: events to play out. We can test it in simulation.
Speaker 2: Right. That's pretty cool. And so what are the different
Speaker 2: types of simulations that scientists use.
Speaker 1: I would say that the simulation is almost everywhere in science.
Speaker 1: You know, it used to be limited to a few
Speaker 1: computationally complex fields, but now everybody sees how useful it is.
Speaker 1: You know, even big companies like you want to design
Speaker 1: a new airplane and you're considering a few different wing shapes.
Speaker 1: It used to be you have to build prototypes of
Speaker 1: those wing shapes and put them in a real huge
Speaker 1: wind tunnel, very time consuming expensive. Now you can just
Speaker 1: simulate the wind tunnel and get an idea for which
Speaker 1: wing shape is going to work. You can explore thousands
Speaker 1: of different shapes simultaneously, so that can be very very powerful.
Speaker 1: So I think simulations are essentially everywhere in science now.
Speaker 2: Well, I think they've been using simulations in things like
Speaker 2: aerospace for a long time, right, Like even I'm thinking
Speaker 2: in the space program in the fifties and sixties. I mean,
Speaker 2: they didn't use physical computers, but they use people computers
Speaker 2: to sort of simulate what the trajectories of the spacecraft
Speaker 2: were going to be, right.
Speaker 1: They definitely used human brains to do those calculations. Whether
Speaker 1: you consider that a simulation, I think as a tricky point.
Speaker 1: Is that just a theoretical calculation which people have been doing,
Speaker 1: you know since Galileo or Francis Bacon or whatever. Is
Speaker 1: it actually a simulation?
Speaker 2: I don't know that. That's a tough question, all right,
Speaker 2: So then what are some of the other types of
Speaker 2: simulations people do, or what are some other ways that
Speaker 2: physicists use simulations.
Speaker 1: Another way they use them is to observe things that
Speaker 1: they otherwise couldn't see, Like we want to know what's
Speaker 1: going on inside the sun. Well, we have really no
Speaker 1: prospects for actually seeing what's going on inside the Sun.
Speaker 1: But we can build a simulation of the inside of
Speaker 1: the Sun, and that's going to make predictions for things
Speaker 1: that we can see, things happening on the surface of
Speaker 1: the Sun, or the number of neutrinos coming to Earth,
Speaker 1: and that helps us get an understanding for what's really
Speaker 1: happening inside the Sun. And in the simulation, you're not limited, right,
Speaker 1: you can ask questions about anything that's happening, like what
Speaker 1: is the temperature or the core of the sun, what
Speaker 1: is the velocity of the plasma. So often simulations always
Speaker 1: checked by real experiments in places where we can observe
Speaker 1: them give us access to things that we can't otherwise observe.
Speaker 2: I think that's a crucial step in this process, right,
Speaker 2: Like you can come up with this imaginary world in
Speaker 2: your theater, but it has to match sort of what
Speaker 2: you see at the end with reality, right absolutely.
Speaker 1: Otherwise it's just science fiction, which you know has its
Speaker 1: own value. But there is a special interest in our
Speaker 1: universe and that's led to all sorts of deep understanding.
Speaker 1: You know, the original simulations of the Sun predicted a
Speaker 1: huge number of neutrinos landing on the surface of the Earth,
Speaker 1: and they went out and measured them and the answer
Speaker 1: was wrong, and they thought, did we get the sun wrong?
Speaker 1: Or is there something going on with neutrinos? And it
Speaker 1: turned out the simulation of the sun was correct and
Speaker 1: neutrinos were doing something wonky between there and.
Speaker 2: Here, right. I think the idea is that, you know,
Speaker 2: if you create a simulation and you tweak the parameters
Speaker 2: of it, right, like the numbers in it, so that
Speaker 2: it matches what you see coming, for example, out of
Speaker 2: the real Sun, then the idea is that maybe what
Speaker 2: do you think is going on inside the sun is
Speaker 2: actually what is going on inside the sun?
Speaker 1: Yeah, that's exactly right. And somebody else might come up
Speaker 1: with another simulation saying, actually, I think something else is happening.
Speaker 1: And then you can ask, well, what's the difference between
Speaker 1: these two simulations. Do they predict any different things that
Speaker 1: we actually can measure that you can go off and
Speaker 1: use that to distinguish between two various ideas. And we
Speaker 1: talk about this all the time on the podcast. Sometimes
Speaker 1: we have like two different possible ideas for what's happening
Speaker 1: in near black holes. Remember we once talked about the
Speaker 1: magnetic field near black holes something we could definitely not measure.
Speaker 1: And there were two different models. One was called mad,
Speaker 1: one was called sane, and they made slightly different predictions.
Speaker 1: And then the recent picture of the black hole helped
Speaker 1: us distinguish between these two models, these two simulations for
Speaker 1: black hole magnet fields.
Speaker 2: Right. But I guess that's the tricky thing, is like
Speaker 2: just because the simulation matches what you see at the end,
Speaker 2: it may not necessarily be what's going on inside, Right,
Speaker 2: It could just be sort of a coincidence that it matches.
Speaker 1: It certainly could be, And you always have to be
Speaker 1: careful trusting your simulation. You always need ways to validate
Speaker 1: it and to ensure that the bits that are important
Speaker 1: to your science question are accurate.
Speaker 2: Because I guess sometimes that's the only option that we have, right,
Speaker 2: Like you're saying you can't just stick a stick inside
Speaker 2: the science and see what's going on and things like
Speaker 2: maybe black holes or the Big Bang, Like there's no
Speaker 2: way where we can go back in time and do
Speaker 2: an experiment on the Big Bang, Right, So we sort
Speaker 2: of have to rely on these simulations to try to
Speaker 2: understand what was going on.
Speaker 1: Yeah, And they've turned out to be extraordinarily powerful tools
Speaker 1: that give us insight into what might have happened in
Speaker 1: the early universe or what's going on in the hearts
Speaker 1: of black holes or neutron stars. I can't really imagine
Speaker 1: doing science without them.
Speaker 2: All right, Well, that's sort of what I guess a
Speaker 2: pretty good answer for why scientists use simulations. And surprise, twist,
Speaker 2: this whole conversation was just a simulation of our discussion
Speaker 2: of the topic. This is like a sixth sense. I
Speaker 2: only see simulated people. This was not the real podcast, right, Daniel.
Speaker 1: That's right? Yeah, and hopefully this answer is also true
Speaker 1: in the real universe.
Speaker 2: But Daniel, you got to interview a scientist who does
Speaker 2: physics and actually also wrote a book about simulating things
Speaker 2: in the universe.
Speaker 1: That's right. I had a fun chat with Professor Andrew Pnsen.
Speaker 1: He's a cosmologist and a professor at University of College London,
Speaker 1: and he wrote a new fun book called Universe in
Speaker 1: a Box, which explores the role of simulation in cosmology
Speaker 1: and in science in general. And he does have an
Speaker 1: answer for the question is our universe a simulation?
Speaker 2: Now? If I order Universe in a Box, do I
Speaker 2: get a universe in a box.
Speaker 1: Maybe you get a recipe for how to put a
Speaker 1: universe into a box.
Speaker 2: That's a that's not the universe in a box? And
Speaker 2: also why a box? Why not? I don't know, as
Speaker 2: spherical container.
Speaker 1: I thought you were going to ask, if the universe
Speaker 1: is in a box, what universe is the box in?
Speaker 2: Hmmm, No, I wasn't going to ask that.
Speaker 1: Dang it will my simulated Jorge wind Riven.
Speaker 2: It's in the multiverse. I don't know. Aren't they like
Speaker 2: meta universes outside of our universe? Isn't that the idea?
Speaker 1: Click on the multiverse box option on Amazon Shipping.
Speaker 2: The question then is can you have a multiverse in
Speaker 2: a box? Anyways, you had a great conversation with Andrew.
Speaker 1: I certainly did.
Speaker 2: What motivated him to write this book.
Speaker 1: You felt like the role of simulation in science was
Speaker 1: super important and yet hadn't really been explored by any
Speaker 1: pop side book, and so he wanted to share his
Speaker 1: love for simulations with everybody.
Speaker 2: Cool. Well, here is Daniel's interview with Professor Andrew Ponson
Speaker 2: about his new book, Universe in a Box.
Speaker 1: So then it's my pleasure to welcome to the program.
Speaker 1: Andrew Ponson, a cosmologist and professor at University College London,
Speaker 1: and youw thank you very much for joining us today.
Speaker 3: Oh, thank you.
Speaker 1: So your book is called Universe in a Box. It's
Speaker 1: a fascinating and telling history and sort of definition of
Speaker 1: what is a simulation and why it's important in science.
Speaker 1: So let's start off with very very basics. What is
Speaker 1: a simulation in your point of view?
Speaker 6: There are different definitions you can give, but I think
Speaker 6: a good place to start is by thinking it's trying
Speaker 6: to capture some element of the real world inside a computer,
Speaker 6: and that can take many different forms. It doesn't even
Speaker 6: have to be a physics right, it's we can have
Speaker 6: simulations of something like human behavior. There are simulations of
Speaker 6: the way that crowds might behave that architects use to
Speaker 6: make safer buildings by making the passageways the right kind
Speaker 6: of size and shape that if there's an emergency situation,
Speaker 6: then humans will evacuate the building in the most efficient,
Speaker 6: safe way. But I think what that already teaches you
Speaker 6: is that it's possible to do a simulation of something
Speaker 6: without necessarily understanding everything about that thing before you start.
Speaker 6: Because if you think of crowds, they're made out of people.
Speaker 6: We can't actually predict everything about how an individual human's
Speaker 6: going to react in any given scenario, And yet we
Speaker 6: can make simulations that are useful. They might not be perfect,
Speaker 6: but they're useful for giving us some insight into the
Speaker 6: way that crowds might behave. So when you take that
Speaker 6: across to the physics environment, and in particular my area cosmology,
Speaker 6: it's about trying to capture something about how the universe behaves.
Speaker 6: But we know from the outset we're never going to
Speaker 6: get that perfect.
Speaker 1: So then where is the value in a simulation if
Speaker 1: you have to, like encode in already what's going to
Speaker 1: happen when people bump into each other, or the purchasing
Speaker 1: choices of people, or how galaxies interact. If you have
Speaker 1: to already build in the physics, what are you learning
Speaker 1: from the simulation? How do you get any information out
Speaker 1: of it?
Speaker 6: Well, the point is that you code in some things
Speaker 6: about how the individual bits within your simulation behave. I
Speaker 6: guess you know, in the case of the crowd that
Speaker 6: would be how an individual human might behave under a
Speaker 6: variety of circumstances. But in the case of physics, it
Speaker 6: might be how we think dark matter particles flow through
Speaker 6: the universe and interact with each other through gravity, and
Speaker 6: what the simulation does is take a very large number
Speaker 6: of those elements and kind of have them all individually
Speaker 6: doing their thing. But the behavior that then emerges can
Speaker 6: be very hard to anticipate in advance. And that's the point.
Speaker 6: Understanding how a crowd behaves is not at all the
Speaker 6: same thing as understanding how a human behaves, and in
Speaker 6: the same way, understanding how dark matter behaves through our
Speaker 6: whole cosmos is not at all the same thing as
Speaker 6: understanding what an individual particle of dark matter might do.
Speaker 1: This principle of emergent phenomena is something I'm super fascinated by.
Speaker 1: It's incredible to me that sometimes we have laws of
Speaker 1: physics at one scale, which you know, causally determine different
Speaker 1: sort of laws of physics at another scale, which we
Speaker 1: can't always easily predict. But as you say, we can
Speaker 1: observe in action if we can, you know, construct the
Speaker 1: right setup to you is simulation. Is it a kind
Speaker 1: of experiment, Is it a kind of theory, or is
Speaker 1: it sort of a new branch of science.
Speaker 6: I think it's a new branch, but I think it's
Speaker 6: got something in common with theory and with experiments, and
Speaker 6: I guess I tilt mainly towards thinking of it as
Speaker 6: an experiment. Now that's a little bit controversial. Sometimes people say, well,
Speaker 6: it can't be an experiment. You've told the computer what
Speaker 6: to do, Whereas in an experiment, you're supposed to go
Speaker 6: and ask nature. You know, you're supposed to put things
Speaker 6: to the test and confront them with the reality of
Speaker 6: how things really work.
Speaker 3: But you know, I'm not.
Speaker 6: Sure that that distinction is always so clear. So an
Speaker 6: example that I give in the book is, let's say
Speaker 6: you're just trying to build an aircraft and you have
Speaker 6: some idea about how you want to shape the wing,
Speaker 6: but you don't know exactly how that wing is going
Speaker 6: to perform. Now you now have a choice. You could
Speaker 6: build a scale model of your wing and put it
Speaker 6: inside a wind tunnel and see how it performs inside
Speaker 6: a wind tunnel, and that's kind of an experiment. Or
Speaker 6: you could make a digital version of your wing and
Speaker 6: put it inside an airflow inside a computer that's a
Speaker 6: kind of simulated airflow, and see how it behaves there.
Speaker 6: And both of those are going to have limitations. There's
Speaker 6: definitely limitations on what you can achieve inside the computer,
Speaker 6: but there's also limitations in what you can achieve in
Speaker 6: a wind tunnel. You can't make an infinitely big wind tunnel.
Speaker 6: It's going to have edges, things are going to be
Speaker 6: the wrong scale. So when you do experiments, you are
Speaker 6: making some set of assumptions about the real world and
Speaker 6: how what you're doing applies to the real world. And
Speaker 6: I think that's just that's true in simulations as well.
Speaker 6: So overall, this is why I start to think more
Speaker 6: and more of simulations as types of experiment.
Speaker 1: I have an argument with my brother who's a computer scientist,
Speaker 1: and he runs what he calls experiments on his machine
Speaker 1: learning models. I'm like, that's not an experiment. You're just
Speaker 1: doing it in your computer. But you're absolutely right that
Speaker 1: if you don't know the outcome and you're learning something,
Speaker 1: it can be considered also an experiment. But you mentioned
Speaker 1: something which I wanted to ask you about anyway, which
Speaker 1: is the limitation of simulation. You're concocting sort of an
Speaker 1: artificial universe, and you're learning something about that universe. If
Speaker 1: that universe doesn't follow the same rules as ours, then
Speaker 1: obviously we're not learning something about reality, which usually is
Speaker 1: the goal. Can you say something about how we know
Speaker 1: when to trust our simulations with the fundamental limitations of simulation.
Speaker 6: Are yeah, I mean that is the hardest, most difficult question.
Speaker 6: At the heart of doing good simulation is knowing what
Speaker 6: to trust and what not to trust, and often it's hard,
Speaker 6: you know, it can be really hard to know. I mean, fundamentally,
Speaker 6: the limitation is just computational power that even if you're
Speaker 6: doing something like a weather forecast, which I talk about
Speaker 6: a bit in the book, you know, just the atmosphere
Speaker 6: of the Earth has so many molecules in it that
Speaker 6: you're never going to track each individual molecule, right, So
Speaker 6: you're going to have to make some kind of approximation.
Speaker 6: You're going to parcel up the air into almost like
Speaker 6: big hypothetical bags of air that move around through our
Speaker 6: atmosphere and use some laws to describe that. But then
Speaker 6: you're going to have to go and say, well, you know,
Speaker 6: we're not getting all the small scale details right, We're
Speaker 6: going to have to put in some corrections. In the
Speaker 6: case of meteorology, even clouds can be quite hard to
Speaker 6: predict because you're just not getting all of those tiny
Speaker 6: details that contribute to the way that a cloud forms
Speaker 6: in reality. So you need to go in and put
Speaker 6: into your simulation some kind of correction almost by hand.
Speaker 6: You say, under these circumstances, clouds must start to form.
Speaker 6: And you know, if you're a weather forecaster, you see
Speaker 6: how well did I do by making that assumption about
Speaker 6: how clouds form? And over time you sort of incrementally
Speaker 6: improve by comparing how your simulation did with the reality
Speaker 6: of how the weather unfolded. So we can do something
Speaker 6: a bit similar in cosmology. It's not quite the same
Speaker 6: because we don't get to kind of do the repeat
Speaker 6: experiments in quite the same way as you do in
Speaker 6: weather forecasting, for example. In some level, it's the same
Speaker 6: kind of iterative process that we're getting better over time.
Speaker 6: We're understanding the way that we have to put in
Speaker 6: corrections to our simulations to account for things like the
Speaker 6: way that stars evolve and change over time and dump
Speaker 6: energy into the universe, and what black holes are up to,
Speaker 6: and all of these things that we actually have to
Speaker 6: help the computer along the way, if you like.
Speaker 1: So why is it that we need to make these corrections?
Speaker 1: We have these limitations is it purely just computational power.
Speaker 1: In the limit of infinite computing power, could we predict
Speaker 1: the weather tomorrow starting from particle physics and modeling every
Speaker 1: single quirk in the atmosphe sphere, or is there a
Speaker 1: conceptual limit there some obstacle that we can't overcome even
Speaker 1: with infinite computing.
Speaker 3: I think both are a problem.
Speaker 6: So, first of all, we are very far from having
Speaker 6: infinite computer power, a very very long way away from that.
Speaker 6: But secondly, you're right, I mean there are more fundamental
Speaker 6: limitations as well. In particular, we do not know exactly
Speaker 6: where every molecule is in the atmosphere to start with.
Speaker 6: So even if you had a computer powerful enough to
Speaker 6: track at the molecular level what our atmosphere is doing,
Speaker 6: you wouldn't know how to start the simulation. You wouldn't
Speaker 6: have enough data to tell it what the atmosphere looks
Speaker 6: like today. So there's an inaccuracy that's sort of just
Speaker 6: coming from not having that perfect data, and an effect
Speaker 6: known as chaos means that imperfect initial data very quickly
Speaker 6: turns into big errors. So there's a kind of famous
Speaker 6: example of this. It was Edward Lorenz who sort of
Speaker 6: gave the thought experiment of butterfly flapping its wings somewhere
Speaker 6: in Europe, say, and it has a sort of series
Speaker 6: of knock on effects that over time just amplify and amplify,
Speaker 6: and eventually the tiny little gust from the butterfly's wings
Speaker 6: actually stimulates the formation of a hurricane. And you know,
Speaker 6: these kind of effects, we know they're there, and physics
Speaker 6: we call them chaos. And so you know, the slightest
Speaker 6: inaccuracy in how you set things up will eventually make
Speaker 6: the simulation depart from reality.
Speaker 3: So we know that's true in cosmology as well.
Speaker 6: And we don't have, you know, perfect information about the
Speaker 6: early universe. We have quite good ideas for what was
Speaker 6: going on there, but it's imperfect, and that means because
Speaker 6: of chaos and the way that those imperfections are amplified
Speaker 6: over time, what we end up with is, in some sense,
Speaker 6: it's like a statistical.
Speaker 3: Recreation of the universe.
Speaker 6: It's telling us to hisstically what sorts of things should
Speaker 6: be in the universe and what sorts of mixtures and
Speaker 6: what kind of patterns, rather than literally recreating the universe.
Speaker 6: So it's almost more like sort of climates, like a
Speaker 6: climate simulation almost rather than a weather simulation.
Speaker 1: I'm very interested in the history of simulations as well.
Speaker 1: I mean, theoretical science is like thousands of years old.
Speaker 1: Experimental science people argue about might be hundreds of years old.
Speaker 1: Simulation based science seems like decades old. Can you take
Speaker 1: us back to the root of it? Where does it begin? Really?
Speaker 4: Well?
Speaker 6: I think you know, the very earliest route you can
Speaker 6: find is in the nineteenth century, where Charles Babbage and
Speaker 6: Ada Lovelace were working on the idea of a computer
Speaker 6: very similar to our modern computers. It was the first
Speaker 6: time really anybody expressed the idea of having a machine
Speaker 6: that could be told to perform any calculation. So before
Speaker 6: that there were machines that did specific calculations, but this
Speaker 6: was the first time somebody envisage a machine where you
Speaker 6: could just give it instructions and it would carry out
Speaker 6: calculations to your specifications. And Ada Lovelace actually wrote at
Speaker 6: that time that one of the applications of being able
Speaker 6: to do that was to be able to take what
Speaker 6: we think are the governing laws of our physics and
Speaker 6: make them kind of practical, you know, get the computer
Speaker 6: to do all of the calculations that turns those abstract
Speaker 6: equations into concrete, specific predictions for different scenarios.
Speaker 3: So that's probably the first.
Speaker 6: Time anyone expressed what we would recognize as a modern simulation. Then,
Speaker 6: in terms of actually performing simulations, remarkably, some people tried
Speaker 6: to do this in the twentieth century before digital computers
Speaker 6: were actually made. So there are some beautiful stories like
Speaker 6: a crazy character called Lewis Fry Richardson who was actually
Speaker 6: on the front line of World War One trying to
Speaker 6: calculate weather forecasts using pen and paper, very very repetitive
Speaker 6: calculations he was doing that would be exactly what a
Speaker 6: computer does today to do a weather forecast, but he
Speaker 6: was doing it just with pen and paper and taking him,
Speaker 6: you know, weeks stretching out into years just to do
Speaker 6: one forecast. He wasn't trying to be practical about it.
Speaker 6: He was just trying to prove a point that this
Speaker 6: is actually doable in practice. And then you know, by
Speaker 6: the end of World War Two there were actual computers available,
Speaker 6: and very quickly from there the whole business of simulating
Speaker 6: all sorts of different things, but ultimately the entire universe
Speaker 6: kind of grew up quite quickly from there.
Speaker 1: Tell us more about the role of simulation in or
Speaker 1: personal research. Is this something you explore because you're fascinated
Speaker 1: by the computer science of it, or to use it
Speaker 1: just a tool that helps answer your physics questions.
Speaker 6: I think it depends on the day you ask me.
Speaker 6: I mean, some days I really enjoy the computer science
Speaker 6: of all this, and you know, it's undeniably cool to
Speaker 6: get to work with some of the world's biggest supercomputers
Speaker 6: and be able to instruct them to carry out these
Speaker 6: kind of simulations, and the results are enormous fun to
Speaker 6: work with as well. So there is a bit of
Speaker 6: there's a bit of that kind of nerdery in it.
Speaker 6: But I think ultimately the thing that really keeps me
Speaker 6: hooked is the idea that we are contributing to a
Speaker 6: bigger picture of how our universe evolved, of the role
Speaker 6: for materials in it that we as yet don't understand.
Speaker 3: Things like dark matter and dark.
Speaker 6: Energy that we know they're out there, they seem to
Speaker 6: be having a profound effect on our universe, but we
Speaker 6: really don't know what they are, and we're trying to
Speaker 6: learn more about that. And then I suppose ultimately what
Speaker 6: we're building towards is a better understanding of where we
Speaker 6: came from. You know, the existence of us carbon based
Speaker 6: life forms on this rocky planet is part of the
Speaker 6: story that we're telling. Because the chemical elements from which
Speaker 6: our planet and life are constructed weren't there in the
Speaker 6: Big Bang. They've been manufactured over time. They need very
Speaker 6: specific conditions to be manufactured and then concentrated enough to
Speaker 6: start forming planets and enabling life and so on. So
Speaker 6: I think, you know, ultimately that's the thing I'm most
Speaker 6: excited by that we are telling this bigger story that
Speaker 6: in the end speaks to a kind of deep question
Speaker 6: within all of us about where did we come from?
Speaker 1: Have you seen yet machine learning being used to amplify
Speaker 1: or speed up, or just overall enhance simulations in your research.
Speaker 6: Yeah, I mean machine learning is more and more important
Speaker 6: throughout astrophysics. So it's being used in a variety of
Speaker 6: different ways. One is to try and improve on some
Speaker 6: of these things that we were talking about a moment
Speaker 6: ago about you know, what do you do about the
Speaker 6: things that you can't quite get right, Like the way
Speaker 6: that stars form out of gas just such a complicated
Speaker 6: process that we can't capture it perfectly the way that
Speaker 6: those stars then put energy back into the galaxy that
Speaker 6: they're forming within the roles of black holes, all of
Speaker 6: these things that are very complicated and multifaceted.
Speaker 3: We can use machine learning.
Speaker 6: To do some of the hard work for us to
Speaker 6: learn from examples of individual simulated stars, say about how
Speaker 6: they behave, and kind of learn the lessons from those
Speaker 6: and then take them and put them in the bigger
Speaker 6: setting of trying to simulate then hundreds of billions of
Speaker 6: stars across a galaxy or maybe you know, even out
Speaker 6: into the universe. Sochine learning can kind of help us
Speaker 6: take lessons from one bit of our simulations or one
Speaker 6: bit of physics and insert them in an efficient way
Speaker 6: into other simulations. That's one role for them, But they're
Speaker 6: also crucial in interpreting the data we get from the
Speaker 6: real universe. So the data that is coming from telescopes
Speaker 6: and especially big new survey telescopes, things like EUCLID and
Speaker 6: the Verra Rubin Observatory, these giant efforts to scan the
Speaker 6: sky and build maps of our universe. They need machine
Speaker 6: learning because the machine learning can kind of do a
Speaker 6: lot of the initial data processing figuring out what's interesting,
Speaker 6: what we're actually looking at, where it is in three
Speaker 6: D space, and what needs to be flagged for further
Speaker 6: human follow up. All of these things, machine learning is
Speaker 6: playing an increasingly important role.
Speaker 1: And in your view, what does a future hold for
Speaker 1: simulation based science? Are there fields of science that don't
Speaker 1: yet use any simulation and are on the cusp of
Speaker 1: being revolutionized by this powerful new tool.
Speaker 6: I mean, I'm not aware of fields that have shied
Speaker 6: away from simulation. I think it is such a powerful
Speaker 6: tool that when you start looking for it, you do
Speaker 6: find it in use absolutely everywhere. But I think what
Speaker 6: we can say about simulation is that we're still a
Speaker 6: very early stage of understanding how to use simulation and
Speaker 6: what roles it can play within the overall scientific progress. So,
Speaker 6: you know, it goes all the way back to what
Speaker 6: really is a simulation? Is it an experiment or is
Speaker 6: it a calculation? How should we think about it? And
Speaker 6: tied up with that is this question of how can
Speaker 6: we improve our simulations? How do we get past that
Speaker 6: stage of well, we just have to kind of play
Speaker 6: around with things and tweak them till they fit because
Speaker 6: of the intrinsic limitations. So I think we're at a
Speaker 6: very early stage in understanding all of these things. So
Speaker 6: for certain the role that simulations play is going to
Speaker 6: change and evolve and I hope improve over the coming years.
Speaker 1: So if you use the words universe and simulation together
Speaker 1: in a sentence, that of course evokes in people's minds
Speaker 1: this conversation that seems to be omnipresent, which is, you
Speaker 1: know whether or not our universe could be a simulation
Speaker 1: or the same question sort of when we build artificial
Speaker 1: universes that have bits and pieces in it, could those
Speaker 1: universes feel real to those occupants. So in your book,
Speaker 1: you take a sort of skeptical view of the question
Speaker 1: of whether we could be living in a simulation. Since
Speaker 1: you're an expert on simulating universes, what are your arguments
Speaker 1: against the concept that we could be living in a simulation.
Speaker 6: Yeah, I think the primary argument is just to look
Speaker 6: at the complexity of the universe that we're in. So
Speaker 6: you can actually calculate something called the number of cubits,
Speaker 6: so basically the number of quantum bits that you would
Speaker 6: need in a quantum computer if you wanted to do
Speaker 6: according to all the physics we know so far, if
Speaker 6: you wanted to do a perfect simulation of our universe,
Speaker 6: and that is a vast number. I forget it off
Speaker 6: the top of my head. I think it's something like
Speaker 6: ten to the one hundred and twenty four cubits.
Speaker 3: It's something like that. It's a very very large number.
Speaker 6: And right now, you know, we struggle even to make
Speaker 6: a single cubit in a quantum computer. It's a vast
Speaker 6: extrapolation from where we are now to the idea that
Speaker 6: we will routinely be able to run simulations that have
Speaker 6: the same kind of richness as the reality that we
Speaker 6: currently live in.
Speaker 3: But even more than that. If you ask, well, you know, what.
Speaker 6: Resources would you need to build a computer that really
Speaker 6: had that level of capability, Well, it turns out you
Speaker 6: would need to make use of the entire universe just
Speaker 6: to simulate one universe. Because physics puts limitations on information processing.
Speaker 6: You can't process as much information as you like. There
Speaker 6: are limitations placed on it according to the physical system
Speaker 6: that it's being processed by, and so these come back
Speaker 6: to bite you. So and that's what gives rise to
Speaker 6: this claim that you can only simulate the whole universe
Speaker 6: perfectly if you have access to the entire physical resources
Speaker 6: of that universe. Now, there are lots of further objections
Speaker 6: you can raise. You can say, well, what about, for example,
Speaker 6: if the higher up universe where we're being simulated is
Speaker 6: just a much bigger universe with many more cubits at
Speaker 6: their disposal, and so these resources seem terribly trivial maybe
Speaker 6: to the people in the higher up universe. But at
Speaker 6: that point I kind of lose patience with the argument,
Speaker 6: because it seems to me, at that point we it's
Speaker 6: no longer a sort of obvious extrapolation from where we are.
Speaker 6: Now you're now talking about hypothesizing beings with so much
Speaker 6: more power and so much more technological capability than we have,
Speaker 6: that we might as well just go and talk about religion,
Speaker 6: because at this point it's lost contact, in my view,
Speaker 6: with any science.
Speaker 1: Wonderful. Well, I really enjoyed your book, And a question
Speaker 1: I always have for folks who write popular science books
Speaker 1: about very technical topics is why what do you think
Speaker 1: that the general public, folks out there who are not
Speaker 1: simulation experts need to know about simulation?
Speaker 3: I think there were two reasons.
Speaker 6: The first was it was amazing to me that nobody
Speaker 6: had written about this topic before because it's so central
Speaker 6: in our field of cosmology, and you know, cosmology is
Speaker 6: something that people do talk about a lot. There's a
Speaker 6: lot of it out there in the media and in books,
Speaker 6: because it's genuinely you know, it's exciting, and I think
Speaker 6: it speaks to all of us about where we came from.
Speaker 6: And so it seems a real surprise to me that
Speaker 6: this central tool in how we're learning about the universe
Speaker 6: was not being written about, and so it felt to
Speaker 6: me like it needs to be rectified. We need to
Speaker 6: be talking about this because it offers immense strengths, you know,
Speaker 6: real enormous strengths that I think are kind of hidden
Speaker 6: away sometimes. But it also comes with lots of caveats,
Speaker 6: some of them we've discussed. You know that we can't
Speaker 6: do these perfect recreations of the universe. There are lots
Speaker 6: of approximations. We might be making mistakes, and you know,
Speaker 6: there's no way to rule that out. It's just part
Speaker 6: of the scientific process that we have to keep an
Speaker 6: open mind about these things. So I wanted to write
Speaker 6: about that in a kind of open and honest way,
Speaker 6: rather than sort of leaving the simulations as black boxes
Speaker 6: that seemingly recreate our universe through some process of magic. No,
Speaker 6: it's a very human process and it's got all of
Speaker 6: the strengths and weaknesses that come with being that kind
Speaker 6: of human process.
Speaker 1: Wonderful. Well, thank you very much again. Professor Andrew Hanson
Speaker 1: at University of College London and author of the book
Speaker 1: The Universe in a Box Out now encourage everyone to
Speaker 1: go out and about how science is actually done. Thanks
Speaker 1: again very much Andrew for joining us today.
Speaker 2: All Right, did you feel you had a real conversation
Speaker 2: with him, Like, did things get real or was it
Speaker 2: all just simulated pleasantries?
Speaker 1: I was able to simulate enjoying a conversation. Yeah. Oh
Speaker 1: that's sort of always my situation though, because I'm kind
Speaker 1: of an introvert, so I have to simulate enjoying human interactions.
Speaker 2: Yet you have to simulate being engaging human being.
Speaker 1: I'm trying to quietly slip unnoticed into human society.
Speaker 2: So you are a simulation, Daniel.
Speaker 1: I'm simulating being a human.
Speaker 2: But anyways, that was a great conversation. What's your main
Speaker 2: takeaway from it?
Speaker 1: To me, it's fascinating that so recently in the history
Speaker 1: of science, just the last few decades, we've developed this
Speaker 1: crucial new tool that we now think is indispensable. It
Speaker 1: makes me wonder in fifty years and one hundred years,
Speaker 1: what new branch of science or what new tool we're
Speaker 1: going to add to our toolbox which future scientists will
Speaker 1: think is indispensable, And we'll wonder, like, how did Daniel
Speaker 1: and folks who lived way back then do any science
Speaker 1: without it?
Speaker 2: Or maybe even like why did Daniel do science? Why
Speaker 2: didn't they choose use the AI physicists to answer all
Speaker 2: the questions?
Speaker 1: Yeah, stay in bed and let the AIS do the work.
Speaker 1: The answer to that is the AIS wan't answer questions
Speaker 1: the AIS are interested in and science is for people,
Speaker 1: by people, and of people, So you got to have
Speaker 1: people asking questions.
Speaker 2: Wait, wait, do you mean the AIS won't always do
Speaker 2: what we ask them to do.
Speaker 1: They'll always do exactly what we ask them to do,
Speaker 1: just not and maybe in the way that we want
Speaker 1: them to.
Speaker 2: All right, Well, stay tuned to see if we are
Speaker 2: in a simulation right now? Maybe I guess some people
Speaker 2: consider the whole universe to be service.
Speaker 1: Simulation, right, how can we possibly know?
Speaker 2: Yeah, Like, what's the difference if.
Speaker 1: We are in a vast alien simulation. Then I definitely
Speaker 1: want to talk to those aliens because they have some
Speaker 1: awesome computers.
Speaker 2: And also to stay tuned about what new developments humans
Speaker 2: are going to discover using simulations and or artificial intelligence.
Speaker 2: So we hope you enjoyed that. Thanks for joining us,
Speaker 2: see you next time.
Speaker 1: Thanks for listening, and remember that Daniel and Jorge Explain
Speaker 1: the Universe is a production of iHeartRadio. For more podcasts
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