Speaker 1: Andy Daniel, Did you always want to be a paid physicist?
Speaker 2: Definitely not. When I was a kid, I did not
Speaker 2: want to be a physicist.
Speaker 1: Really, you knew what it was, but you knew you
Speaker 1: didn't want to be one.
Speaker 2: I don't think I understood what a scientist was well enough.
Speaker 2: But when I was a kid, I wanted to be
Speaker 2: an explorer. I wanted to get on a ship and
Speaker 2: find some new island and name it after myself.
Speaker 1: You just want to get out of Los Alamos.
Speaker 2: Main purpose here, Yeah, though you can't really take a
Speaker 2: ship out of Los Alamos because it's landlocked. So there
Speaker 2: were some basic problems in my thinking.
Speaker 1: Well, you could take a train and then a ship.
Speaker 1: But don't they say everyone's a physicists, especially little kids.
Speaker 2: Yeah, I think everybody is a scientist because they're curious
Speaker 2: about the world. And in the end I discovered that
Speaker 2: being a physicist it's kind of like being an explorer,
Speaker 2: except instead of discovering new continents, we're trying to discover
Speaker 2: new frontiers of knowledge.
Speaker 1: Instead of surfing waves out there in the sea, you're
Speaker 1: surfing the couch.
Speaker 2: Mostly, I'm clickly clacking my way to new shores of knowledge.
Speaker 1: Just don't get scurvy on your couch.
Speaker 2: I got a bowl of limes here next to me.
Speaker 1: Okay for with the tequila and the margaritas. That's for
Speaker 1: after work, after work work these days? What's the difference. Hi,
Speaker 1: I'm Hori. I'm a cartoonist and the author of Oliver's
Speaker 1: Great Big Universe.
Speaker 2: Hi, I'm Daniel. I'm a particle physicist and a professor
Speaker 2: at UC Irvine, and I want to teach people to
Speaker 2: think like a physicist.
Speaker 1: Wait, I'm confused. If everyone's a physicist, aren't just teaching
Speaker 1: people to think like humans?
Speaker 2: Yeah, basically, I'm done. I can retire. It's after work time, margarita.
Speaker 1: I know, let's get the shots going.
Speaker 2: No, I think everybody does have curiosity. But you know,
Speaker 2: it took us a while to figure out some tips
Speaker 2: and some tricks to effectively extract knowledge from the universe
Speaker 2: rather than just like, you know, making up cute stories
Speaker 2: to satisfy our curiosity.
Speaker 1: Right, it probably took a while to get paid to
Speaker 1: do it too.
Speaker 2: Yeah, that's certainly true. A lot of the big names
Speaker 2: in the history of science were men of leisure, you know,
Speaker 2: operating on their trust funds or daddy's bank account.
Speaker 1: Who do you think was the first professional physicists?
Speaker 2: You know, science as a profession is not actually that old.
Speaker 2: It's something like in the late eighteen hundreds that people
Speaker 2: started to call themselves scientists and get paid to do it.
Speaker 2: There are money to hire people to do this kind
Speaker 2: of research. Until then, it was you know, natural philosophers
Speaker 2: and people just sort of like curious, poking around in
Speaker 2: their own laboratories. Yeah, but scientists as a job is
Speaker 2: not much more than like one hundred years old.
Speaker 1: WHOA. So even the word science is relatively new.
Speaker 2: Yeah, exactly. If you ask like Gaos or Newton or
Speaker 2: leading It or Aristotle, they certainly would not call themselves
Speaker 2: a scientist. That's a new word.
Speaker 1: Or maybe they did it on purpose. They're like science,
Speaker 1: No thanks, it's a new fangled thing that all the
Speaker 1: kids are talking about. I prefer to be a natural philosopher.
Speaker 1: But anyways, welcome to our podcast, Daniel and Jorge Explain
Speaker 1: the Universe, a production of iHeartRadio.
Speaker 2: In which we do our best to demonstrate what it's
Speaker 2: like to think like a physicist. We take a physicist
Speaker 2: approach to dismantling the whole universe, understanding all of its
Speaker 2: little bits, building mental mathematical models to try to explain it,
Speaker 2: asking questions of those models, and then wondering what does
Speaker 2: it all mean anyway?
Speaker 1: Yeah, because, as we talked about before, the universe belongs
Speaker 1: to everyone, and asking questions is everyone's job, but a
Speaker 1: few people get to do it as a career.
Speaker 2: Get to Yes, exactly. It's definitely a treat and a privilege.
Speaker 1: Well, you get paid to do it, I guess, and
Speaker 1: to do that, there's a certain mindset you have to have,
Speaker 1: right in order to be part of the profession.
Speaker 2: Yeah, there definitely is a way of thinking that's sort
Speaker 2: of like a physicist way of thinking. And I see
Speaker 2: this because people who are trained as physicists and then
Speaker 2: go out into the world and work in other areas
Speaker 2: chemistry or engineering or computer science still take with them
Speaker 2: a certain mindset, a certain way of approaching problems, which
Speaker 2: can be really really helpful and useful or also sometimes
Speaker 2: frustrating for their colleagues.
Speaker 1: Yeah, no, I can totally relate. I think that also
Speaker 1: the same is for engineers. You know, anyone who studied
Speaker 1: engineering definitely thinks like an engineer is trying to think
Speaker 1: it a certain way and a certain mindset about tackling
Speaker 1: problems for sure.
Speaker 2: Yeah, absolutely take an engineering approach to cartooning.
Speaker 1: For example, Yeah, whenever I draw a bridge, I mean
Speaker 1: I really put some calculations behind it, why to make
Speaker 1: sure it doesn't fall down?
Speaker 2: Yeah, I know all those cartoons could be injured. I mean,
Speaker 2: think about their families.
Speaker 1: Yeah. I usually build in a safety factor of like
Speaker 1: two or three to every cartoon, just in case. But yeah,
Speaker 1: but professional physicists do think about things in a very
Speaker 1: different way than the rest of us. And so that's
Speaker 1: the question we'll be exploring today. So to the on
Speaker 1: the podcast, we'll be taggling how to think like a physicist?
Speaker 2: And I'm not sure if this should be like an
Speaker 2: instruction manual or like.
Speaker 1: A warning, Oh why what can happen?
Speaker 2: You know, like watch out for these signs that you're
Speaker 2: thinking like a physicist, or like, hey, would you like
Speaker 2: to think like a physicist? Here's steps one, two, five.
Speaker 1: Well, I guess if it was the former, we should
Speaker 1: title the eposite, hard to Not Think like a Physicist?
Speaker 1: How to avoid thinking like a physicist.
Speaker 2: We're going to get into the positives, I'm sure, but
Speaker 2: you know, there is this lore that sometimes physicists oversimplify things.
Speaker 2: They're like, come into a new field, They're like, oh,
Speaker 2: these can just approximate this with a sphere, maybe a
Speaker 2: line on it or whatever. There's this urban legend that
Speaker 2: physicists being too simplistic or the cause of the two
Speaker 2: thousand and eight financial collapse, for example. So you know,
Speaker 2: there are potentially some dangers to applying physics thinking to
Speaker 2: the broader world.
Speaker 1: Daniel, I wonder if you're overestimating how much people think
Speaker 1: about physicists.
Speaker 2: Probably I definitely don't have a clear view of that.
Speaker 1: I mean, I think for an urban legend to exist,
Speaker 1: do you sort of need urban people talking.
Speaker 2: About Maybe that's just an urban legend within physics, maybe
Speaker 2: nobody else.
Speaker 1: It's like a yeah, just have issues.
Speaker 2: We definitely do.
Speaker 1: But it's an interesting question to ask if you're thinking
Speaker 1: about following a career in physics, or wondering what is
Speaker 1: the job entail and what kind of mindset do you
Speaker 1: have to have in order to do it at a
Speaker 1: research university or to become one, or to get a
Speaker 1: degree in.
Speaker 2: It, or if you're just an armchair physicist, if you
Speaker 2: like thinking about the nature of the universe and making
Speaker 2: progress and over the years, maybe while listening to this podcast,
Speaker 2: you've been putting together your own personal mental model of
Speaker 2: the universe, asking questions, trying to click it together, coming
Speaker 2: to a holistic understanding of how things work. In that case,
Speaker 2: you might have picked up a few of the tricks
Speaker 2: of thinking like a physicist.
Speaker 1: Well, as usually, we were wondering how many people out
Speaker 1: there had thought about this question, had maybe wondered what
Speaker 1: it's like to be a professional physicist and what kind
Speaker 1: of mental skills you need to be one.
Speaker 2: Thanks very much to everybody who answers these random questions.
Speaker 2: Love hearing your thoughts. Please don't be shy if you
Speaker 2: want to join the group, just write to me to
Speaker 2: Questions at Danielandjorge dot com.
Speaker 1: So think about it for a second. What do you
Speaker 1: think it takes to think like a physicist? Here's what
Speaker 1: people have to say.
Speaker 3: A physicist must think of small and apply it to
Speaker 3: the infinitely large universe, and that's not easy to do.
Speaker 3: Hence the podcast for the rest of us.
Speaker 4: What if that's an expression? I haven't heard of it before,
Speaker 4: so it's value. I think it probably would refer to
Speaker 4: someone being very practical, someone following the scientific method very dogmatic, accurate.
Speaker 4: But then some theoretical physicists that are a bit wacky
Speaker 4: in what they come up with, so possibly a little
Speaker 4: bit of that too.
Speaker 2: I think like a physicist is to be asking questions
Speaker 2: and be relentless in your quest for an answer. I'd
Speaker 2: say thinking like a physicist means being curious and searching
Speaker 2: for answers through trial and error and experiments.
Speaker 5: This means there's our podcast about physicists, and to do
Speaker 5: it with your cartoonist friend.
Speaker 6: Basically, to think like a physicist means if you discover something,
Speaker 6: you get to really terrible name that doesn't make sense.
Speaker 5: I think it means to contemplate matter and energy and
Speaker 5: their interactions with one another.
Speaker 7: Well, from the episodes that I have listened to so far,
Speaker 7: I would that to think like a physicist means to
Speaker 7: be inquisitive, to try to make connections between different aspects
Speaker 7: facets of life, and wondering why and.
Speaker 2: Trying to.
Speaker 7: Better understand and explain the phenomena we see throughout our
Speaker 7: daily lives.
Speaker 1: All right, I like some of these answers. I guess
Speaker 1: we're done because one of them said, we just need
Speaker 1: to start on a podcast about physics.
Speaker 2: And then give everything you discover a terrible name. These
Speaker 2: are some juicy answers.
Speaker 1: I guess people have been listening to our podcast.
Speaker 2: I love these answers because there's so meta. They tell
Speaker 2: me basically what people have learned from listening to the
Speaker 2: podcast for all these years. It's fantastic.
Speaker 1: Well, hopefully people are thinking a little bit more like scientists,
Speaker 1: like rational thinkers because of this podcast, and also maybe
Speaker 1: learning a little bit more about the universe and how
Speaker 1: it all works down to the atomic level and the
Speaker 1: galactic level.
Speaker 2: Yeah, and not just absorbing facts and little bits of knowledge,
Speaker 2: pieces of information, but also training yourself into how to
Speaker 2: accumulate more information, how to fit those pieces of information together,
Speaker 2: how to think about them. Science is more than just
Speaker 2: what we've learned. It's how we're going to learn more.
Speaker 1: How are we driving the distinction here between physicis and
Speaker 1: just a regular scientist or do you mean how to
Speaker 1: think like a scientist?
Speaker 2: Yeah, it's a great question. I don't know the answer
Speaker 2: to that. I'm probably not even the right person to
Speaker 2: answer the question of how do physicists think because I'm
Speaker 2: stuck in that mindset. I can't really see outside of
Speaker 2: it to understand how other people think. But when I
Speaker 2: mean chemists or biologists or economists, I do notice that
Speaker 2: answer and ask questions in a different way. There's something
Speaker 2: I have more in common with other physicists than I
Speaker 2: have with other scientists. So there's something to it.
Speaker 1: All, right, Well, let's dig into it. What do you
Speaker 1: think is specific about how physicists think.
Speaker 2: I think some of it comes from the fundamental motivation
Speaker 2: and the assumptions that underlie physics. Like the goal is big.
Speaker 2: We want to understand the universe. We want to figure
Speaker 2: it out. And the assumptions are pretty basic. They're like, look,
Speaker 2: the universe is understand a bull, and we can't describe
Speaker 2: it with mathematical laws. We can build a mental model.
Speaker 2: The model should follow those laws, and we can use
Speaker 2: it to like predict the future and to understand the
Speaker 2: nature of the universe. You know, inherent in that is
Speaker 2: that we are simplifying the universe. We're taking all these
Speaker 2: observations and the weaving them together into a story. That's
Speaker 2: what the mathematical model is. We're saying, here's how this works,
Speaker 2: here's what's really happening behind the scene. So there's sort
Speaker 2: of like an ambition there to say, like we can
Speaker 2: describe the basic elements of the universe, whereas, and again
Speaker 2: I'm not an expert in other fields, you know, they
Speaker 2: feel a little bit more zoomed out, so they're not
Speaker 2: always as ambitious about like the fundamental understanding. They're describing
Speaker 2: things as sort of a higher level, which again still
Speaker 2: requires mathematical modeling and great precision. It's not a question
Speaker 2: of like precision or rigors, just a question of like
Speaker 2: the ambition the context of the questions you're asking.
Speaker 1: Well, well, are you saying that other scientists are not
Speaker 1: as ambitious?
Speaker 2: I think maybe philosophically, physics and at least fundamental physics
Speaker 2: and particle physics is asking more ambitious questions than other fields. Yeah,
Speaker 2: I think they have deeper and broader implications again philosophically.
Speaker 1: Right, right, So you think your topic of research is
Speaker 1: more important than other scientists because you're a physicist. I'm
Speaker 1: just saying there might be a little bit of a
Speaker 1: bias here at Daniel.
Speaker 2: No, it's totally reasonable to dig into that. I wouldn't
Speaker 2: say more important. You know, somebody who's developing new techniques
Speaker 2: to develop green energy, for example, they're not answering deep
Speaker 2: and fundamental questions about the nature of reality. But they're
Speaker 2: improving people's lives and maybe saving the planet, so that's
Speaker 2: arguably much more important. But I think in terms of
Speaker 2: the philosophical context of our lives, particle physics and fundamental
Speaker 2: physics is answering those questions. Whether that's important or not
Speaker 2: is totally subjective, you know, whether it has value. Every
Speaker 2: kind of science is answering different kinds of questions, giving
Speaker 2: different kinds of insight into how the universe works. For me,
Speaker 2: at least one of the appeals of fundamental physics are
Speaker 2: these philosophical implications of it.
Speaker 1: Right, Well, I think, you know, most scientists would agree
Speaker 1: that what they're doing is also trying to understand and
Speaker 1: explain the world. I wonder if maybe a lot of
Speaker 1: the difference is just in the topic and the kinds
Speaker 1: of things that you're looking at the scope of it,
Speaker 1: or the kinds of phenomena you're looking at.
Speaker 2: Yeah, I think that everybody is doing the thing they
Speaker 2: think is most interesting and most exciting, and that's very personal.
Speaker 2: Right the person who's like crouching in a rainforest watching
Speaker 2: spiders crawl up twigs for hours and hours a day,
Speaker 2: is deeply fascinated by that and chose to do that
Speaker 2: instead of economics or psychiatry or whatever for a reason.
Speaker 2: And that's totally cool. So you're right, and the choice
Speaker 2: of topic is very, very personal. But I think the
Speaker 2: choice of topic also sometimes leads to a different way
Speaker 2: of thinking. Like I think, because we're trying to ask
Speaker 2: fundamental questions and deep questions about the universe, we feel
Speaker 2: like we can touch onto some sort of mathematical purity,
Speaker 2: that there is maybe mathematics that describes this that we
Speaker 2: can drill down into and reveal, you know, somebody who's
Speaker 2: studying like hurricanes. You know, we don't have any mathematics
Speaker 2: that describes hurricanes. So we can do some simulations, but
Speaker 2: we're sort of at a loss because of all the
Speaker 2: chaos and the details. But when you zoom down into
Speaker 2: the fundamental firmament of the universe, we hope maybe there
Speaker 2: is some mathematics there that can describe what's going on.
Speaker 2: And so that's I think why physicists tend to build
Speaker 2: these mental mathematical models, sometimes too simplified, you know, hence
Speaker 2: the famous spherical cowjoke, which I don't know, maybe that's
Speaker 2: only a famous joke within physics.
Speaker 1: You tell me, I've never heard of that before. But
Speaker 1: you know, I think all scientists would say that what
Speaker 1: they're doing is fundamental as well. Like, if you're studying spiders,
Speaker 1: you're probably thinking about the different ways that life can form,
Speaker 1: or the different factors that go into creating life and
Speaker 1: the factors that shape live That seems pretty fundamental as well. Yeah,
Speaker 1: and ambitious.
Speaker 2: M h. And if anything, I think you probably have
Speaker 2: a lot more insight into this than I do, or
Speaker 2: than most people, because you interact with so many different
Speaker 2: kinds of scientists and obviously you've been spending a lot
Speaker 2: of time learning about physics and decoding the brains of
Speaker 2: physicists but also other scientists, And so from your perspective,
Speaker 2: I'd be very curious to hear, like, do you think
Speaker 2: physicists think differently? Is the mind of physicists trained at
Speaker 2: different skills? Do they take a different approach or all
Speaker 2: scientists just one category?
Speaker 1: For you? You know, I think that if you're a scientist,
Speaker 1: you're probably trying to figure out how the world and
Speaker 1: the universe works. You're just asking questions about different phenomena
Speaker 1: in it. You know, if you're someone who studies hurricanes.
Speaker 1: You're trying to understand how certain physical processes work and
Speaker 1: how they can come together to create large effects. For example,
Speaker 1: that seems pretty fundamental as well or as fundamental as
Speaker 1: asking you know what an atom is made of of?
Speaker 2: Yeah, can spiders come together to make hurricanes? Wouldn't that
Speaker 2: be awesome? And shouldn't we pitch that show to the
Speaker 2: Discovery Channel?
Speaker 1: Spider NATO's.
Speaker 2: Spider Cane.
Speaker 1: So I don't know, Sorry, science Spider NATO's sounds like
Speaker 1: a winner.
Speaker 2: Yeah, well, I can't tell you whether it's fundamentally different
Speaker 2: from the way other scientists think, because I'm not other scientists.
Speaker 2: Maybe you can comment, but I can try to keep
Speaker 2: you a little bit of an insight into the way
Speaker 2: I approach a problem with the way I think about problems,
Speaker 2: and that's this reliance on building a model. You know.
Speaker 2: I look at a science problem like where is that
Speaker 2: ball going to land after it comes off the bat?
Speaker 2: Try to predict that? And I think, well, to get
Speaker 2: that exactly right is way too complicated, And there's so
Speaker 2: many factors involved. There's the wind speed, there's that bird
Speaker 2: flying by, there's tufts in the air, et cetera, and
Speaker 2: so I build a simpler model of the universe. I say,
Speaker 2: toss out the real universe. Can we come up with
Speaker 2: a simpler version of the universe and ask the question
Speaker 2: in that universe, but build a model in such a
Speaker 2: way that the answer in the simpler universe is still
Speaker 2: relevant to reality. So can we extract the crucial details
Speaker 2: of the problem, put those into our model, and then
Speaker 2: use that to answer the question. So you know, you
Speaker 2: don't care, for example, about the color of the ball,
Speaker 2: you don't care whether some kid in the stand is
Speaker 2: eating ice cream. None of these details about glorious reality
Speaker 2: matter to answering this question. So you build the simpler
Speaker 2: model specific to that question because it's good at answering
Speaker 2: that question, not every other question. And you use that
Speaker 2: to answer the question. And you know, you can argue
Speaker 2: philosophically like is that model real, what does it mean
Speaker 2: about the universe if it works, et cetera, et cetera.
Speaker 2: But that's sort of to me, the core of thinking
Speaker 2: like a physicist is building a little mental model and
Speaker 2: then using that to answer your questions.
Speaker 1: Yeah, I think you're basically describing what any scientist does.
Speaker 1: You know, chemists, biologists, they all work off models. I
Speaker 1: mean probably the word model is the most used word
Speaker 1: in all of science. Yeah, you know, biologists make models
Speaker 1: about evolution, about gene interactions, about how molecules interact, or
Speaker 1: how a species propagated, and things like that. But I
Speaker 1: wonder if the difference with you is that you're making
Speaker 1: models about the physical world or about baseballs, for example,
Speaker 1: and not spiders.
Speaker 2: Spiders are just way too complicated. There's no way for
Speaker 2: me to build a model of a spider. I have
Speaker 2: no idea exactly, and I know how to make the
Speaker 2: approximation so that I can describe a baseball. I know
Speaker 2: what to ignore. Maybe that's just my physics intuition, but
Speaker 2: I don't know how to do that for a spider,
Speaker 2: and I want to push back a little bit. I
Speaker 2: do think there's a difference between the models built by
Speaker 2: physicists and those built biologists, for example. I mean, in biology,
Speaker 2: we know that every model we build is effective. It's
Speaker 2: not fundamental. It's describing some emerging phenomenon like butterflies or spiders.
Speaker 2: Something we know is not an inherent object in the universe,
Speaker 2: but made out of those bits. It comes together through
Speaker 2: a special arrangement. So biology isn't describing something inherent to
Speaker 2: the universe. It's just approximately describing how things work during
Speaker 2: special conditions where like spiders and butterflies happen to emerge
Speaker 2: because they don't always right. There's a long time in
Speaker 2: the universe without spiders and butterflies, and so those rules
Speaker 2: don't apply in those scenarios. But physics is trying to
Speaker 2: figure out the fundamental laws, those that always apply in
Speaker 2: all circumstances that are inherent to the universe. And that
Speaker 2: difference in goal, I think leads to a different way
Speaker 2: of thinking, you know, good or bad. It leads to
Speaker 2: a hubrist that we can describe anything with simple laws,
Speaker 2: and it leads to different approaches and in different scientific culture,
Speaker 2: so that physicists are kind of recognizable to others and
Speaker 2: also to each other.
Speaker 1: Well, you're married to a biologist, how does your way
Speaker 1: of thinking different from your spouses?
Speaker 2: Yeah, I think something that's different in between the way
Speaker 2: that I think about things and the way biologists like
Speaker 2: my wife think about things is we're definitely much more
Speaker 2: focused on questions of like uncertainty and making things quantitative
Speaker 2: in order to try to extract some knowledge. Sometimes the
Speaker 2: things we're dealing with are abstract or indirect. You know,
Speaker 2: we're talking about tiny particles or things we can't ever
Speaker 2: see or even struggle to visualize. And so to help
Speaker 2: us guide our thinking, we rely really heavily on the uncertainty.
Speaker 2: How well do we know this? What can we say
Speaker 2: about this? Because we don't have much intuition, we can't
Speaker 2: like always got check our answers and say, is that
Speaker 2: reasonable that the top quark lives for ten to the
Speaker 2: minus twenty three seconds? I mean, you can't see that anyway.
Speaker 2: Whereas you know, my wife she can look at stuff
Speaker 2: and oh is it growing? And did we get this right?
Speaker 2: Is this virus killing that bacteria? Is somebody's got health
Speaker 2: improving when they eat more chia seeds, this kind of stuff.
Speaker 1: But she works with models as well, right, Her.
Speaker 2: Grad students are really good looking. Yes, they're like models.
Speaker 1: Yeah, yeah, well in comparison to physicists, I'm sure.
Speaker 2: Ooh, you're right though, that models is a very abused word,
Speaker 2: Like I also work in the machine learning community, and
Speaker 2: their model means to make very very different than a
Speaker 2: model in physics, than a model in fashion, and so
Speaker 2: it's a very generic word unfortunately.
Speaker 1: But you think that maybe it's something to do with
Speaker 1: the way that you look at the world and you
Speaker 1: formulate models. But I guess I'm trying to say that
Speaker 1: I think that's what all scientists do, right, across different fields.
Speaker 2: Yeah, so maybe physicists have more in common with other
Speaker 2: scientists than I ever imagined. Happy to.
Speaker 1: Sounds like you need to talk to people outside your
Speaker 1: department a little more, baby, besides your spouse, horten to.
Speaker 1: You interact with the economists or chemists.
Speaker 2: Economists very rarely, only if I run into them at
Speaker 2: the park. Chemists and computer scientists and engineers much more common.
Speaker 2: We sometimes have problems in common, you know, working on
Speaker 2: electronics for a new technology we want to bury in
Speaker 2: the ice in Antarctica, we need to understand the engineering
Speaker 2: details of it, or thinking about how to apply machine
Speaker 2: learning techniques we've developed for neutron stars to the problem
Speaker 2: of like predicting organic synthesis, these kind of things. So, yeah,
Speaker 2: definitely interact with the more physical science and engineering people
Speaker 2: more often than like psychiatrists. But I also talk to
Speaker 2: philosophers quite a bit. I don't know if they qualify
Speaker 2: as scientists.
Speaker 1: Do they I think they're not usually They're not in
Speaker 1: the same department for a reason, isn't it.
Speaker 2: It's fascinating though, Actually people in the philosophy of physics
Speaker 2: department here, they all have their PhDs in physics rather
Speaker 2: than in philosophy.
Speaker 1: Well, so they're physicists who have alosophy degree in the
Speaker 1: philosophy of science physics.
Speaker 2: That a doctor of philosophy of physicists, but now they're
Speaker 2: professors in philosophy of physics.
Speaker 1: It sounds like what is it the snake finally ate
Speaker 1: its tale. It is interesting to think about how people
Speaker 1: who are paid to do physics in particular think and
Speaker 1: what kinds of what makes them a tick I guess,
Speaker 1: and how does that color how they see the world,
Speaker 1: and so to get more insight into that, Danielle, you
Speaker 1: interviewed a couple of physicists and one ex physicists.
Speaker 2: Yeah, that's right. I talked to one physicist who's made
Speaker 2: it her mission to explain to people how physicists think
Speaker 2: about uncertainty, and another whose job is to guide physicists
Speaker 2: into the real world to find positions outside of academic
Speaker 2: physics and research.
Speaker 1: Well, it sounds like these are sort of like physics
Speaker 1: translators or physics counselors.
Speaker 2: Yeah, exactly, trying to bridge the gap between physicists and
Speaker 2: actual human beings.
Speaker 1: All right, well, when we come back, we'll listen to
Speaker 1: Daniel talking to two physicists whose jobs it is to
Speaker 1: translate what physicists think and do to the rest of
Speaker 1: the universe. So we'll dig into that, but first let's
Speaker 1: take a quick break.
Speaker 8: Bar.
Speaker 1: We're asking the question how to think like a physicist
Speaker 1: and apparently that involves talking to more.
Speaker 2: Physicists group think like a physicist?
Speaker 1: All right, well, you got to interview you too interesting
Speaker 1: people here, Daniel. First one is doctor Jen Kyle. What
Speaker 1: does Jen Kyle do?
Speaker 2: Jen Kyle is a theoretical physicist, but she also runs
Speaker 2: the YouTube channel Think like a Physicist, where she tries
Speaker 2: to explain to you how do you use the techniques
Speaker 2: and tricks of physics to think about the world and
Speaker 2: also to decode science results so you can get an
Speaker 2: understanding for whether that newsflash you just read about black
Speaker 2: holes is real or not?
Speaker 1: And did she talk to non physicists to figure out
Speaker 1: if a physicists thinking of unique way.
Speaker 2: Through her YouTube channel? So yeah, via the comments section.
Speaker 1: Oh boy, and we all know how productive. Those can be.
Speaker 2: Great insights in the comment section as always.
Speaker 1: All right, well, here's Daniels interview with particle physicists and
Speaker 1: YouTuber Jen Kyle.
Speaker 2: So it's my pleasure to introduce the podcast doctor Jen Kyle. Jen,
Speaker 2: thanks very much for joining us today.
Speaker 9: Hi, great to be here.
Speaker 2: Great. Tell us a little bit about yourself. What's your
Speaker 2: background with your training? What are you up to now?
Speaker 9: Ah?
Speaker 5: Well, I'm a theoretical particle physicist. I've done mostly work
Speaker 5: beyond the standard model physics. I've looked at some things
Speaker 5: on dark matter and possible new theories of flavor in
Speaker 5: the Cork and Lepton sectors, and I basically dabbled in
Speaker 5: physics beyond what we know now.
Speaker 2: Great, so you are definitely a trained and practicing physicist.
Speaker 2: So tell me what does it mean to you to
Speaker 2: think like a physicist? Can you remember learning how to
Speaker 2: do that? Can you compare the way you think now
Speaker 2: to the way you thought before you went to grad school?
Speaker 2: What does it mean to think like a physicist?
Speaker 5: I would definitely say it was not something that one
Speaker 5: learns in one day. It's more of a practice that
Speaker 5: you learn over many years. And I would say that
Speaker 5: a large part of thinking like a physicist is knowing
Speaker 5: how to draw conclusions from the universe and observations that
Speaker 5: we make of it, but also always keeping in mind
Speaker 5: how uncertain those conclusions that we draw from our observations
Speaker 5: can possibly be.
Speaker 2: What do you mean uncertain, Like we have a hunch
Speaker 2: and we're not sure. H we don't have enough information,
Speaker 2: or we could be confused. What do you mean by.
Speaker 5: Uncertain Well, basically, we draw conclusions about the universe from
Speaker 5: making observations and making measurements. So let's say that we
Speaker 5: have some amazing new idea that someone has come up with,
Speaker 5: but it hasn't been tested. It will make predictions about
Speaker 5: the universe, and oftentimes these are predictions about the values
Speaker 5: of certain quantities that we can measure, like the lifetime
Speaker 5: of a particle or the rate of a certain process
Speaker 5: that happens at the large Hadron collider. And we want
Speaker 5: to test this new amazing hypothesis, so we go and
Speaker 5: measure those quantities.
Speaker 9: And when we measure those.
Speaker 5: Quantities, we use experimental apparatuses and techniques, but it's not
Speaker 5: possible to ever have a perfect experiment. Whenever you get
Speaker 5: a measured value of a quantity, it's always going to
Speaker 5: differ at least a little bit from the true value
Speaker 5: of the quantity that you're trying to measure. So if
Speaker 5: you try to measure the electron mass, you will get
Speaker 5: a measured value of the electron mass, but it's not
Speaker 5: going to be exactly the true value of the electron mass.
Speaker 2: So let's make a little bit more concrete instead of
Speaker 2: thinking about particle physics. Let's say somebody gives me a
Speaker 2: coin and I have a theory that this coin is
Speaker 2: not fair, that it's going to favor heads right sixty
Speaker 2: six percent or something, and then I can do an
Speaker 2: experiment to see, well, is it a fair coin by
Speaker 2: flipping it right five hundred times. So I think you're
Speaker 2: saying that there's uncertainty because even if I flip it
Speaker 2: a thousand times, I'm never going to know precisely what
Speaker 2: the real probability is because I'm not flipping an infinite
Speaker 2: number of times. There's always some randomness. Is that what
Speaker 2: you're saying exactly? Okay, So there's uncertainty in our measurements
Speaker 2: because we don't take infinitely long experiments and we don't
Speaker 2: have infinite amounts of data. What are some other ways
Speaker 2: that we can be wrong or uncertain about our conclusions.
Speaker 5: Well, there are lots of ways that error can sneak
Speaker 5: into measurements. For example, we make measurements using some kind
Speaker 5: of experimental measurement apparatus.
Speaker 9: So, for example, let's say if.
Speaker 5: We're trying to measure any quantity, we're using some kind
Speaker 5: of experimental apparatus to do it, and that apparatus is
Speaker 5: going to have a finite resolution of some kind. So
Speaker 5: for example, let's say you're trying to measure the size of.
Speaker 9: An object in a room. You use a ruler, and
Speaker 9: that ruler.
Speaker 5: Has a finite gradation on it. You can't see down
Speaker 5: to the micron size using a ruler, so there's automatically
Speaker 5: some level of uncertainty that's going to come in because
Speaker 5: of effects like that. You may also for very complicated measurements, like,
Speaker 5: for example, if you're trying to measure a cross section
Speaker 5: at the large hadron collider, you have very complicated measuring
Speaker 5: devices and you have to simulate various parts of the
Speaker 5: not only the physics that you're trying to understand, but
Speaker 5: the device, and those simulations will never match up exactly
Speaker 5: well with reality.
Speaker 2: So I think what you're saying is that sometimes to
Speaker 2: do these experiments, we have to use devices we don't
Speaker 2: even really understand exactly how they work. Like if I'm
Speaker 2: measuring an electron the lartadron collider, and I have some
Speaker 2: device to measure an electrons energy, it's complicated to measure
Speaker 2: an electrons energy, and I don't exactly know what happens
Speaker 2: when an electron slams into a block of copper and
Speaker 2: creates a huge shower of other particles. It's complicated physics,
Speaker 2: and I could be wrong about what's going on in
Speaker 2: my own experimental device that I built and designed. Right.
Speaker 5: Yes, in fact, we don't entirely understan stand our own
Speaker 5: measuring devices perfectly, so we have to model them and
Speaker 5: simulate them, and sometimes compare those simulations to data in
Speaker 5: order to current to improve those simulations and get a
Speaker 5: better measurement of whatever it is we're trying to measure.
Speaker 2: Right, So, like back to the coin example. You know
Speaker 2: it's easy to look at a coin and say, oh
Speaker 2: it's heads or oh it's tails, But say it was harder, right,
Speaker 2: Say I couldn't just look at the coin. I needed
Speaker 2: to have some little device that told me if it
Speaker 2: was heads or tails, and that device I didn't really
Speaker 2: know how it worked, and I wasn't always sure it
Speaker 2: was correct. That would lead some like uncertainty into my measurement, right,
Speaker 2: because it could be wrong, or I could think that
Speaker 2: it's correct, but it's it's incorrect in some other ways.
Speaker 5: Yes, and it might be using some pattern recognition software
Speaker 5: that doesn't handle like certain light levels very well or
Speaker 5: something like that.
Speaker 9: So, yeah, it could make a mistake every.
Speaker 5: Once in a while until you you've got heads, when
Speaker 5: you've got tails or vice versa.
Speaker 2: Yeah, And so in physics, we're very quantitative about this, right,
Speaker 2: We're very specific when we measure something. We say, oh,
Speaker 2: is a two percent chance we've been wrong, or a
Speaker 2: zero point zer zer zer one percent chance we're wrong.
Speaker 2: Why are we such sticklers about this in physics? Why
Speaker 2: are we such nerds about measuring precisely how wrong we
Speaker 2: might be in physics? What do you think?
Speaker 9: Well?
Speaker 5: I think that part of it is that physics was
Speaker 5: one of the first fields to do a lot of measurements.
Speaker 5: So if you're only doing ten measurements and you think
Speaker 5: you'll screw up like one out of a thousand, you're
Speaker 5: probably not too worried that you're that you're going to
Speaker 5: produce a wrong result, or produce a result that had
Speaker 5: a large statistical fluctuation where you didn't you didn't screw
Speaker 5: up anything, and you're you're apparatus performed exactly correctly. But
Speaker 5: nonetheless you've got very unlucky if you think that that
Speaker 5: probability is small and you're only making like ten measurements,
Speaker 5: you're not too worried that you're going to publish a
Speaker 5: result that's going to lead people down a wrong path.
Speaker 5: But in particle physics, we make thousands of measurements, most
Speaker 5: of which you never hear about in the news because
Speaker 5: unfortunately most of them agree.
Speaker 9: With the standard model.
Speaker 5: But because we make so many, there's going to be
Speaker 5: some just out of statistical fluctuations that happen to appear
Speaker 5: to disagree a lot with what we expect, and so
Speaker 5: it's very important to have a very strict criterion for
Speaker 5: deciding when something disagrees with what we expect so much
Speaker 5: that it must be interesting.
Speaker 2: Yeah, I think that's probably true. Do you think it's
Speaker 2: also because some of the things we're probing are sort
Speaker 2: of invisible, so that our measurements are always going to
Speaker 2: be indirect, you know, like if somebody discovers a new
Speaker 2: kind of turtle in biology, they're like, here's the turtle.
Speaker 2: Like I can show you, look, this is a turtle.
Speaker 2: Like nobody's confused about whether it's a turtle. But if
Speaker 2: we're saying, hey, I discovered the squigglyon, it's not like
Speaker 2: I can say, I've got a pile of squiglyons. Here
Speaker 2: they are. That's all play with them to show you data,
Speaker 2: and the data has statistics, and we have to make inference,
Speaker 2: and so it's always frustratingly indirect. And I wonder if
Speaker 2: that's one reason why we have to be such nerds
Speaker 2: about whether or not we've been confused, because there's so
Speaker 2: many different steps between the physical reality and the actual
Speaker 2: measurements we make.
Speaker 5: Yeah, it's also the case that in particle physics we're
Speaker 5: also dealing with looking for processes in colliders that can
Speaker 5: look a lot like other processes that we aren't actually
Speaker 5: interested in.
Speaker 9: So it's not so much like we.
Speaker 5: Go out into the world and we find a new
Speaker 5: turtle and we bring it back and show people and
Speaker 5: say this is a new turtle. It's more like we
Speaker 5: go out into the world, and we find a new
Speaker 5: turtle that looks very, very similar to a lot of
Speaker 5: other turtles, and we bring that turtle and another thirty
Speaker 5: turtles back, and we show the collection of turtles to
Speaker 5: our colleagues, and we have to convince them that that
Speaker 5: one turtle really is special.
Speaker 2: It's not just the same turtle all the way down. Yeah, exactly.
Speaker 2: And then we do experiments with those turtles, flipping them
Speaker 2: to see if they're fair coins in that. So this
Speaker 2: is the way that physicists think about things. We're really
Speaker 2: focused on what we've measured, how well we know it,
Speaker 2: quantifying that uncertainty different ways we can be wrong when
Speaker 2: we communicate our results to the public. This is a challenge,
Speaker 2: right to express to them here's what we think, but
Speaker 2: here's how much wrong we might be. What do you
Speaker 2: think are the usual stumbling blocks for people who haven't
Speaker 2: spent their lives learning to think like a physicist for
Speaker 2: understanding uncertainties and what we mean by uncertainties when we
Speaker 2: talk about them.
Speaker 5: Well, I think one problem is that most of the
Speaker 5: time in real life, when we're talking about needing to
Speaker 5: know the value of some quantity, we.
Speaker 2: Were not hold on, are you contrasting physics with real
Speaker 2: life is that what you just did here, are you
Speaker 2: saying physics is not real for me?
Speaker 9: The same thing?
Speaker 5: But in the ordinary life, where we go outside and
Speaker 5: you know, do things where we're not looking at a
Speaker 5: computer screen, we do get values for various quantities. Like
Speaker 5: if we're driving our car, we do look at our
Speaker 5: speedometer hopefully and see what speed we're getting. And generally
Speaker 5: the outside world isn't very it's not used to giving
Speaker 5: us uncertainties on the numbers that we get. So we
Speaker 5: look at that speedometer and it tells us we're going
Speaker 5: fifty seven miles an hour, but it doesn't put an
Speaker 5: error bar on it. And also when we're learning things
Speaker 5: about either physics or anything else.
Speaker 9: In our in our education, at least in.
Speaker 5: Our earlier education, usually the idea is, here are the
Speaker 5: principles that we work from.
Speaker 9: What can we figure out from it?
Speaker 5: But we don't actually stop and think, well, what are
Speaker 5: the experimental results that led to us having those principles,
Speaker 5: and what were the errors on those principles, what were
Speaker 5: the uncertainties on those principles? And you know, how well
Speaker 5: does that principle work with the situation I'm trying to
Speaker 5: trying to study at the moment. Am I actually using
Speaker 5: the right the right set of scientific principles for the
Speaker 5: situation at hand, or am I introducing some uncertainties that maybe.
Speaker 9: Maybe I need to think about.
Speaker 5: So I would say that the main stumbling block is
Speaker 5: that we just aren't exposed to it. It's it's hard
Speaker 5: to come by.
Speaker 2: Yeah, I see. So maybe when you get pulled over,
Speaker 2: you can tell the officer like, look, it said it
Speaker 2: was I was doing sixty. I don't know why your
Speaker 2: machine says I was doing eighty five. Maybe there's some
Speaker 2: mistakes somewhere, right, Sometimes we have a little bit of
Speaker 2: intuitive grasp of like maybe there's fuzz in the numbers.
Speaker 2: But you're right, we're rarely like measuring the uncertainties in
Speaker 2: quote unquote real life. So for people who are not
Speaker 2: trained like a physicist and don't nerd out about statistics
Speaker 2: all the time, well, it's a sort of intuitive or
Speaker 2: easy way to start to think about these uncertainties. What
Speaker 2: do you recommend? I know you have a wonderful YouTube
Speaker 2: channel where you teach people to think like a physicist
Speaker 2: and think about uncertainties. How should people get started thinking
Speaker 2: about uncertainties like a physicist.
Speaker 5: Well, if you want to think about it the way
Speaker 5: physicists do, I guess I would explain how physicists arrive
Speaker 5: at those uncertainties. So, like a physicist who is conducting
Speaker 5: some kind of an experiment, they are going to want
Speaker 5: to produce a result, and they're going to want to
Speaker 5: produce an error bar that goes with that result.
Speaker 9: That tells you and what the uncertainty on that result is.
Speaker 2: Let's stop there, firm and describe exactly what you mean.
Speaker 2: They're like the error bar. So if I say I've
Speaker 2: measured my speed to be seventy miles an hour with
Speaker 2: an air bar of five, what does that mean? What
Speaker 2: does the err bar mean? What am I saying when
Speaker 2: I say five?
Speaker 5: So the error bar, if you're at least thinking about
Speaker 5: it from a physicist point of view, is you've thought
Speaker 5: about what the possible sources of error that can come
Speaker 5: in the ways that you could be wrong, the ways
Speaker 5: that you could measure it incorrectly, and you've done some
Speaker 5: kind of analysis or thinking about it to add those
Speaker 5: sources together and figure out roughly typically how much you
Speaker 5: would be wrong by.
Speaker 2: So does that mean that if I measure my speed
Speaker 2: to be seventy plus or minus five, that the true
Speaker 2: speed is definitely within sixty five to seventy five, Like,
Speaker 2: does the error bar completely define the possible extent of
Speaker 2: the truth.
Speaker 5: Absolutely not. It's a typical value. It's a typical value
Speaker 5: for the difference between the true value of something and
Speaker 5: the value that we measure. And we don't know whether
Speaker 5: the value we measure is above the true value or
Speaker 5: below it. And we don't know if the difference between
Speaker 5: the true value and our measured value is larger than
Speaker 5: that error bar or smaller.
Speaker 9: Than that error bar.
Speaker 5: And an instance of a specific measurement, what that error
Speaker 5: bar means is that's a typical value for how the
Speaker 5: true value in the measured value would disagree.
Speaker 2: Right, And so if we quote seventy plus or minus five,
Speaker 2: or let's talk about you know, politics, Joe Biden's pulling
Speaker 2: numbers are forty four percent with a uncertainty of three percent. Right,
Speaker 2: that doesn't mean that his true value is between you know,
Speaker 2: forty four plus three and forty four minus three. It
Speaker 2: means that there's a sixty percent chance that it is.
Speaker 2: And then therefore there's a thirty two percent chance that
Speaker 2: it isn't right. So the airbar tells us, as you say,
Speaker 2: roughly the size of the expected difference between the truths
Speaker 2: and the measured value. But it doesn't bound it right.
Speaker 2: It doesn't tell us it's exactly within that. I see
Speaker 2: this sort of misunderstanding all the time in political journalism.
Speaker 2: You know, where they have two candidates and if they're
Speaker 2: separated by ten points and the uncertainty is four points,
Speaker 2: then they say, Okay, it's definitely a lead, but you know,
Speaker 2: it still could be the opposite, or two candidates who
Speaker 2: are near each other but within the statistical uncertainty, they
Speaker 2: call it a tie, even though if one of them
Speaker 2: has a larger value, we're pretty sure that you know,
Speaker 2: we're somewhat sure at least that they have more support.
Speaker 2: I think there's a lot of misunderstanding about what this
Speaker 2: error bar means. It seems so much more definitive right
Speaker 2: than the way that we meet it. It's really, as
Speaker 2: you say, just a typical value. It tells you roughly
Speaker 2: the scale of how far off you might be. So
Speaker 2: when people are out there reading a scientific result, right
Speaker 2: when they're not measuring their speedometer, when they're reading a
Speaker 2: paper about a new particle and they come across something,
Speaker 2: what should they be asking themselves? They should what should
Speaker 2: they be looking for in that article to help understand
Speaker 2: how uncertain are physicists about this new squeakly unparticle.
Speaker 5: Well, I mean, at the most basic level, if the
Speaker 5: result is measuring something and saying this value was large
Speaker 5: than what we were expecting from our prediction if the
Speaker 5: particle didn't exist, the first question is to ask, well,
Speaker 5: what was the difference between what was observed and what
Speaker 5: was expected if the particle didn't exist, And then how
Speaker 5: does that difference compare to the quoted uncertainty.
Speaker 9: So if that difference is a lot larger.
Speaker 5: Than the quoted uncertainty, then we would tend to think
Speaker 5: that something interesting is going on. You know, maybe it's
Speaker 5: particle discovery. Hopefully it's particle discovery, but it always could
Speaker 5: be that something has gone wrong with the experiment that
Speaker 5: we don't understand. On the other hand, if the difference
Speaker 5: between what's observed and what's expected from the no new
Speaker 5: particle hypothesis, if that difference is not much larger than
Speaker 5: the uncertainty, or maybe it's only a couple times the uncertainty,
Speaker 5: then it's probably a little bit too early to get excited.
Speaker 5: We need more data and we need more results and
Speaker 5: possibly more experiments to look at it before we say
Speaker 5: anything definitive.
Speaker 2: Right, then make a concrete and go back to our
Speaker 2: coin that we're tossing, or the turtle that we're flipping.
Speaker 2: Let's say I flip the coin two times and I
Speaker 2: get two heads, so it's one hundred percent heads, right,
Speaker 2: And then I go off and I write a paper saying, look,
Speaker 2: my coin is one hundred percent heads. It's totally unfair.
Speaker 2: And you're the reviewer. You might look and say, all right,
Speaker 2: you know, but the prediction for a fair coin is
Speaker 2: fifty percent, and the prediction for an unfair coin is
Speaker 2: you know, something above that. But the uncertainty on your
Speaker 2: measurement is huge because you only flipped it twice, right,
Speaker 2: So yes, you measured one hundred percent heads, but you
Speaker 2: could have also gotten fifty percent heads or seventy five
Speaker 2: percent heads or whatever. And so you're saying if I
Speaker 2: go back and then flip it a million times and
Speaker 2: I still get a million heads, that that's very different, right,
Speaker 2: And I think people can understand that that's much more compelling.
Speaker 2: If you get a million heads in a row, it's
Speaker 2: very unlikely to be a fair coin. And that's the difference,
Speaker 2: right that there's a smaller uncertainty on my measurement of
Speaker 2: one hundred percent heads if I flip it a million
Speaker 2: times and if I flip it two times times, And
Speaker 2: so the two different hypotheses of like a fair coin
Speaker 2: fifty percent heads and an unfair coin and one hundred
Speaker 2: percent heads. The difference there is now large compared to
Speaker 2: the uncertainty, whereas it was small when I only flipped
Speaker 2: it twice.
Speaker 5: Yeah, when you only flip it twice, I mean, even
Speaker 5: if the coin is fair, the probability is twenty five
Speaker 5: percent it's going to come up heads both times. So
Speaker 5: it's it's important to not jump the gun and think
Speaker 5: that you've discovered something amazing when you might.
Speaker 9: Just have a quarter exactly.
Speaker 5: On the other hand, if you flip the coin ten
Speaker 5: times and it comes up heads each time, well then
Speaker 5: you know you start to think maybe something's up. And
Speaker 5: if you do it twenty times, then you might start
Speaker 5: to really think that's something up. And certainly, if you
Speaker 5: flip it a million times, then you're pretty darn certain
Speaker 5: something's else.
Speaker 2: Exactly, but I think it's fascinating that even now, for example,
Speaker 2: we can't say one hundred percent definitively that the Higgs
Speaker 2: boson exists. Like we've taken so much data, we have
Speaker 2: so much evidence, and yet still it could all be
Speaker 2: a fluctuation, right, It could all just be We could
Speaker 2: be that situation where we flipped a fair coin a
Speaker 2: million times and gotten a million heads in a row.
Speaker 2: It can happen, and we could have been fooled by
Speaker 2: our data. We don't have like a pile of Higgs
Speaker 2: bosons we can point to and say these are them, folks.
Speaker 2: We just have, you know, basically the result of flipping
Speaker 2: a bunch of coins and seeing it come out weird
Speaker 2: compared to our prediction for no Higgs boson. So in
Speaker 2: principle we know we don't really know that any particle
Speaker 2: is out there, though I guess as we continue to
Speaker 2: make collisions and analyze data we get more and more certain.
Speaker 2: But it's sort of like approaching the speed of light, right,
Speaker 2: you can never actually get there.
Speaker 9: That's right.
Speaker 5: You can never be absolutely certain of any scientific result
Speaker 5: that you produce. But on the other hand, you can
Speaker 5: also not be absolutely certain that this chair sitting next
Speaker 5: to you actually exists, because of course your eyes could
Speaker 5: have malfunctioned, you could be dreaming.
Speaker 9: So one percent certainty is a dream. It's an illusion.
Speaker 9: It's not something we can ever achieve.
Speaker 2: Exactly right, I'm not one hundred percent shortain we're having
Speaker 2: this conversation. Yeah, exactly, Great, Well, so tell us more
Speaker 2: about your project Think like a Physicist, where people can
Speaker 2: go to learn more about it and learn more about
Speaker 2: thinking like a physicist.
Speaker 9: Yeah.
Speaker 5: So I have a YouTube channel. It's called Think like
Speaker 5: a Physicist. And the idea behind my channel is I
Speaker 5: wanted to take the statistical methods, especially also the other
Speaker 5: methods the physicists use, but especially the statistical methods the
Speaker 5: physicists use, and I wanted to explain them in a
Speaker 5: way that I hope non scientists can understand. And the
Speaker 5: idea is that I would like for people when they
Speaker 5: read about a scientific result and it has an error
Speaker 5: bar on it, that they would be able to have
Speaker 5: a better understanding of what that error bar means, and
Speaker 5: also that that way they can understand scientific results in context.
Speaker 5: For example, if you hear that one experiment does a
Speaker 5: measurement of a certain quantity and it agrees with the
Speaker 5: standard model. And then three years later you hear that
Speaker 5: another experiment measured the same quantity and they got a
Speaker 5: different result. You know, it might be because the second
Speaker 5: experiment had a smaller error bar than the first one did,
Speaker 5: and so you can understand results in context better.
Speaker 9: So basically, I go through a.
Speaker 5: Lot of the basic statistical techniques that physicists use, and
Speaker 5: I hope that I explained them in a way that
Speaker 5: people can understand. And so, yeah, I would very much
Speaker 5: like the public to know more about these topics so
Speaker 5: that they can understand what we do a bit better.
Speaker 2: Great, tell us one more time where people can find you.
Speaker 5: Yeah, my YouTube channel is called think like a Physicist.
Speaker 2: Great, well, thanks very much Jen for coming on podcast
Speaker 2: today and thinking like a Physicist with me. I appreciate it.
Speaker 9: Thank you so much. It's been great.
Speaker 1: All right, interesting interview. I like the she talked about
Speaker 1: uncertainties and how you know this concept, you know, spills
Speaker 1: into our everyday lives, especially when it comes to things
Speaker 1: like policies. But people don't seem to have a pretty
Speaker 1: good understanding of that. Maybe they should talk to statisticians,
Speaker 1: not physicists or politicians.
Speaker 2: How to think like a statistician exactly.
Speaker 1: Yeah, how to probably think like a statistician?
Speaker 2: How statisticians likely think?
Speaker 1: Yeah, likely think or think likely?
Speaker 2: The likelihood of me finding a good joke is low.
Speaker 1: Yeah, we'll make that the null hypothesis. All right, and
Speaker 1: an interesting perspective though, about how to think like a physicist. Now,
Speaker 1: let's talk to someone whose job it is to, I guess,
Speaker 1: reintroduce physicists out into the world, sort of like those
Speaker 1: wildlife experts who have to retrain animals to live in
Speaker 1: the wild. Is that is that kind of her job?
Speaker 2: Yeah, exactly, Or re educate prisoners who are emerging.
Speaker 1: Oh, oh my goodness, I guess I could eat me
Speaker 1: a sort of like a prison. There are walls, towers,
Speaker 1: you know, small rooms where people are sitting all day.
Speaker 2: The food is terrible.
Speaker 1: Do you have does your door have bars in it
Speaker 1: as well? And the average sentence is like sixty seven years? Right?
Speaker 2: Oh, I got a lifetime sentence over here.
Speaker 1: You did a capital discovery. All right, Well, we'll get
Speaker 1: to Daniel's interview with physicists Kathy Kopick about what physicists
Speaker 1: can do outside of physics. So let's dig into that.
Speaker 1: But first, let's take another quick break. All right, we're
Speaker 1: asking the question how to think like a physicist? That
Speaker 1: sounds like a great T shirt, Think like.
Speaker 2: A physicist, yeah, or a bumper sticker and in.
Speaker 1: The bags is snap like a physicist too? Well, Danny,
Speaker 1: you got to talk to another physicist who sort of
Speaker 1: does something else that's kind of interesting.
Speaker 2: Yeah. Kathy Kopeik is an old friend of mine. She
Speaker 2: and I did experimental particle physics together many years ago,
Speaker 2: but then she ventured out into the world instead of
Speaker 2: continuing in physics research, and for many years her job
Speaker 2: was to help people who have PhDs in physics find
Speaker 2: jobs outside of physics, mostly in data science and in
Speaker 2: machine learning industry, which has been gobbling up a lot
Speaker 2: of physics PhDs.
Speaker 1: Well, she did this for a company or is it
Speaker 1: consultant or what.
Speaker 2: Yeah, there was a company called Insight Data Science, which
Speaker 2: was like a boot campo take people from physics, give
Speaker 2: them a little bit of an introduction into the tools
Speaker 2: of business or industry, or at least help them translate
Speaker 2: their experience so they knew how to talk about it.
Speaker 2: I find that one of the biggest barriers between fields
Speaker 2: is just vocabulary. You know, everybody talks about the same
Speaker 2: thing and using different words, and so if you just
Speaker 2: learn to translate your work, your expertise into somebody else's language,
Speaker 2: you can help them understand how you might be useful
Speaker 2: to their company.
Speaker 1: Right, Right, You just have to say things like I
Speaker 1: worked on a model to understand the universe, and then
Speaker 1: all scientists will understand you.
Speaker 2: I'm gonna circle back and connect with stakeholders so that
Speaker 2: we can maximize shareholder profit. Right, that's my attempt to.
Speaker 1: Speak corporate world. That's how you think they talk in
Speaker 1: corporate America.
Speaker 2: I mean based on the sitcoms I watch, I mean
Speaker 2: research I've done.
Speaker 1: Then, yes, is that part of thinking like a physicist
Speaker 1: is doing your research on TV and YouTube?
Speaker 2: That's just part of living man.
Speaker 1: Now, you said Kathy used to do that. What does
Speaker 1: she do now?
Speaker 2: Yeah? Now Kathy has a bunch of jobs. She's teaching
Speaker 2: at Berkeley and at Stanford, and she has her own
Speaker 2: consulting company helping people find physicists to work in their teams.
Speaker 1: All right, Well, here is Daniel's interview doctor Kathy Kopeck
Speaker 1: on how to think like a physicist and how to
Speaker 1: get a job as as a physicist, or how to
Speaker 1: pretend you're not a physicist to get a job. Is
Speaker 1: that that?
Speaker 2: Yeah, yeah, to get a non physics job if you
Speaker 2: are a physicist, there you go. All right. So then
Speaker 2: it's my great pleasure to introduce to the podcast my
Speaker 2: friend and colleague, doctor Kathy Copik. Kathy, thanks very much
Speaker 2: for joining us today.
Speaker 8: Oh, thanks so much. I'm really excited.
Speaker 2: Tell us a little bit about who you are, what
Speaker 2: your background is. You have a special and unusual journey.
Speaker 8: Oh yeah, thanks sure. So I was a physicist and
Speaker 8: am a physicist. I don't know if we talked in
Speaker 8: the past or present tense, but I worked in experimental
Speaker 8: particle physicists for a long time, first actually in California
Speaker 8: and but Bar, then outside Chicago on the CDs experiment
Speaker 8: at Formulab. Then I was at CERN for a long time,
Speaker 8: as were you, working on the Atlas experiment with Columbia
Speaker 8: and then with Berkeley. So I just I was in
Speaker 8: physics for a long time, studying the smallest things, and
Speaker 8: then I worked in the last ten years a lot
Speaker 8: on helping teams outside of academia think about how they
Speaker 8: use data in lots of ways, and how they hire
Speaker 8: their teams. I worked for about seven years at the
Speaker 8: Insight Data Science Fellows Program, working with a lot of
Speaker 8: scientists making a transition from working in science to working
Speaker 8: in tech in business, and worked with literally thousands of
Speaker 8: people making career transitions to literally hundreds of companies. And
Speaker 8: now I work as a consultant field Work partners with
Speaker 8: a friend and we help teams do the same kind
Speaker 8: of things as consultants.
Speaker 2: So this may seem like an obvious question, but why
Speaker 2: are people making a transition. You're getting a PhD in
Speaker 2: particle physics, You're studying the secrets of the universe. Why
Speaker 2: are people then going to work for healthcare companies or whatever?
Speaker 8: Sure, yeah, I say two main reasons. One is genuine interest.
Speaker 8: You know, people are excited about and curious about lots
Speaker 8: of things. It's one of the things that drive them
Speaker 8: to be scientists in the first place. And I talk
Speaker 8: to lots of people who are interviewing with our program
Speaker 8: to make that transition, and people were like, you know,
Speaker 8: I've done this thing for a long time and I
Speaker 8: really like doing it, and now I'm interested in doing
Speaker 8: something else. And so I think there is definitely genuine
Speaker 8: interest and curiosity about what it's like. And then I
Speaker 8: think on the other side, you know, the job market
Speaker 8: for academics is very hard getting that next position, that
Speaker 8: next position. Both it's very challenging. There's fewer and fewer
Speaker 8: positions at every level, and so naturally people have to
Speaker 8: exit academia. And also there's often fewer choice, like less
Speaker 8: choice of each level, so you know where you're going
Speaker 8: to live, what you're going to work on, who you're
Speaker 8: going to work with. Getting those positions is pretty tough,
Speaker 8: and so not just in physics, but in all fields
Speaker 8: across academia. People transition out after their undergrad after their PhD,
Speaker 8: after post docs, and sometimes at the faculty level as well.
Speaker 2: So we're always telling our students, hey, come to a
Speaker 2: PhD in physics because you're going to learn important skills
Speaker 2: about thinking and you're going to train yourself to be
Speaker 2: a smart person. And those skills are broadly applicable. And
Speaker 2: I've never worked outside of academia, so I don't know
Speaker 2: if I've been lying to people. Tell me, have I
Speaker 2: been lying to people? What skills do physics PhDs learn
Speaker 2: that are actually useful outside of particle physics?
Speaker 8: Sure? Sure, I do not think you are lying to people.
Speaker 8: I do think those skills are genuinely useful, and you
Speaker 8: can tell when you see where people go on to
Speaker 8: work after they've been in physics a lot of times
Speaker 8: in physics, and also that's in other places. The skills
Speaker 8: that people learn. I think there's three main things. The
Speaker 8: first one is just trying to figure out how to
Speaker 8: break a problem into smaller problems and questions, thinking about like, Okay,
Speaker 8: there's this big question we have, like what's the smallest
Speaker 8: thing in the universe, the thing that both you and
Speaker 8: I worked on and so have the big question? But
Speaker 8: then okay, how do I break that down into things
Speaker 8: that can be measured or things that we can write
Speaker 8: a theoretic model for. So breaking big questions into small
Speaker 8: questions it's a really important skill if you want to
Speaker 8: ask questions about the universe, but also if you want
Speaker 8: to ask questions about a business, or you know, how
Speaker 8: how many beds in a hospital are likely to be
Speaker 8: available on a given day given the procedures and things
Speaker 8: that are coming up, and how uncertain is it that
Speaker 8: people will get discharged on a certain day. If you're
Speaker 8: trying to build a model of anything, not just in science,
Speaker 8: but also in the real world, breaking a big problem
Speaker 8: into small questions is a big, big skill.
Speaker 2: Let me drill into that a little bit. I understand
Speaker 2: it's important to know, like how to get started on
Speaker 2: a problem. You're working for a company and they give
Speaker 2: you this project. They're like, build us this widget that
Speaker 2: does that thing, and you need to know what to
Speaker 2: do on day one so that after day ninety you're there.
Speaker 2: Why is that something that physicists in particular are good at,
Speaker 2: Like how does study in the nature of the universe
Speaker 2: make you good at learning how to break down problems?
Speaker 8: Yeah, a lot of things that physicists are good at
Speaker 8: are think scientists in general are good at asking question
Speaker 8: breaking it into problems, But physics in particular, I think
Speaker 8: both people who are drawn to physics and physics education
Speaker 8: reinforce the same thing, which is not just being a
Speaker 8: little bit curious, but being like really curious. You know,
Speaker 8: they're not just stopping at some level that's like a
Speaker 8: service level or where there's maybe approximations or things you're
Speaker 8: like really continuing to either you personally because it's how
Speaker 8: you think about the world, or in your education, working
Speaker 8: with your teachers and mentors are like really really really
Speaker 8: drilling down to these questions, to the really basic pieces
Speaker 8: of it. And I think that is unique to physics.
Speaker 8: It's you know, the people who study physics have chosen
Speaker 8: to kind of like continue down that path of questions
Speaker 8: to where you know, there's things are not even alive anymore.
Speaker 8: You're studying one atom, or studying how galaxies form, or
Speaker 8: some like very complicated basic question about the universe. So
Speaker 8: and I think it's true everybody takes a question and
Speaker 8: breaks it into smaller questions in science, but in physics
Speaker 8: really really trying to get to the most basic things
Speaker 8: about how the world works.
Speaker 2: Right, all right, So I interrupted you. You were telling us
Speaker 2: the good things that physicists learned, and number one is
Speaker 2: breaking things into pieces, and number two.
Speaker 8: Was breaking things into pieces. Number two I think especially
Speaker 8: in experimental physics working with very large general purpose data
Speaker 8: sets and a lot of parts of science. You know,
Speaker 8: every experimental science people have data sets. Sometimes they're very large,
Speaker 8: but a lot of scientists create those data sets themselves
Speaker 8: in a smaller group, so they have you know, they're
Speaker 8: trying to study one thing about how a certain bacteria
Speaker 8: does something, or you know, you're in their own lab
Speaker 8: and they kind of know, oh, maybe the data from
Speaker 8: July is no good because the temperature was off or something.
Speaker 8: You know, they know the data often because they created it.
Speaker 8: In physics, especially in experimental particle physics, where we both worked,
Speaker 8: but also in astrophysics and lots of other areas of physics,
Speaker 8: people have these very collaborative general purpose data sets that
Speaker 8: are meant not just to answer one question, but you
Speaker 8: can ask so many questions from them. And they're messy.
Speaker 8: They're built, these detectors that are built, and we have problems,
Speaker 8: some parts not working. Maybe that's showing up in some
Speaker 8: initial variables, also in some calculated variables down the road.
Speaker 8: You have to make corrections. Working with that kind of
Speaker 8: general purpose data is a real skill because that real
Speaker 8: world data that you might study if you're working at
Speaker 8: a business or nonprofit or asking some questions about non
Speaker 8: academic data, very similar to So that's a skill I
Speaker 8: think people learn in physics. And then a third one
Speaker 8: I would say is this collaboration working in. Not all
Speaker 8: collaborations are as big as the ones that we worked on.
Speaker 8: Most are not, but but working in everybody who's working
Speaker 8: in physics and in science is really trying to figure
Speaker 8: out what's already been done. Who has domain knowledge that
Speaker 8: might help me figure out the piece of it that
Speaker 8: I'm working on. How do I share what I'm working
Speaker 8: on in a way that can make sense to build
Speaker 8: some collaboration. How do I share my results back? How
Speaker 8: do I write about and speak about what I learned
Speaker 8: in a way that's going to help advance the research
Speaker 8: on this question. So all of those I think are
Speaker 8: really important.
Speaker 2: So in understanding what it's like to think like a physicist,
Speaker 2: I think one thing that's helpful is understanding where physicists
Speaker 2: find their skills useful. So you told us the kind
Speaker 2: of skills we learn, But where do people who have
Speaker 2: been trained in particle physics end up making impacts in
Speaker 2: the world outside of particle physics. Where are these skills helpful?
Speaker 1: Yeah?
Speaker 8: I think really everywhere. And I'm not just like trying
Speaker 8: to make it seem just everywhere. But in all the
Speaker 8: kinds of tech companies that you can think of that
Speaker 8: are working today, people are doing interesting work. Also, small places,
Speaker 8: nonprofits I mentioned initially. I mentioned this, like people working
Speaker 8: at a hospital to try to figure out how to
Speaker 8: build a system that helps predict when patients are going
Speaker 8: to be coming in or not. People are working in pharmaceuticals,
Speaker 8: just really in every area I think people are working.
Speaker 8: I mean I yeah, there's there's so many ex article
Speaker 8: business to know, so many of us that people go
Speaker 8: in and people are driven and curious to work on
Speaker 8: so many things that Yeah, just lots of places.
Speaker 2: And you know, physics is very good broad training, but
Speaker 2: we're not learning everything when people go out into the
Speaker 2: world and try to work on actual practical problems with
Speaker 2: real deliverables and stuff. What are some sort of blind spots.
Speaker 2: What are some things that physicists don't learn that are
Speaker 2: useful in the rest of the world.
Speaker 8: Yeah, I think that all of those advantages, those superpowers
Speaker 8: that I talked about have some kind of reverse kryptonite,
Speaker 8: which is like being very curious and very detail oriented
Speaker 8: and driven to like get to the very bottom of
Speaker 8: the question is a good instinct in physics, it's important.
Speaker 8: But sometimes in the business world you don't have the
Speaker 8: time or resources to like get really to the very
Speaker 8: bottom of something, and you have to kind of step
Speaker 8: back and make an approximation or maybe we're only going
Speaker 8: to run this thing for a week and we're going
Speaker 8: to get as far as we're going to get, but
Speaker 8: at the end, what we're trying to do is like
Speaker 8: recommend the next song for someone, or recommend the next
Speaker 8: for someone to watch. And so actually it's okay if
Speaker 8: like we don't understand everything about this, and so sometimes
Speaker 8: taking that step back and being like, you know, this
Speaker 8: isn't a six month project or a six year project.
Speaker 8: This is like a six week project, and we're gonna
Speaker 8: build something and we're gonna ship it and it's going
Speaker 8: to be good enough for that need, you know. And
Speaker 8: there are areas where that's true. And then there are areas,
Speaker 8: you know, where like in health and healthcare, where you
Speaker 8: don't want to make errors. And so I think people
Speaker 8: kind of through their personality might choose areas where it's
Speaker 8: okay to you know, recommend the next song for someone
Speaker 8: they might not enjoy as much. Where it's not okay
Speaker 8: to recommend, you know, a medication to someone that's not
Speaker 8: the right fit for them, right if it's you know,
Speaker 8: and there's still usually in a healthcare setting, there would be
Speaker 8: a doctor that would be the prescriber. But if you
Speaker 8: have a tool that's very biased or making wrong predictions
Speaker 8: for something that's really important like healthcare. You know, there's
Speaker 8: less room for error.
Speaker 2: So you've helped a lot of people figure out how
Speaker 2: to go from particle physics to someplace in the real
Speaker 2: world where they can make a contribution. How do you
Speaker 2: do that? How do you like get to know somebody
Speaker 2: and figure out, like what are their strengths and weaknesses
Speaker 2: and how does it fit. I mean, you're basically like
Speaker 2: the yinta of particle physics and jobs. But tell us
Speaker 2: about your process.
Speaker 8: Sure, sure, everybody is very different. That's one thing that
Speaker 8: I enjoy about it. So you know, some people need
Speaker 8: to grow or change in one area, where other folks
Speaker 8: that's very different for them. I think the first thing
Speaker 8: that I try to ask is what motivates people, what
Speaker 8: they're excited by. You know, some people are very excited
Speaker 8: by the impact in the real world and the people
Speaker 8: that might use or be helped by the thing they're
Speaker 8: working on. Other folks are very excited about the technical
Speaker 8: tools themselves, like getting to use the most advanced tools
Speaker 8: and models and getting to work on something technically very exciting.
Speaker 8: Other people are have worked very deeply and you know,
Speaker 8: worked ten years on one thing and are actually looking
Speaker 8: to do something more broad like they're like, oh, I
Speaker 8: want to learn about a lot of things. Some people
Speaker 8: love to interact with a lot of people. Some people
Speaker 8: want to be a little bit more like I kind
Speaker 8: of want to be given the thing and do my
Speaker 8: own thing. And so I think there's very different work
Speaker 8: for people depending on what they like and what they're
Speaker 8: interested in. And so once you know a little bit
Speaker 8: more about that, like what are the constraints around the
Speaker 8: kind of jobs that people are looking for, then I
Speaker 8: think it's easy to recommend specific like okay, well, and
Speaker 8: based on geography too, like there's just different kinds of
Speaker 8: jobs in different places in North America in the world,
Speaker 8: and so okay, well, for you, it sounds like you're
Speaker 8: excited about this and you're living here and these are
Speaker 8: your experiences. Helping people describe what they've done and what
Speaker 8: they want to do next. People usually don't need to
Speaker 8: build new skills. They have a lot of skills. It's
Speaker 8: just they need to have some kind of exploration of
Speaker 8: the space of available things, what they want, what they have,
Speaker 8: how they can describe what they've done and maybe demonstrate
Speaker 8: it in a different way by talking about it differently.
Speaker 8: Those are the main things I think I would do.
Speaker 2: So, I've seen a lot of physicists end up like
Speaker 2: on Wall Street or in data science. These seem to
Speaker 2: be places like where that community has an appetite for it.
Speaker 2: They're like, oh, yeah, we like hiring physicists or whatever. Yeah,
Speaker 2: but tell us some other places where physicists might end
Speaker 2: up some you know, maybe unusual or bizarre places physics
Speaker 2: PhDs end up working in.
Speaker 8: Yeah, that's a good question. I do you think people
Speaker 8: end up in a lot of places that basically anywhere
Speaker 8: where people are like building some models to help a
Speaker 8: system run better. So it could be you know, things education,
Speaker 8: educational software. People are trying to build ways to help
Speaker 8: kids learn to read and learn to do math. There's
Speaker 8: all kinds of games that people work on. Anything that
Speaker 8: you buy or sell clothes or you know, any sort
Speaker 8: of products, any sort of recommendations for things that you're
Speaker 8: that people are working on. Anything in the healthcare industry.
Speaker 8: I talked about that a lot already. Anything in the
Speaker 8: kind of broad tech you see, there's a ton of
Speaker 8: work right now in AI, certainly large language models. A
Speaker 8: lot of people from physics are working on those tools
Speaker 8: at all the places you can imagine. There's really a
Speaker 8: lot of a lot of places. I can't think of
Speaker 8: one like fun especially funny, Like, oh, here's one thing
Speaker 8: you can think of. But in every area media, fashion,
Speaker 8: people are working in all sorts of areas.
Speaker 2: People working on like optimizing you know, underwear sizes and
Speaker 2: stuff like this.
Speaker 8: For sure, for sure, that's particle physics at work. That's right,
Speaker 8: it's funny and it's a joke. But it's also true
Speaker 8: that like I don't know, for me, finding clothes that
Speaker 8: fit is actually really nice.
Speaker 2: Yes, it's an important, unsolved fart. You can make a
Speaker 2: real impact in people's daily lives.
Speaker 8: I mean, it's like a little bit silly, But it's
Speaker 8: also true that there's a lot of I think there's
Speaker 8: a lot of systems where people have just done the
Speaker 8: same thing forever and having a fresh take on it
Speaker 8: can be helpful.
Speaker 2: Yeah, everybody's got like their favorite pair of jeans or
Speaker 2: their favorite pair of underwear, and there's a reason they
Speaker 2: fit right, it feels good. So there's this lore going
Speaker 2: around that I hear a lot that one of the
Speaker 2: reasons behind the two thousand and eight financial collapse was
Speaker 2: that Wall Street went a little bit crazy with its modeling,
Speaker 2: and that there were these crazy quants and that most
Speaker 2: of them were ex physicists who didn't really understand the
Speaker 2: system and just like wrote a bunch of code that
Speaker 2: went crazy and destroyed people's lives. So would they have
Speaker 2: to say that did just call the cause the financial
Speaker 2: collapse or not?
Speaker 8: Probably not alone. I'll say that the I do you
Speaker 8: think there's a superpower kryptonite that physicists are very interested in,
Speaker 8: you know, going down to the root causes the basic
Speaker 8: how do you take this problem break it into the
Speaker 8: basic parts? And I think that the cryptonite version of
Speaker 8: that is like thinking that you can do that in
Speaker 8: any field, for any topic without necessarily consulting and learning
Speaker 8: about the domaining knowledge of the practitioners or people that
Speaker 8: have worked in that area. There's a famous data science person,
Speaker 8: Drew Conway used to say, physicists where like kind of
Speaker 8: like wil to beasts that would like run into an
Speaker 8: area that seems interesting, like biophysics, right, It's like, oh,
Speaker 8: there's something interesting there. All the physics here comes a
Speaker 8: lot of old you know, ex physicists who are like,
Speaker 8: we'll solve all the problems. And so when I would
Speaker 8: give talks to physics. I would say, don't be a
Speaker 8: will to beast, like, don't run into their area to
Speaker 8: a new area. So these maybe these two thousand and
Speaker 8: eight physicists are kind of just like I know, I'll
Speaker 8: break down this problem into these parts and look what
Speaker 8: I'm doing, isn't it cool? But if there is a
Speaker 8: little bit more domain knowledge or thought around, how could
Speaker 8: this go wrong? How might this affect people who? Why
Speaker 8: might we not do this? They could have avoided some
Speaker 8: bad outcomes?
Speaker 2: All right, So maybe we're not totally guilty, just partially.
Speaker 8: Yeah.
Speaker 2: So a lot of our audience are folks who like
Speaker 2: physics and like thinking about physics and have been listening
Speaker 2: to the pod and learning to think like a physicist
Speaker 2: and applying you know, that mental model to questions about
Speaker 2: the universe. But what would be your advice for somebody
Speaker 2: out there who wants to take advantage of this way
Speaker 2: of thinking, somebody who's not necessarily trained as a physicist
Speaker 2: but wants to learn to think like a physicist. What
Speaker 2: would be your advice for learn need to think that way?
Speaker 8: Yeah? I think there's this. I'm sure you talk about
Speaker 8: the Drake's equation, which is used for thinking about where
Speaker 8: extraterrestrial life might be in the Milky Way?
Speaker 2: Right?
Speaker 8: Is that right? Probably know much more of that. So
Speaker 8: that's the thing where you kind of are taking these pieces.
Speaker 8: Anybody can look up the Drake equation or Drake's equation
Speaker 8: and taking these pieces and trying to put it together
Speaker 8: to get one answer. And I went to a business
Speaker 8: class where people were talking about using the same sort
Speaker 8: of thing to model businesses or other processes where it's
Speaker 8: just trying to think about anybody can think about what
Speaker 8: are the parts that come together to create some answer
Speaker 8: or some prediction. And so just take thinking about that.
Speaker 8: Breaking something up into things that you can measure individually
Speaker 8: or you can think about individually, can really help solve
Speaker 8: a problem, whether it's a science problem, business problem, any
Speaker 8: kind of problems.
Speaker 2: All right, So then last question, a bit of a
Speaker 2: personal one. What do you miss most about actively working
Speaker 2: in physics? Say about being a physicist, because I think
Speaker 2: you're always a physicist once you're trying, like once a Jedi,
Speaker 2: always in Jedi. But what do you miss most about
Speaker 2: like working on particle physics other than working with me?
Speaker 8: Obviously I was gonna say, I mean, you're joking, but
Speaker 8: I think I really really did. There's a very special, fun,
Speaker 8: exciting environment of being at the lab in these big
Speaker 8: experiments at both that you know, at Slack in California,
Speaker 8: Fermi Lab, Brooke Caven Cern that these labs just it's
Speaker 8: really literally people from all over the world and having
Speaker 8: lunch together and the big cafeteria. Cerns called our one
Speaker 8: restaurant one a very creative name. I don't know if
Speaker 8: it still is. It's not named after someone now, is
Speaker 8: it still our one? Yeah? So our one. So if
Speaker 8: you're there for lunch or for coffee or the end
Speaker 8: of the day, it's just really fun to run into
Speaker 8: so many people then you've worked with over your whole career,
Speaker 8: people who are getting into the field, people who are
Speaker 8: very senior. You never know who's going to be there.
Speaker 8: Just having some food, drinking coffee and getting to talk
Speaker 8: to people about what they're working on and also what
Speaker 8: they're doing and how they are. It's very very fun
Speaker 8: memories of hanging out there with all sorts of people,
Speaker 8: and yeah, no, it was a great time. So I
Speaker 8: would say just missing being with all the people that
Speaker 8: we used to work with and getting to meet new people.
Speaker 8: That's a really truly international environment too, really fun.
Speaker 2: It is fun to hear conversations in so many different languages.
Speaker 2: I like running into the same really old Nobel Prize
Speaker 2: winners over and over again, introducing myself every single time
Speaker 2: because they don't remember me because they're like one hundred
Speaker 2: and fifty years old. And I also remember one of
Speaker 2: the first times I was at our one and you
Speaker 2: had some special trick for making an iced coffee. When
Speaker 2: you showed it to meet and Katrina and for the
Speaker 2: rest of the summer we were like, oh, let's get
Speaker 2: a Kathy. We called it a kathy, you know, a coppuccino.
Speaker 8: That's yeah, yeah, I've made up created a TGI Fridays.
Speaker 8: I don't know if you're looking for sponsorships Stan TGI
Speaker 8: Fridays the restaurant when I was a server there, created
Speaker 8: the Copacina Cocino delicious.
Speaker 2: Thank you got us through that summer.
Speaker 8: Yeah, they don't have They didn't have cappuccino machines. That's
Speaker 8: sort They had espresso machines, but no, no cappuccino machines,
Speaker 8: so you got to figure it out all right.
Speaker 2: Well, thanks very much for sharing with us how to
Speaker 2: think like a physicist, and how to drink coffee like
Speaker 2: a physicist. Really appreciate it.
Speaker 8: We'll put the put the recipe people.
Speaker 2: In the show notes for Copucina show notes. All right,
Speaker 2: thanks Kathy.
Speaker 1: All Right, interesting talk there, Daniel. It seems like she's
Speaker 1: basically saying we all have skills.
Speaker 2: Everybody's skills are different at least. Yeah. I think she
Speaker 2: probably aligns with you to think that, like scientists are
Speaker 2: all curious thinkers and mental model builders, and not even
Speaker 2: all physicists are the same. We all think differently and
Speaker 2: enjoy different parts of the process. Hmm.
Speaker 1: And that can help you get a job, right, because
Speaker 1: these are all skills that we could all use in
Speaker 1: every field.
Speaker 2: Probably, yeah, exactly. And so in the end, thinking like
Speaker 2: a physicist is just like thinking like a scientist, being
Speaker 2: a curious person, trying to understand the world, being methodical
Speaker 2: about it, try not to fool yourself with what the
Speaker 2: data is telling you.
Speaker 1: Yeah, and just trying to maximize your functionality to the stakeholders.
Speaker 2: Exactly.
Speaker 1: Maximize shareholder revenue, maximize physicists employment.
Speaker 2: Try not to cause any more financial collapses. Please.
Speaker 1: All right, Well, an interesting discussion about thinking like a scientist,
Speaker 1: thinking like a physicist, what are the commonalities and how
Speaker 1: things might be a little bit unique. For people who
Speaker 1: pursue physics as a career.
Speaker 2: And for those of you out there not pursuing physics
Speaker 2: as a career, but who have discovered a love for physics,
Speaker 2: keep doing it, keep thinking like a physicist or a scientist,
Speaker 2: and keep being curious about the world and trying to
Speaker 2: make the whole thing click together in your mind.
Speaker 1: Yeah, but mostly just keep thinking, please.
Speaker 2: And keep listening to the pod.
Speaker 1: Thanks everybody, We hope you enjoyed that. Thanks for joining us.
Speaker 1: See you next time.
Speaker 2: For more science and curiosity, come find us on social
Speaker 2: media where we answer questions and post videos. We're on Twitter, Discorg, Insta,
Speaker 2: and now TikTok. Thanks for listening and remember that Daniel
Speaker 2: and Jorge Explain the Universe is a production of iHeartRadio.
Speaker 2: For more podcasts from iHeartRadio, visit the iHeartRadio app, Apple Podcasts,
Speaker 2: or wherever you listen to your favorite shows.