Daniel and Kelly’s Extraordinary Universe - How to think like a physicist?

Episode Date: July 30, 2024

Daniel and Jorge discuss whether physicists think about the world differently, and how that makes them more or less useful.See omnystudio.com/listener for privacy information.

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Brought to you by Novartis, founding partner of IHeart Women's Sports Network. Why are TSA rules so confusing? You got a hood of you. I'll take it all! I'm Manny. I'm Noah. This is Devin. And we're best friends and journalists with a new podcast. podcast called No Such Thing, where we get to the bottom of questions like that. Why are you screaming at me? I can't expect what to do. Now, if the rule was the same,

go off on me. I deserve it. You know, lock him up. Listen to No Such Thing on the Iheart radio app, Apple Podcasts, or wherever you get your podcast. No such thing. Hey, Daniel, did you always want to be a paid for? Physicist? Definitely not. When I was a kid, I did not want to be a physicist. Really? You knew what it was, but you knew you didn't want to be one. I don't think I understood what a scientist was well enough, but when I was a kid, I wanted to be an explorer. I wanted to get on a ship and find some new island and name it after myself.

You just might get out of Los Alamos, said the main purpose here. Yeah, though, you can't really take a ship out of Los Alamos because it's landlocked, so there was some basic problems in my thinking. Well, you could take a train and then a ship. But don't they say everyone's a physicist, especially little kids? Yeah, I think everybody is a scientist because they are curious about the world. And in the end, I discover that being a physicist is kind of like being an explorer, except instead of discovering new continents, we're trying to discover new frontiers of knowledge. Instead of surfing the waves out there in the sea, you're surfing the couch, mostly.

I'm clickety-clacking my way to new shores of knowledge. Just don't get scurvy on your couch. I got a bowl of limes here next to me. Okay. With the tequila and the margaritas? That's for after work. After work, work. These days, what's the difference?

Hi, I'm Jorge. I'm a cartoonist and the author of Oliver's Great Big Universe. Hi, I'm Daniel. I'm a particle physicist and a professor at UC Irvine, and I want to teach people to think like a physicist. Wait, I'm confused. If everyone's a physicist, aren't you just teaching people to think like humans? Yeah, basically, I'm done. I can retire. It's after work time. Where's my margarita? I know. Let's get the shots going. No, I think everybody does have curiosity, but, you know, it took us a while to figure out some tips and some tricks to effectively extract knowledge from the universe, rather than just like, you know, making up cute stories to satisfy our curiosity.

Right. It probably took a while to get paid to do it, too. Yeah, that's certainly true. A lot of the big names in the history of science were men of leisure, you know, operating on their trust funds or daddy's bank account. Who do you think was the first professional physicist? You know, science as a profession is not actually that old. It's something like in the late 1800s that.

people started to call themselves scientists and get paid to do it or money to hire people to do this kind of research until then it was you know natural philosophers and people just sort of like curious poking around in their own laboratories yeah but scientists as a job is not much more than like a hundred years old whoa so even the word science is relatively new yeah exactly if you guys like gauss or newton or leibnitz or aristotle they certainly would not call themselves a scientist that's a new word or maybe they did it on purpose very like science no thanks it's that newfangled thing

that all the kids are talking about I prefer to be a natural philosopher but anyways welcome to our podcast Daniel and Jorge Explain the Universe a production of iHeard radio in which we do our best to demonstrate what it's like to think like a physicist we take a physicist approach

to dismantling the whole universe understanding all of its little bits building mental mathematical models to try to explain it asking questions of those models and then wondering what does it all mean anyway? Yeah, because as we talked about before, the universe belongs to everyone

and asking questions is everyone's job, but a few people get to do it as a career. Get to, yes, exactly. It's definitely a treat and a privilege. Well, you get paid to do it, I guess. And to do that, there's a certain mindset you have to have, right, in order to be part of the profession. Yeah, there definitely is a way of thinking

that's sort of like a physicist's way of thinking. And I see this because people who are trained as physicists and then go out into the world and work in other areas, chemistry or engineering or computer science, still take with them a certain mindset, a certain way of approaching problems, which can be really, really helpful and useful or also sometimes frustrating for their colleagues. Yeah, and I can totally relate. I think that also the same is for engineers. You know, anyone who studied engineering definitely thinks like an engineer is trained to think in a certain way and a certain mindset about. tackling problems for sure. Yeah, absolutely. Take an engineering approach to cartooning, for example.

Yeah, whenever I draw a bridge, I mean, I really put some calculations behind it. You know, I want to make sure it doesn't fall down. Yeah, I know all those cartoons could be injured. I mean, think about their families. Yeah, I usually build in a safety factor of like two or three to every cartoon I draw, just in case. But yeah, but professional physicists do think about things in a very different way than the rest of us. And so that's the question we'll be exploring today.

So today on the podcast, we'll be tackling. How to think like a physicist. And I'm not sure if this should be like an instruction manual or like a warning. Oh, why? What can happen? You know, like watch out for these signs that you're thinking like a physicist. Or like, hey, would you like to think like a physicist? Here's steps one to five.

Well, I guess if it was the former, Aren't we titled the episode? How to not think like a physicist? How to avoid thinking like a physicist? We're going to get into the positives, I'm sure. But, you know, there is this lore that sometimes physicists oversimplify things. They're like, come into a new field. They're like, oh, you can just approximate this with a sphere,

maybe a line on it or whatever. There's this urban legend that physicists being too simplistic or the cause of the 2008 financial collapse, for example. So, you know, there are potentially some dangers to applying physics thinking to the broader world. Daniel, I wonder if you're overest, how much people think about physicists. Probably.

I definitely don't have a clear view of that. I mean, I think for an urban legend to exist, you sort of need urban people talking about it. Maybe that's just an urban legend within physics. Maybe nobody else. Yeah, maybe it's like a, yeah. I mean, physicists just have issues.

We definitely do. But it's an interesting question to ask if you're thinking about following a career in physics or are wondering, What is the job entail and what kind of mindset do you have to have in order to do it at a research university or to become one or to get a degree in it? Or if you're just an armchair of physicist, if you like thinking about the nature of the universe and making progress. And over the years, maybe while listening to this podcast, you've been putting together your own personal mental model of the universe, asking you questions, trying to click it together, coming to a holistic understanding of how things work. In that case, you might have picked up a few of the tricks of thinking like a physicist.

Well, as usual, we were wondering how many people out there had thought about this question, had maybe wondered what it's like to be a professional physicist and what kind of mental skills you need to be one. Thanks very much to everybody who answers these random questions. Love hearing your thoughts. Please don't be shy. If you want to join the group, just write to me to questions at danielanhorpe.com. So think about it for a second.

What do you think it takes to think like a physicist? Here's what people have to say. A physicist must think of slightly small and apply it to the infinitely large universe. And that's not easy to do. Hence, the podcast for the rest of us. Well, if that's an expression, I haven't heard of it before. So at its value, I think it probably would refer to someone being very practical,

someone following the scientific method, very dogmatic, accurate. But then some theoretical physicists, they're a bit wacky in what come out with, so possibly a little bit of that too. I think like a physicist is to be asking questions and be relentless in your quest for an answer. I'd say thinking like a physicist means being curious and searching for answers through trial and error and experiments. This is our podcast about physics and to do it with your cartoonist friend. Basically, to think like a physicist means if you discover something,

You get to a really terrible name that doesn't make sense. I think it means to contemplate matter and energy and their interactions with one another. Well, from the episodes that I have listened to so far, I would say that to think like a physicist means to be inquisitive, to try to make connections between different aspects, facets of life, and wondering why. trying to better understand and explain the phenomena we see throughout our daily lives. All right. I like some of these answers. I guess we're done because one of them said we just need to start a podcast about physics.

And then give everything you discover a terrible name. These are some juicy answers. I guess people have been listening to our podcast. I love these answers because they're so meta. They tell me basically what people have. learned from listening to the podcast for all these years. It's fantastic. Well, hopefully people are thinking a little bit more like scientists, like rational thinkers because of this podcast and also maybe learning a little bit more about the universe and how it all works down to the atomic level

and the galactic level. Yeah, and not just absorbing facts and little bits of knowledge, pieces of information, but also training yourself into how to accumulate more information, how to fit those pieces of information together, how to think about them, Science is more than just what we've learned. It's how we're going to learn more. How are we drawing the distinction here between physicists and just a regular scientist? Or do you mean how to think like a scientist? Yeah, it's a great question.

I don't know the answer to that. I'm probably not even the right person to answer the question of how do physicists think because I'm stuck in that mindset. I can't really see outside of it to understand how other people think. But when I meet chemists or biologists or economists, I do notice that they answer and ask questions in a different way. There's something I have more in common with other physicists than I have with other scientists. So there's something to it.

All right. Well, let's dig into it. What do you think is specific about how physicists think? I think some of it comes from the fundamental motivation and the assumptions that underlie physics. Like the goal is big. We want to understand the universe. We want to figure it out. And the assumptions are pretty basic.

They're like, look, the universe is understandable. And we can describe it with mathematical law. We can build a mental model. The model should follow those laws. And we can use it to like predict the future and to understand the nature of the universe. You know, inherent in that is that we are simplifying the universe. We're taking all these observations and then weaving them together into a story. That's what the mathematical model is.

We're saying, here's how this works. Here's what's really happening behind the scene. So there's sort of like an ambition there to say like, we can describe the basic elements of the universe. Whereas, and again, I'm not an expert. In other fields, you know, they feel like, you know, they feel a little bit more zoomed out. So they're not always as ambitious about like the fundamental understanding. They're describing things at sort of a higher level,

which again still requires mathematical modeling and great precision. It's not a question of like precision or rigor. It's just a question of like the ambition, the context of the questions you're asking. Well, are you saying that other scientists are not as ambitious? I think maybe philosophically physics and at least fundamental physics and particle physics is asking,

more ambitious questions than other fields. Yeah, I think they have deeper and broader implications, again, philosophically. Right, right. So you think your topic of research is more important than other scientists because you're a physicist. I'm just saying there might be a little bit of bias here, Daniel. No, it's totally reasonable to dig into that. I wouldn't say more important. You know, somebody who's developing new techniques to develop green energy, for example,

They're not answering deep and fundamental questions about the nature of reality, but they're improving people's lives and maybe saving the planet. So that's arguably much more important. But I think in terms of the philosophical context of our lives, particle physics and fundamental physics is answering those questions. Whether that's important or not is totally subjective, you know, whether it has value. Every kind of science is answering different kinds of questions, giving different kinds of insight into how the universe works.

For me, at least one of the appeals of fundamental, physics are these philosophical implications of it. Right. Well, I think, you know, most scientists would agree that what they're doing is also trying to understand and explain the world. I wonder if maybe a lot of the difference is just in the topic and the kinds of things that you're looking at, the scope of it or the kinds of phenomena you're looking at. Yeah, I think that everybody is doing the thing they think is most interesting and most exciting

and that's very personal, right? The person who's like crouching in a rainforest watching spiders crawl up twigs for an hours and hours a day is deeply fascinated by that and chose to do that instead of economics or psychiatry or whatever for a reason. And that's totally cool. So you're right. And the choice of topic is very, very personal. But I think the choice of topic also sometimes leads to a different way of thinking.

Like I think because we're trying to ask fundamental questions and deep questions about the universe, we feel like we can touch onto some sort of mathematical purity. that there is maybe mathematics that describes this that we can drill down into and reveal. You know, somebody who's studying like hurricanes, you know, we don't have any mathematics that describes hurricanes. So we can do some simulations, but we're sort of at a loss because of all the chaos and the details.

But when you zoom down into the fundamental firmament of the universe, we hope maybe there is some mathematics there that can describe what's going on. And so that's, I think, why physicists tend to build these mental mathematical models, sometimes too simplified, you know, Hence the famous spherical cow joke, which I don't know, maybe that's only a famous joke within physics. You tell me. I've never heard that before.

But, you know, I think all scientists would say that what they're doing is fundamental as well. Like if you're studying spiders, you're probably thinking about the different ways that life can form or the different factors that go into creating life and the factors that shape life. That seems pretty fundamental as well. Yeah, absolutely. And if anything, I think you probably have a lot more insight into this than I do or than most people, because you interact with so many different kinds of scientists. And obviously, you've been spending a lot of time learning about physics

and decoding the brains of physicists, but also other scientists. And so from your perspective, I'd be very curious to hear, like, do you think physicists think differently? Is the mind of a physicist trained at different skills? Do they take a different approach? Or all scientists, just in one category for you? I think that if you're a scientist, you're probably trying to figure out how the world and the universe works.

You're just asking questions about different phenomena in it. You know, if you're someone who studies hurricanes, you're trying to understand how certain physical processes work and how they can come together to create large effects. For example, that seems pretty fundamental as well. Or as fundamental as asking, you know, what an atom is made of. Yeah. Can spiders come together to make hurricanes?

Wouldn't that be awesome? And shouldn't we pitch that show to the Discovery Channel? Spider-nados. Spider-cane. Oh, no, no, sorry, science spider deers. Sounds like a winner. Yeah. Well, I can't tell you whether it's fundamentally different from the way other scientists think

because I'm not other scientists. I mean, you can comment, but I can try to give you a little bit of an insight into the way I approach a problem or the way I think about problems. And that's this reliance on building a model. You know, I look at a science problem like, where is that ball going to land after it comes off the bat? Try to predict that. And I think, well, to get that exactly right is way too complicated.

And there's so many factors involved. There's the wind speed. There's that bird flying by. There's tufts in the air, et cetera. And so I build a simpler model of the universe. I say, toss out the real universe. Can we come up with a simpler version of the universe and ask the question in that universe, but build the model in such a way that the answer in the simpler universe is still relevant

to reality? So can we extract the crucial details of the problem? Put those into our model and then use that to answer the question. So, you know, you don't care, for example, about the color of the ball. You don't care whether some kid in the stand is eating ice cream. None of these details about glorious reality matter to answering this question. So you build a simpler model specific to that question because it's good at answering that question, not every other question. And you know, you can argue philosophically like, is that model real?

What does it mean about the universe? If it works, et cetera, et cetera. But that's sort of, to me, the core of thinking like a physicist is building a little mental model and then using that to answer your question. Yeah, I think you're basically describing what any scientist does, you know, chemists, biologists, they all work off models. I mean, probably the word models the most used word in all of science. Yeah, you know, biologists make models about evolution, about gene interactions, about how molecules interact or how species propagate and things like that. But I wonder if the difference with you is that you're making models about the physical world or about baseballs, for example, and not spiders. Spiders are just way too complicated. There's no way for me to build a model of a spider. I have no idea. So it's a baseball. Exactly. And I know how to make the approximation so that I can describe a baseball. I know what to ignore. Maybe that's just my physics intuition. But I don't know how to do that for a spider.

And I want to push back a little bit. I do think there's a difference between the models built by physicists and those built biologists, for example. I mean, in biology, we know that every model we build, is effective. It's not fundamental. It's describing some emergent phenomenon like butterflies or spiders. Something we know is not an inherent object in the universe, but made out of those bits. It comes together through a special arrangement. So biology isn't describing something inherent to the universe. It's just approximately describing how things work during special conditions where like spiders and butterflies happen to emerge because they don't always, right? that there's a long time in the universe without spiders and butterflies.

And so those rules don't apply in those scenarios. But physics is trying to figure out the fundamental laws, those that always apply in all circumstances that are inherent to the universe. And that difference in goal, I think, leads to a different way of thinking, you know, good or bad. It leads to a hubris that we can describe anything with simple laws. And it leads to different approaches and a different scientific culture so that physics are kind of recognizable to others and also to each other. Well, you're married to a biologist.

How does your way of thinking differ from your spouses? Yeah, I think something that's different in between the way that I think about things and the way biologists like my wife think about things is we're definitely much more focused on questions of like uncertainty and making things quantitative in order to try to extract some knowledge. Sometimes the things we're dealing with are abstract or indirect. You know, we're talking about tiny particles or things we can't ever see or even struggle to visualize. And so to help us guide our thinking, we rely really heavily on the uncertainty.

How well do we know this? What can we say about this? Because we don't have much intuition. We can't like always gut check our answers and say, is that reasonable that the top quirk lives for 10 to the minus 23 seconds? I mean, you can't see that anyway. Whereas, you know, my wife, she can like look at stuff and, oh, is it growing? Did we get this right? Is this virus killing that bacteria?

Is somebody's gut health improving when they eat more chia seeds, this kind of stuff. But she works with models as well, right? Her grad students are really good looking. Yes, they are like models. Yeah, well, in comparison to physicists, I'm sure. Oh, snap. Oh, snap. You're right, though, that models is a very abused word.

Like, I also work in the machine learning community, and they're a model means me very, very different than a model in physics, than a model in fashion. And so it's a very generic word, unfortunately. But you think that maybe it's something to do with the way that you look at the world and you formulate models. But I guess I'm trying to say that. I think that's what all scientists do, right, across different fields? Yeah, so maybe physicists have more in common with other scientists than I ever imagined. Happy day.

Sounds like you need to talk to people outside your department a little more, maybe. Besides your spouse, how often do you interact with economists or chemists? Economists very rarely, only if I run into them at the park. chemists and computer scientists and engineers much more common. We sometimes have problems in common, you know, working on electronics for a new technology. We want to bury in the ice in Antarctica. We need to understand the engineering details of it or thinking about how to apply machine learning techniques.

We've developed for neutron stars to the problem of like predicting organic synthesis, these kind of things. So yeah, definitely interact with the more physical science than engineering people more often than like psychiatrists. But I also talk to philosophers quite a bit. I don't know if they qualify as scientists. Do they? I think they're not usually not in the same department for a reason, isn't it?

It's fascinating, though. Actually, people in the philosophy of physics department here, they all have their PhDs in physics rather than in philosophy. Well, so they're physicists who have a philosophy degree in the philosophy of science, physics. They have a doctor of philosophy and physics, but now they're professors in philosophy of science. physics it sounds like what is it the snake finally ate its tail it is interesting to think about how people who are paid to do physics in particular think and what kinds of what makes

them a tick i guess and what how does that color how they see the world and so to get more insight into that daniel you interviewed a couple of physicists and one ex-physicist yeah that's right i I talked to one physicist who's made it her mission to explain to people how physicists think about uncertainty. And another whose job is to guide physicists into the real world to find positions outside of academic physics and research. Wow. It sounds like these are sort of like physics translators or physics counselors. Yeah, exactly. Trying to bridge the gap between physicists and actual human beings.

All right, well, when we come back, we'll listen to Daniel talking to two physicists whose jobs it is to translate what physicists think and do to the rest of the universe. So we'll dig into that. But first, let's take a quick break. Kids gripping their new Christmas toys. Then, at 6.33 p.m., everything changed. There's been a bombing at the TWA terminal. Apparently, the explosion actually impelled metal, glass. The injured were being loaded into ambulances. Just a chaotic, chaotic scene.

In its wake, a new kind of enemy emerged, and it was here to stay. Terrorism. Law and law and... order criminal justice system is back. In season two, we're turning our focus to a threat that hides in plain sight. That's harder to predict and even harder to stop. Listen to the new season of Law and Order Criminal Justice System on the IHeart Radio app, Apple Podcasts, or wherever you get your podcasts. My boyfriend's professor is way too friendly and now I'm seriously suspicious. Well, wait a minute, Sam. Maybe her boyfriend's just looking for

extra credit. Well, Dakota, it's back to school week on the OK Storytime podcast, so we'll find out soon. This person writes, my boyfriend has been hanging out with his young professor a lot. He doesn't think it's a problem, but I don't trust her. Now, he's insisting we get to know each other, but I just want her gone. Now, hold up. Isn't that against school policy? That sounds totally inappropriate. Well, according to this person, this is her boyfriend's former professor, and they're the same age. And it's even more likely that they're cheating. He insists there's nothing between them. I mean, do you believe him? Well, he's certainly trying to get this person, to believe him because he now wants them

both to meet. So, do we find out if this person's boyfriend really cheated with his professor or not? To hear the explosive finale, listen to the OK Storytime podcast on the IHeart Radio app, Apple Podcasts, or wherever you get your podcast. Have you ever wished for a change but weren't sure how to make it? Maybe you felt stuck in a

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from rising stars to legends chasing history, the predictions, well, we see a first time winner and the pressure. Billy Jean King says pressure is a privilege, you know. Plus, the stories and events off the court and, of course, the honey deuses, the signature cocktail of the U.S. Open. The U.S. Open has gotten to be a very fancy, wonderfully experiential sporting event. I mean, Listen, the whole aim is to be accessible and inclusive for all tennis fans, whether you play tennis or not. Tennis is full of compelling stories of late. Have you heard about Icon Venus Williams' recent wildcard bids? Or the young Canadian, Victoria Mboko, making a name for herself.

How about Naomi Osaka getting back to form? To hear this and more, listen to Good Game with Sarah Spain, an Iheart women's sports production in partnership with deep blue sports and entertainment on the IHeart Radio app, Apple Podcasts, or wherever you get your podcasts. Presented by Capital One, founding partner of IHeart Women's Sports. All right, we're asking the question, how to think like a physicist. And apparently that involves talking to more physicists. Group think like a physicist. All right, well, you got to interview two interesting people here, Daniel. The first one is Dr. Jen Kyle.

What does Jen Kyle do? Jen Kyle is a theoretical physicist, but she also runs the YouTube channel Think Like a Physicist, where she tries to explain to you how to use the techniques and tricks of physics to think about the world and also to decode science results. So you can get an understanding for whether that news flash you just read about black holes is real or not. And did she talk to non-physicist to figure out if a physicist think in a unique way? through her YouTube channel. So yeah, via the comments section.

Oh, boy. And we all know how productive those can be. Great insights in the comment section, as always. All right. Well, here's Daniel's interview with particle physicists and YouTuber Jen Kyle. So it's my pleasure to introduce the podcast, Dr. Jen Kyle. Jen, thanks very much for joining us today. Hi.

Great to be here. Great. Tell us a little bit about yourself. What's your background, what your training? What are you up to now? Oh, well, I'm a theoretical particle physicist. I've done mostly work on beyond the standard model physics. I've looked at some things on dark matter and possible new theories of flavor in the quark and lepton sectors. And I basically dabbled in physics beyond what. we know now. Great. So you are definitely a trained and practicing physicist. So tell me what does it mean to you to think like a physicist? Can you remember learning how to do that? Can you compare the way you think now to the way you thought before you went to grad school? What does it mean to think

like a physicist? I would definitely say it was not something that one learns in one day. it's more of a practice that that you learn over many years. And I would say that a large part of thinking like a physicist is knowing how to draw conclusions from the universe and observations that we make of it, but also always keeping in mind how uncertain those conclusions that we draw from our observations can possibly be. What do you mean uncertain? Like we have a hunch and we're not sure or we don't have enough information or we could be confused. What do you mean by uncertain?

Well, basically we draw conclusions about the universe from making observations and making measurements. So let's say that we have some amazing new idea that someone has come up with, but it hasn't been tested. It will make predictions about the universe. and oftentimes these are predictions about the values of certain quantities that we can measure, like the lifetime of a particle or the rate of a certain process that happens at the large Hadron Collider. And we want to test this new amazing hypothesis, so we go and measure those quantities. And when we measure those quantities, we use experimental apparatuses and techniques, but it's not

possible to ever have a perfect experiment. Whenever you get a measured value of a quantity, it's always going to differ at least a little bit from the true value of the quantity that you're trying to measure. So if you try to measure the electron mass, you will get a measured value of the electron mass, but it's not going to be exactly the true value of the electron mass. So let's make a little bit more concrete instead of thinking about particle physics. Let's say somebody gives me a coin and I have a theory that this coin is not fair, that it's going to favor heads, right, 66% or something. And then I can do an experiment to see, well, is it a fair coin by flipping it, right? 500 times. So I think you're saying that there's uncertainty because

even if I flip it a thousand times, I'm never going to know precisely what the real probability is because I'm not flipping an infinite number of times. There's always some randomness. Is that what you're saying? Exactly. Okay. So there's uncertainty in our measurements because we don't take infinitely long experiments and we don't have infinite amounts of data. What are some other ways that we can be wrong or uncertain about our conclusions? Well, there are lots of ways that error can sneak into measurements. For example, we make measurements using some kind of experimental measurement apparatus. So, for example, let's say if we're trying to measure any quantity, we're using some kind of experimental apparatus to do it. And that apparatus is going to have a finite

resolution of some kind. So, for example, let's say you're trying to measure the size of an object in a room. You use a ruler. And that ruler has a finite gradation on it. You can't see down to the micron size using a ruler. So there's automatically some level of uncertainty that's going to come in because of effects like that. You may also, for very complicated measurements, like, for example, if you're trying to measure a cross-section at the Large Hadron Collider, you have very complicated measuring devices, and you have to simulate various parts of not only the physics that you're trying to understand, but the device. And those simulations will never match up exactly well with reality.

So I think what you're saying is that sometimes to do these experiments, we have to use devices we don't even really understand exactly how they work. Like if I'm measuring an electron, the Large Hadron Collider, and I have some device to measure an electron's energy, it's complicated to measure an electron's energy, and I don't exactly know what happens when an electron slams into a block of copper and creates a huge shower of other particles. It's complicated physics, and I could be wrong about what's going on in my own. experimental device that I built and designed, right?

Yes, in fact, we don't entirely understand our own measuring devices perfectly. So we have to model them and simulate them and sometimes compare those simulations to data in order to improve those simulations and get a better measurement of whatever it is we're trying to measure. Right. So like back to the coin example, you know, it's easy to look at a coin and say, oh, it's heads or oh, it's tails. But say it was harder, right?

Say I couldn't just look at the coin. I needed to have some little device that told me if it was heads or tails. And that device, I didn't really know how it worked and I wasn't always sure it was correct. That would lead some like uncertainty into my measurement, right? Because it could be wrong or I could think that it's correct, but it's incorrect in some other ways. Yes, and it might be using some pattern recognition software that doesn't handle like certain light levels very well or something like that. So, yeah, it could make a mistake every once in a while until you got heads when you got tails or vice versa. Yeah. And so in physics, we're very quantitative about this, right? We're very specific. When we measure something, we say, oh, there's a 2% chance we've been wrong or a 0.000-1% chance we're wrong. Why are we such sticklers about this in physics? Why are we such nerds about measuring precisely how wrong we might be in physics? What do you think?

Well, I think that part of it is that physics was one of the first fields to do a lot of measurements. So if you're only doing 10 measurements and you think you'll screw up like one out of a thousand, you're probably not too worried that you're going to produce a wrong result or produce a result that had a large statistical fluctuation where you didn't screw up anything and your apparatus performed exactly correctly, but nonetheless, you got very unlucky. If you think that that probability is small and you're only making like 10 measurements, you're not too worried that you're going to publish a result that's going to lead people down a wrong path.

But in particle physics, we make thousands of measurements, most of which you never hear about in the news, because unfortunately most of them agree with the standard model. But because we make so many, there's going to be some just out of statistical fluctuations that happen to appear to disagree a lot with what we expect. And so it's very important to have a very strict criterion for deciding when something disagrees with what we expect so much that it must be interesting. Yeah, I think that's probably true. Do you think it's also because some of the things were.

probing are sort of invisible so that our measurements are always going to be indirect. You know, like if somebody discovers a new kind of turtle in biology, they're like, here's the turtle. Like I can show you. Look, this is a turtle. Like nobody's confused about whether it's a turtle. But if we're saying, hey, I discovered the squiglion. It's not like I can say, I've got a pile of squiglions. Here they are. Let's all play with them. I have to show you data and the data has statistics and we have to make inference. And so it's always frustratingly indirect. And I wonder if that's one reason why we have to be such nerds about whether or not we've been confused because there's so many different steps between the physical reality

and the actual measurements we make. Yeah, it's also the case that in particle physics, we're also dealing with looking for processes in colliders that can look a lot like other processes that we aren't actually interested in. So it's not so much like we go out into the world and we find a new turtle and we bring it back and show people and say this is a new turtle it's more like

we go out into the world and we find a new turtle that looks very, very similar to a lot of other turtles and we bring that turtle and another 30 turtles back and we show the collection of turtles to our colleagues

and we have to convince them that that one turtle really is special. It's not just the same turtle all the way down. Yeah, exactly. And then we do, Experiments with those turtles flipping them to see if they're fair coins or not. So this is the way that physicists think about things.

We're really focused on what we've measured, how well we know it, quantifying that uncertainty, different ways we can be wrong. When we communicate our results to the public, this is a challenge, right? To express to them, here's what we think, but here's how much wrong we might be. What do you think are the usual stumbling blocks for people who haven't spent their lives learning to think like a physicist for understanding uncertainties and what we mean by uncertainties when we talk about them? Well, I think one problem is that most of the time in real life, when we're talking about

needing to know the value of some quantity, we're not... Hold on. Are you contrasting physics with real life? Is that what you just did here? Hey, for me, physics is not real? For me, they're the same thing. Okay. But in the ordinary life where we go outside and, you know, do things where we're not looking at a computer screen, we do get values for various quantities. Like if we're driving our car, we do look at our speedometer, hopefully, and see what speed we're getting. And generally the outside world isn't very, it's not used to giving us uncertainties on the numbers that we get.

So we look at that speedometer and it tells us we're going 57 miles an hour, but it doesn't put an error bar on it. And also, when we're learning things about either physics or anything else in our education, at least in our earlier education, Usually, the idea is, here are the principles that we work from, what can we figure out from it. But we don't actually stop and think, well, what are the experimental results that led to us having those principles and what were the errors on those principles? What were the uncertainties on those principles? And, you know, how well does that principle work with the situation I'm trying to study at the moment? Am I actually using the right set of scientific principles? for the situation at hand, or am I introducing some uncertainties that maybe I need to think

about? So I would say that the main stumbling block is that we just aren't exposed to it. It's hard to come by. Yeah, I see. So maybe when you get pulled over, you can tell the officer, like, look, it said it was, I was doing 60. I don't know why your machine says I was doing 85. maybe there's some mistake somewhere, right? Sometimes we have a little bit of intuitive grasp of like, maybe there's fuzz in the numbers. But you're right, we're rarely like measuring the uncertainties in quote unquote real life.

So for people who are not trained like a physicist and don't nerd out about statistics all the time, what's a sort of intuitive or easy way to start to think about these uncertainties? What do you recommend? I know you have a wonderful YouTube channel where you teach people to think like a physicist and think about uncertainties. How should people get started? thinking about uncertainties like a physicist? Well, if you want to think about it the way physicists do, I guess I would explain how physicists arrive at those uncertainties.

So like a physicist who is conducting some kind of an experiment, they are going to want to produce a result, and they're going to want to produce an error bar that goes with that result that tells you what the uncertainty on that result is. Let's stop there for a minute and describe exactly what you mean there, like the error bar. So if I say, I've measured my speed to be 70, miles an hour with an error bar of five. What does that mean? What does the error bar mean? What am I

saying when I say five there? So the error bar, if you're at least thinking about it from a, from a physicist point of view, is you've thought about what the possible sources of error that can come in, the ways that you could be wrong, the ways that you could measure it incorrectly. And you've done some kind of analysis or thinking about it to add those sources together and figure out roughly, typically how much you would be wrong by. So does that mean that if I measure my speed to be 70 plus or minus five, that the true speed is definitely within 65 to 75? Like does the error bar completely define the possible extent of the truth?

Absolutely not. It's a typical value. It's a typical value for the difference between the true value of something and the value that we measure. And we don't know whether the value we measure is above the true value or below it. And we don't know if the difference between the true value and our measured value is larger than that error bar or smaller than that error bar in an instance of a specific measurement. What that error bar means is that's a typical value for how the true value and the measured value would disagree. Right. And so if we quote 70 plus or minus five, or let's talk about, you know, politics, Joe Biden's polling numbers are 44% with a uncertainty of 3%, right? That doesn't mean that his true value is between, you know, 44 plus 3 and 44 minus 3. It means that there's a 60% chance that it is. And then therefore there's a 32% chance that it isn't, right? So the air bar,

tells us, as you say, roughly the size of the expected difference between the truth and the measured value, but it doesn't bound it, right? It doesn't tell us it's exactly within that. I see this sort of misunderstanding all the time in political journalism, you know, where they have two candidates. And if they're separated by 10 points and the uncertainty is four points, then they say, okay, it's definitely a lead. But, you know, it still could be the opposite or two candidates who are near each other, but within the statistical uncertainty, they call it a tie, even though if one of them has a larger value, we're pretty sure that, you know, we're somewhat sure at least that they have more support. I think there's a lot of misunderstanding

about what this error bar means. It seems so much more definitive, right, than the way that we mean it. It's really, as you say, just a typical value. It tells you roughly the scale of how far off you might be. So when people are out there reading a scientific result, right, when they're not measuring their speedometer, when they're reading a paper about a new particle and they come across something, what should they be asking themselves? What should be they should, what should they be looking for in that article to help understand like how uncertain are physicists about this new squeakly on particle? Well, I mean, at the most basic level, if the result is, is measuring something and saying

this value was larger than what we were expecting from our prediction if the particle didn't exist. The first question is to ask, well, what was the difference between what was observed and what was expected if the particle didn't exist? and then how does that difference compare to the quoted uncertainty? So if that difference is a lot larger than the quoted uncertainty, then we would tend to think that something interesting is going on. Maybe it's particle discovery, hopefully it's particle discovery,

but it always could be that something has gone wrong with the experiment that we don't understand. On the other hand, if the difference between what's observed and what's expected from the no new particle hypothesis, hypothesis, if that difference is not much larger than the uncertainty, or maybe it's only a couple times the uncertainty, then it's probably a little bit too early to get excited. We need more data and we need more results and possibly more experiments to look at it before we say anything definitive. Right. So then let's make a concrete, go back to our coin that we're tossing or the turtle that we're flipping. Let's say I flip the coin two times and I get two heads. So it's

100% heads, right? And then I go off and I write a paper saying, look, my coin is 100% heads. It's totally unfair. And you're the reviewer. You might look and say, all right, you know, but the prediction for a fair coin is 50%. The prediction for an unfair coin is, you know, something above that. But the uncertainty in your measurement is huge because you only flipped it twice, right? So yes, you measured 100% heads, but you could have also gotten 50% heads or 75% heads or whatever. And so you're saying if I go back and then flip it, a million times and I still get a million heads, that that's very different, right? And I think people can understand that.

That's much more compelling. If you get a million heads in a row, it's very unlikely to be a fair coin. And that's the difference, right? That there's a smaller uncertainty on my measurement of 100% heads. If I flip it a million times and if I flip it two times. And so the two different hypotheses of like a fair coin at 50% heads and an unfair coin and 100% heads, the difference there is now large. compared to the uncertainty, whereas it was small when I only flipped it twice.

Yeah, when you only flip it twice, I mean, even if the coin is fair, the probability is 25% it's going to come up heads both times. So it's important to not jump the gun and think that you've discovered something amazing when you might just have a quarter. Exactly. On the other hand, if you flip the coin 10 times and it comes up heads each time, well, then, you know, you start to think maybe something's up. And if you do it 20 times, then you might start to really think that something up.

And certainly if you flip it a million times, then you're pretty darn certain to something's up. Exactly. But I think it's fascinating that even now, for example, we can't say 100% definitively that the Higgs boson exists. Like we've taken so much data. We have so much evidence. And yet, still, it could all be a fluctuation, right? It could all just be, we could be that situation where we flipped a fair coin a million times and gotten a million heads in a row.

It can happen. And we could have been fooled by our data. We don't have like a pile of Higgs bosons we can point to and say, these are them folks. We just have, you know, basically the result of flipping a bunch of coins and seeing it come out weird compared to our prediction for no Higgs boson. So in principle, I mean, we don't really know that any particle is out there. Though I guess as we continue to make collisions and analyze data, we get more and more certain, but it's sort of like approaching the speed of light, right? You can never actually get there.

That's right. You can never be absolutely certain of any scientific result that you produce. But on the other hand, you can also not be absolutely certain that this chair sitting next to you actually exists because, of course, your eyes could have malfunctioned. You could be dreaming. So 100% certainty is a dream. It's an illusion. It's not something we can ever achieve. Exactly. Right.

I'm not 100% sure we're having this conversation right now. Exactly. Great. Well, so tell us more about your project, Think Like a Physicist, where people can go to learn more about it and learn more about thinking like a physicist. Yeah. So I have a YouTube channel. It's called Think Like a Physicist.

And the idea behind my channel is I wanted to take the statistical methods, especially, also the other methods the physicists use, but especially the statistical methods that physicists use. use. And I wanted to explain them in a way that I hope non-scientists can understand. And the idea is that I would like for people when they read about a scientific result and it has an error bar on it, that they would be able to have a better understanding of what that error bar means. And also that that way they can understand scientific results in context. For example, if you hear that one experiment does a measurement of a certain quantity and it agrees with the

standard model, and then three years later you hear that another experiment measured the same quantity and they got a different result. You know, it might be because the second experiment had a smaller error bar than the first one did. And so you can understand results in context better. So basically, I go through a lot of the basic statistical techniques that physicists use, and I hope that I explain them in a way that people can understand. And so, yeah, I would very much like the public to know more about these topics so that they can understand what we do a bit better. Great. Tell us one more time where people can find you. Yeah, my YouTube channel is called Think Like a Physicist. Great. Well, thanks very much, Jen, for coming on the podcast

today and thinking like a physicist with me. Appreciate it. Thank you so much. It's been great. All right. Interesting interview. I like, though, how she talked about uncertainties and how, you know, this concept, you know, spills into our everyday lives, especially when it comes to things like policies.

But people don't seem to have a pretty good understanding of that. Maybe they should talk to statisticians, not physicists, both politicians. How to think like a statistician, exactly. How to probably think like a statistician. How statisticians likely think. Likely think or think likely? The likelihood of me finding a good joke is low. Yeah.

We'll make that the null hypothesis. All right. An interesting perspective, though, about how to think like a physicist. Now let's talk to someone whose job it is to, I guess, reintroduce physicists out into the world, sort of like those wildlife experts who have to retrain animals to live in the wild. Is that kind of her job? Yeah, exactly. Or re-educate prisoners who are emerging from isolation. Oh my goodness. I guess academia is sort of like a prison. There

are walls, towers, you know, small rooms where people are sitting all day. The food is terrible. Yeah. Does your door have bars in it as well? And the average sentence is like six to seven years, right? Oh, I got a lifetime sentence over here. You did a capital discovery. All right, well, we'll get to Daniel's interview with physicist Kathy Copic about what physicists can do outside of physics. So let's dig into that. But first, let's take another quick break. LaGuardia Airport. The holiday rush, parents hauling luggage, kids gripping their new Christmas toys.

Then, at 6.33 p.m., everything changed. There's been a bombing at the TWA terminal. Apparently the explosion actually impelled metal glass. The injured were being loaded into ambulances. Just a chaotic, chaotic scene. In its wake, a new kind of enemy. emerged. And it was here to stay. Terrorism. Law and order criminal justice system is back. In season two, we're turning our focus to a threat that hides in plain sight. That's harder to predict and even harder to stop. Listen to the new season of Law and Order Criminal Justice

System on the IHeart Radio app, Apple Podcasts, or wherever you get your podcasts. My boyfriend's professor is way too friendly, and now I'm seriously suspicious. Oh, wait a minute, Sam. Maybe her boyfriend's just looking for extra credit. Well, Dakota, it's back to school week on the OK Storytime podcast, so we'll find out soon. This person writes, my boyfriend has been hanging out with his young professor a lot. He doesn't think it's a problem, but I don't trust her. Now, he's insisting we get to know each other, but I just want her gone.

Now, hold up. Isn't that against school policy? That sounds totally inappropriate. Well, according to this person, this is her boyfriend's former professor, and they're the same age. And it's even more likely that they're cheating. He insists there's nothing between them. I mean, do you believe him? Well, he's certainly trying to get this person to believe him because he now wants them both to meet.

So, do we find out if this person's boyfriend really cheated with his professor or not? To hear the explosive finale, listen to the OK Storytime podcast on the IHeart Radio app, Apple Podcasts, or wherever you get your podcast. Have you ever wished for a change but weren't sure how to make it? Maybe you felt stuck in a job, a place, or even a relationship. I'm Emily Tish Sussman, and on she pivots, I dive into the inspiring pivots of women who have taken big leaps in their lives and careers. I'm Gretchen Whitmer, Jody Sweeten. Monica Patton. Elaine Welter-off.

I'm Jessica Voss. And that's when I was like, I got to go. I don't know how, but that kicked off the pivot of how to make the transition. Learn how to get comfortable pivoting because your life is going to be full of them. Every episode gets real about the why behind these changes. and gives you the inspiration and maybe the push to make your next pivot. Listen to these women and more on She Pivots, now on the IHeart Radio app, Apple Podcasts, or wherever you get your podcasts. The U.S. Open is here, and on my podcast, Good Game with Sarah Spain, I'm breaking down the players from rising stars to legends chasing history.

The predictions will we see a first time winner and the pressure. Billy Jean King says pressure is a privilege, you know. Plus, the stories and events off the court and, of course, the Honey Deuces, the signature cocktail of the U.S. Open. The U.S. Open has gotten to be a very fancy, wonderfully experiential sporting event. I mean, listen, the whole aim is to be accessible and inclusive for all tennis fans, whether you play tennis or not. Tennis is full of compelling stories of late. Have you heard about Icon Venus Williams' recent wildcard bids or the young Canadian, Victoria Mboko, making a name for herself? How about Naomi Osaka getting back to form?

To hear this and more, listen to Good Game with Sarah Spain, an Iheart women's sports production in partnership with deep blue sports and entertainment on the IHeart Radio app, Apple Podcasts, or wherever you get your podcasts. Presented by Capital One, founding partner of IHeart Women's Sports. All right, we're asking the question how to think like a physicist that sounds like a great t-shirt. Think like a physicist. Yeah, or a bumper sticker.

And then the bag says, nap like a physicist too. Well, Daniel, you got to talk to another physicist who sort of does something else that's kind of interesting. Yeah, Kathy Kopek is an old friend of mine. She and I did experimental particle physics together many years ago, but then she ventured out into the world instead of continuing in physics research. And for many years, her job was to help people. who have PhDs in physics, find jobs outside of physics, mostly in data science and in machine learning industry, which has been gobbling up a lot of physics PhDs.

Well, does she do this for a company or as a consultant or what? Yeah, there was a company called Insight Data Science, which was like a boot camp, it would take people from physics, give them a little bit of an introduction into the tools of business or industry or at least help them translate their experience so they knew how to talk about it. I find that one of the biggest barriers between fields is just vocabulary. You know, everybody talks about the same thing using different words. And so if you just learn to translate your work, your expertise into somebody else's language,

you can help them understand how you might be useful to their company. Right, right. You just have to say things like, I worked on a model to understand the universe, and then all scientists will understand you. I'm going to circle back and connect with stakeholders so that we can maximize shareholder profit, right? That's my attempt to speak corporate world. That's how you think they talk in corporate America? I mean, based on the sitcoms I watch, I mean research I've done, then yes.

Is that part of thinking like a physicist is doing your research on TV and YouTube? That's just part of living, man. Now you said Kathy used to do that. What does she do now? Yeah, now Kathy has a bunch of jobs. She's teaching at Berkeley and at Stanford and she has her own consulting company, helping people find physicists to work in their teams. All right.

Well, here is Daniel's interview with Dr. Kathy Copick on how to think like a physicist and how to get a job as a physicist or how to pretend you're not a physicist to get a job. Is that a great question? Yeah, how to get a non-physics job if you are a physicist. There you go. All right. So then it's my great pleasure to introduce to the podcast, my friend and colleague, Dr. Kathy Kopic.

Kathy, thank you very much for joining us today. Oh, thanks so much. I'm really excited. Tell us a little bit about who you are, what your background is. You have a special and unusual journey. Oh, yeah, thanks. Sure. So I was a physicist, am a physicist.

I don't know if we talked in the past or present tense, but I worked in experimental particle physicists for a long time. First, actually in California, on Babar, then outside Chicago on the CDS experiment at Fermilab. Then I was at CERN for a long time, as were you. working on the Atlas experiment with Columbia and then with Berkeley. So I was in physics for a long time studying the smallest things. And then I worked in the last 10 years a lot on helping teams outside of academia think about how they use data in lots of ways and how they hire their teams.

I worked for about seven years at the Insight Data Science Fellows program working with a lot of scientists making a transition from working in science to working. in tech and business, and worked with literally thousands of people making career transitions to literally hundreds of companies. And now I work as a consultant, fieldwork partners with a friend, and we help teams do the same kind of things as consultants. So this may seem like an obvious question, but why are people making a transition? You're getting a PhD in particle physics.

You're studying the secrets of the universe. Why are people then going to work for healthcare companies or whatever? Sure. Yeah. I say two main reasons. One is genuine interest. You know, people are excited about and curious about lots of things. It's one of the things that drives them to be scientists in the first place. And I talk to lots of people who are interviewing with our program to make that transition. And people are like, you know, I've done this thing for a long time and I really like doing it. And now I'm interested in doing something else. And so I think there is definitely genuine interest and curiosity about what it's like. And then, I think on the other side, you know, the job market for academics is very hard. Getting that next position, that next position, both, it's very challenging. There's fewer and fewer positions at every level. And so naturally people have to exit academia.

And also there's often fewer choice, less choice of each level. So, you know, where you're going to live, what you're going to work on, who you're going to work with. Getting those positions is pretty tough. And so not just in physics, but in all fields across academia, people transition out after. their undergrad, after their PhD, after postdocs, and sometimes at the faculty level as well. So we're always telling our students, hey, come to a PhD in physics because you're going to learn important skills about thinking and you're going to train yourself to be a smart person. And those skills are broadly applicable. And I've never worked outside of academia. So I don't know

if I've been lying to people. Tell me, have I been lying to people? What skills do physics PhDs learn that are actually useful outside of particle physics? Sure, sure. I, I, I, I, do not think you are lying to people. I do think those skills are genuinely useful. And you can tell when you see where people go on to work after they've been in physics, a lot of times in physics and also some other places. The skills that people learn, I think there's three main things. The first one is just trying to figure out how to break a problem into smaller problems

and questions, thinking about like, okay, there's this big question we have. Like, what's the smallest thing in the universe, the thing that both you and I, worked on. So the big question, but then, okay, how do I break that down into things that can be measured or things that we can write a theoretical model for? So breaking big questions into small questions, it's a really important skill if you want to ask questions about the universe, but also if you want to ask questions about a business or how many beds in a hospital are likely to be available on a given day, given the procedures and things that are coming up and how uncertain is that people will get discharged on a certain day.

If you're trying to build a model of anything, not just in science, but also in the real world, breaking a big problem into small questions is a big, big skill. Let me drill into that a little bit. I understand it's important to know, like, how to get started on a problem. You're working for a company. They gave you this project. They're like, build us this widget that does that thing.

And you need to know what to do on day one so that after day 90, you're there. Why is that something the physicists in particular are good at? Like, how does studying the nature of the universe make you good at learning how to break down problems? Yeah. A lot of the things that physicists are good at are things scientists in general are good at asking a question, breaking it into problems. But physics in particular, I think both people who are drawn to physics and physics education reinforced the same thing, which is not just being a little bit curious, but being like really curious. You know, you're not just stopping at some level that's like a surface level or where there's maybe approximations or you're like really continuing to either you personally because it's how you think about the world or in your education working with your teachers and mentors are like really, really, really drilling down to these questions to the really basic pieces of it.

And I think that is unique to physics. It's, you know, the people who study physics have chosen to kind of, like, continue down that path of questions to where, you know, there's not, things are not even alive anymore. It's like, you're studying one atom or studying how galaxies form or some, like, it's very complicated, basic question about the universe. So, and I think it's true. Everybody takes a question and breaks it into smaller questions in science, but in physics, really, really trying to get to the most. basic things about how the world works, right? All right. So I interrupted you.

You were telling us the good things that physicists learned. And number one is breaking things into pieces and number two was. Breaking things into pieces. Number two, I think, especially in experimental physics, working with very large general purpose data sets. And a lot of parts of science, you know, every experimental science, people have data sets. Sometimes they're very large. But a lot of scientists create those data sets themselves in a lot of science.

smaller group. So they have, you know, they're trying to study one thing about how a certain bacteria does something or, you know, in their own lab and they kind of know, oh, maybe the data from July is no good because the temperature was off or something. You know, they know the data often better because they created it. In physics, especially in experimental particle physics where we both worked, but also astrophysics and lots of other areas of physics, people have these very collaborative general purpose data sets that are meant not just to answer one question, but you can ask so many questions from them. And they're messy, these detectors that are built and have problems, some parts not working.

Maybe that's showing up in some initial variables, also in some calculated variables down the road. You have to make corrections. Working with that kind of general purpose data is a real skill because that real-world data that you might study if you're working at a business or nonprofit or asking some questions about non-academic data. Very similar to the, so that's a skill I think people learn in physics. And then the third one, I would say, is this collaboration working in, you know,

and not all collaborations are as big as the ones that we worked on, most are not. But working, everybody who's working in physics and in science is really trying to figure out what's already been done, who has domain knowledge that might help me figure out the piece of it that I'm working on, how do I share? what I'm working on in a way that can make sense to build some collaboration. How do I share my results back? How do I write about and speak about what I learned in a way that's going to help advance the research on this question?

So all of those, I think, are really important. So in understanding what it's like to think like a physicist, I think one thing that's helpful is understanding where physicists find their skills useful. So you told us the kind of skills we learn, but where do people who have been trained in particle physics end up making impacts in the world outside of particle physics? Where are these skills helpful? Yeah, I think really everywhere, and I'm not just like trying to make it seem just everywhere.

But in all the kinds of tech companies that you can think of that are working today, like people are doing interesting work. Also small places, nonprofits I mentioned initially, I mentioned this like people working at a hospital to try to figure out how to build a system. system that helps predict when patients are going to be coming in or not. People are working in pharmaceuticals, just really in every area. I think people are working. I mean, yeah, there's so many experimental particle physicists, too.

So many of us that people go in. And people are driven and curious to work on so many things that, yeah, just lots of places. And, you know, physics is a very good broad training, but we're not learning everything. When people go out into the world and try to work on actual practical problems with real deliverables and stuff, what are some sort of blind spots? What are some things that physicists don't learn that are useful in the rest of the world? Yeah, I think that all of those advantages, those superpowers that I talked about have some kind of reverse kryptonite, which is like being very curious and very detail-oriented and driven to like get to the very bottom of the question is a good instinct in physics.

that's important. But sometimes in the business world, you don't have the time or resources to like get really to the very bottom of something and you have to kind of step back and make an approximation or maybe we're only going to run this thing for a week and we're going to get as far as we're going to get. But at the end, what we're trying to do is like recommend the next song for someone or recommend the next movie for someone to watch. And so actually it's okay if like we don't understand everything about this. And so sometimes taking that step back and being like, you know, this isn't a six-month project or a six-year project. This is like a six-week project. And we're going to build something and we're going to ship it and it's going to be good

enough for that need. And there are areas where that's true. And then there are areas, you know, where like in health and health care where you don't want to make errors. And so I think people kind of, through their personality, might choose areas where it's okay to, you know, recommend the next song for someone they might not enjoy as much where it's not okay to to recommend a medication to someone that's not the right fit for them, right? If it's, you know, and there's still, usually in a healthcare setting,

there would be a doctor that would be the prescriber, but if you have a tool that's very biased or making wrong predictions for something that's really important, like healthcare, you know, there's less room for error. So you've helped a lot of people figure out how to go from particle physics to someplace in the real world where they can make a contribution.

How do you do that? How do you like get to know somebody and figure out like what are their strengths and weaknesses and how does it fit i mean you're basically like the yenta of you know particle physics and jobs but tell us about your process sure sure everybody is very different that's one thing that i enjoy about it so it's true you know some people need to grow or change in one area where other folks that's very different for them i think the first thing that that i try to ask is what motivates people what they're excited by you know some people are very excited by the impact in the real world and the people that might use or be

helped by the thing they're working on. Other folks are very excited about the technical tools themselves, like getting to use the most advanced tools and models and getting to work on something technically very exciting. Other people have worked very deeply and worked 10 years on one thing and are actually looking to do something more broad. They're like, oh, I want to learn about a lot of things. Some people love to interact with a lot of people. Some people want to be a little bit more like, I kind of want to be given the thing and do my own thing. And so I I think there's very different work for people depending on what they like and what they're interested in. And so once you know a little bit more about that, like, what are the constraints around the kind of jobs that people are looking for?

Then I think it's easy to recommend specific like, okay, well, and based on geography too, like there's just different kinds of jobs in different places in North America and the world. And so, okay, well, for you, it sounds like you're excited about this and you're living here and these are your, experiences, helping people describe what they've done and what they want to do next. People usually don't need to build new skills. They have a lot of skills. It's just they need to have some kind of exploration of the space of available things, what they want, what they have, how they can describe what they've done, and maybe demonstrate it in a different way, by talking about it differently.

Those are the main things I think I would do. So I've seen a lot of physicists end up like on Wall Street or in data science. These seem to be places like where that community has an appetite for. They're like, oh, yeah, we like hiring physicists or whatever. But tell us some other places where physicists might end up. Some, you know, maybe unusual or bizarre places, physics PhDs end up working out. Yeah, that's a good question. I do think people end up in a lot of places that,

Basically, anywhere where people are, like, building some models to help a system run better. So it could be, you know, things, education, educational software. People are trying to build ways to help kids learn to read and learn to do math. There's all kinds of games that people work on. Anything that you buy or sell clothes or, you know, any sort of products, any sort of recommendations for things that people are working on. anything in the healthcare industry, I talked about that a lot already, anything in the kind of broad tech you see, there's a ton of work right now on AI, certainly large language models, a lot of people from physics are working on those tools at all the places you can imagine.

There's really a lot of places. I can't think of one like, especially funny, like, oh, here's one that you can think of, but really in every area. media, fashion, people are working in all sorts of areas. People working on like optimizing, you know, underwear sizes and stuff like this. For sure, for sure. That's particle physics at work. That's right.

It's funny and it's a joke, but it's also true that like, I don't know, for me, finding clothes that fit is actually really nice. Yes, it's an important unsolved front. You can make a real impact in people's daily lives. I mean, it's like a little bit silly, but it's also true that there's a lot of, I think there's a lot of systems where people, have just done the same thing forever and having a fresh take on it can be helpful. Yeah, everybody's got like their favorite pair of jeans or their favorite pair of underwear and

there's a reason they fit right. It feels good. So there's this lore going around that I hear a lot that one of the reasons behind the 2008 financial collapse was that Wall Street went a little bit crazy with its modeling and that they were these crazy quants and most of them were ex-physicists who didn't really understand the system and just like, wrote a bunch of code that went crazy and destroyed people's lives. So what do you have to say of that? Did physicists cause the financial collapse or not? Probably not alone.

I'll say that. Do you think there's a superpower kryptonite that physicists are very interested in, you know, going down to the root causes of the basic, how do you take this problem, break it into the basic parts? And I think that the kryptonite version of that is, like, thinking that you can do that in any field for any topic without necessarily consulting and learning about the domain and knowledge of the practitioners or people that have worked in that area. There's a famous data science person, Drew Conway. We used to say physicists where, like, kind of like, wildebeests that would, like, run into an area that seems interesting, like, biophysics, right? It's like, oh, there's something interesting there.

All the physics. Here comes a lot of old, you know, ex-physicists who are like, will solve a lot of. all the problems. And so when I would give talks to physicists, I would say, don't be a wildebeest. Like, don't run into your area, to a new area. So these, maybe these 2008 physicists are kind of just like, I know, I'll break down this problem into these parts and look what I'm doing. Isn't it cool? But if there was a little bit more domain knowledge or thought around, how could this go wrong? How might this affect people? Why might we not do this? They could they could have avoided some bad outcomes.

All right. So maybe we're not totally guilty, just partially. Yeah. And so a lot of our audience are folks who like physics and like thinking about physics and have been listening to the pod and learning to think like a physicist and applying, you know, that mental model to questions about the universe. But what would be your advice for somebody out there who wants to take advantage of this way of thinking, somebody who's not necessarily trained as a physicist but wants to learn

to think like a physicist? what would be your advice for learning to think that way? Yeah, I think there's this, I'm sure you talk about the Drake's equation, which is used for thinking about where extraterrestrial life might be in the Milky Way, right? Is that right? You probably know much more than that. So that's the thing where you kind of are taking these pieces, anybody can look up the Drake equation or Drake's equation,

and taking these pieces and trying to put it together to get one answer. And I went to a business class where people were talking about using the same sort thing to model businesses or other processes where it's just trying to think about, anybody can think about what are the parts that come together to create some answer or some prediction. And so just take thinking about that, breaking something up into things that you can measure individually, you can think about individually, can really help solve a problem, whether it's a science problem, business problem, any kind of problem. All right.

And so then last question, a bit of a personal one. What do you miss most about actively working in physics? I don't say about being a physicist because I think you're always a physicist once a Jedi, always a Jedi. But what do you miss most about like working on particle physics other than working with me, obviously? I was going to say, I mean, you're choking. But I think I really, really did.

There's a very special, fun, exciting environment of being at the lab. in these big experiments and both at, you know, at Slack in California, Fermi Lab, Brookhaven, at CERN, that these labs just, it's really literally people from all over the world and having lunch together and the big cafeteria at CERN is called R1, restaurant one, a very creative name. I don't know if it still is. It's not named after someone now, is it still R1? Yeah, so R1. So if you're there for lunch or for coffee or at the end of the day, it's just really fun to run into so many people that you've worked with over your whole career, people who are getting into the field, people who are very senior.

You never know who's going to be there. Just having some food, drinking coffee, and getting to talk to people about what they're working on and also what they're doing and how they are. I just have very, very fun memories of hanging out there with all sorts of people. And yeah, no, it was a great time. So I would say just missing being with all the people that we used to work with and getting to meet new people. That's a really truly international environment, too, really fun.

It is fun to hear conversations in so many different languages. Yeah. I like running into the same really old Nobel Prize winners over and over again, introducing myself every single time because they don't remember me because they're like 150 years old. And I also remember one of the first times I was at R1 and you had some special trick for making an iced coffee and you showed it to me and Katrina. And for the rest of the summer, we were like, oh, let's get a Kathy. We called it a Kathy. Copacino. Copacino. That's right. Yeah. Yeah. Yeah. I made up, created at TGI Fridays. I don't know if you're looking for sponsorships, Daniel,

but TGI Fridays, the restaurant when I was a server there. created the copacino delicious thank you got us through that summer yeah they don't have they didn't have cappuccino machines at certain they had espresso machines but no no cabuccino machines so you got to figure it out all right well thanks very much for sharing with us how to think like a physicist and how to drink coffee like a physicist really appreciate it we'll put the put the recipe uh people can contact us in the show notes for a copacino yeah show notes exactly all right thanks kathy all right interesting uh talk there, Daniel. It seems like she's basically saying we all have skills.

Everybody's skills are different at least. Yeah, I think she probably aligns with you to think that like scientists are all curious thinkers and mental model builders and not even all physicists are the same. We all think differently and enjoy different parts of the process. And that can help you get a job, right? Yeah, because these are all skills that we could all use in every field probably. Yeah, exactly. And so in the end, thinking like a physicist is just like thinking like a scientist, being a curious person, trying to understand the world, being methodical about it, trying not to fool yourself with what the data is telling you. Yeah, and just trying to maximize your functionality to the stakeholders.

Exactly. Maximize shareholder revenue. Maximize physicist employment. Try not to cause any more financial collapses, please. All right, well, an interesting discussion about thinking like a scientist, thinking like a physicist. What are the commonalities and how things might be a little bit unique for people who pursue physics as a career? And for those of you out there not pursuing physics as a career, but who have discovered a love for physics. Keep doing it.

Keep thinking like a physicist or a scientist and keep being curious about the world and trying to make the whole thing click together in your mind. Yeah, but mostly just keep thinking, please. And keep listening to the pod. Thanks, everybody. We hope you enjoyed that. Thanks for joining us. you next time for more science and curiosity come find us on social media where we answer questions and post videos we're on twitter discord insta and now tick talk thanks for listening and remember

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