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Speaker 4: Andy Daniel, did you always want to be a paid physicist?
Speaker 1: Definitely not. When I was a kid, I did not
Speaker 1: want to be a physicist.
Speaker 4: Really, you knew what it was, but you knew you
Speaker 4: didn't want to be one.
Speaker 1: I don't think I understood what a scientist was well enough.
Speaker 1: But when I was a kid, I wanted to be
Speaker 1: an explorer. I wanted to get on a ship and
Speaker 1: find some new island and name it after myself.
Speaker 4: You just about to get out of Los Alamos, said
Speaker 4: the main purpose here.
Speaker 1: Yeah, though you can't really take a ship out of
Speaker 1: Los Alamos because it's landlocked. So there were some basic
Speaker 1: problems in my thinking.
Speaker 4: Well, you could take a train and then aship. But
Speaker 4: don't they say everyone's a physicists, especially little kids.
Speaker 1: Yeah. I think everybody is a scientist because they are
Speaker 1: curious about the world. And in the end I discovered
Speaker 1: that being a physicist it's kind of like being an explorer,
Speaker 1: except instead of discovering new continents, we're trying to discover
Speaker 1: new frontiers of knowledge.
Speaker 4: Instead of surfing the waves out there and the sea,
Speaker 4: you're surfing the couch. Mostly.
Speaker 1: I'm clickly clacking my way to new shores of knowledge.
Speaker 4: Just don't get scurvy on your couch.
Speaker 1: I got a bowl of limes here next to.
Speaker 4: Me, Okay, with the tequila and the margaritas. That's for
Speaker 4: after work, After work work these days? What's the difference.
Speaker 4: Hi'm Hori. I'm a cartoonist and the author of Oliver's
Speaker 4: Great Big Universe.
Speaker 1: Hi I'm Daniel. I'm a particle physicist and a professor
Speaker 1: at UC Irvine, and I want to teach people to
Speaker 1: think like a physicist.
Speaker 4: Wait, I'm confused. If everyone's a physicist, aren't just teaching
Speaker 4: people to think like humans?
Speaker 1: Yeah, basically, I'm done. I can retire. It's after work time.
Speaker 1: Where's margarita?
Speaker 4: I know, let's get the shots going.
Speaker 1: No, I think everybody does have curiosity, but you know,
Speaker 1: it took us a while to figure out some tips
Speaker 1: and some tricks to effectively extract knowledge from the universe
Speaker 1: rather than just like you know, making up cute stories
Speaker 1: to satisfy our curiosity.
Speaker 4: Right, it probably took a while to get paid to
Speaker 4: do it too.
Speaker 1: Yeah, that's certainly true. A lot of the big names
Speaker 1: in the history of science were men of leisure, you know,
Speaker 1: operating on their trust funds or daddy's bank account.
Speaker 4: Who do you think was the first professional physicist?
Speaker 1: You know, science as a profession is not actually that old.
Speaker 1: It's something like in the late eighteen hundreds that people
Speaker 1: started to call themselves scientists and get paid to do it.
Speaker 1: There are money to hire people to do this kind
Speaker 1: of research. Until then, it was you know, natural philosophers
Speaker 1: and people just sort of like curious poking around in
Speaker 1: their own laboratories. Yeah, but scientists as a job is
Speaker 1: not much more than like one hundred years old.
Speaker 4: WHOA. So even the word science is relatively new.
Speaker 1: Yeah, exactly. If you ask like Gauss or Newton or
Speaker 1: leading It or Aristotle, they certainly would not call themselves
Speaker 1: a scientist. That's a new word.
Speaker 4: Or maybe they did it on purpose. They're like science, No, thanks,
Speaker 4: it's a new fangled thing that all the kids are
Speaker 4: talking about. I prefer to be a natural philosopher. But anyways,
Speaker 4: welcome to our podcast. Daniel and Jorge explain the universe
Speaker 4: a production of iHeartRadio.
Speaker 1: In which we do our best to demonstrate what it's
Speaker 1: like to think like a physicist. We take a physicist
Speaker 1: approach to dismantling the whole universe, understanding all of its
Speaker 1: little bits, building mental mathematical models to try to explain it,
Speaker 1: asking questions of those models, and then wondering what does
Speaker 1: it all mean anyway?
Speaker 4: Yeah, because, as we talked about before, the universe belongs
Speaker 4: to everyone, and asking questions is everyone's job, but a
Speaker 4: few people get to do it as a career, get.
Speaker 1: To Yes, exactly. It's definitely a treat and a privilege.
Speaker 4: Well you get paid to do it, I guess, and
Speaker 4: to do that, there's a certain mindset you have to have,
Speaker 4: right in order to be part of the profession.
Speaker 1: Yeah, there definitely is a way of thinking that's sort
Speaker 1: of like a physicist way of thinking. And I see
Speaker 1: this because people who are trained as physicists and then
Speaker 1: go out into the world and work in other areas
Speaker 1: chemistry or engineering or computer science still take with them
Speaker 1: a certain mindset, a certain way of approaching problems, which
Speaker 1: can be really really helpful and useful or also sometimes
Speaker 1: frustrating for their colleagues.
Speaker 4: Yeah, no, I can totally relate. I think that also
Speaker 4: the same is for engineers. You know, anyone who studied
Speaker 4: engineering definitely thinks like an engineer is trying to think
Speaker 4: it a certain way and a certain mind. Said about
Speaker 4: tackling problems for sure.
Speaker 1: Yeah, absolutely take an engineering approach to cartooning. For example.
Speaker 4: Yeah, whenever I draw a bridge, I mean I really
Speaker 4: put some calculations behind it. Why to make sure it
Speaker 4: doesn't fall down?
Speaker 1: Yeah, I know all those cartoons could be injured. I
Speaker 1: mean think about their families.
Speaker 4: Yeah. I usually build in a safety factor of like
Speaker 4: two or three to every cartoon I draw, just in case.
Speaker 4: But yeah, but professional physicists do think about things in
Speaker 4: a very different way than the rest of us. And
Speaker 4: so that's the question we'll be exploring today. So to
Speaker 4: the end the podcast will be tagling I to think
Speaker 4: like a physicist.
Speaker 1: And I'm not sure if this should be like an
Speaker 1: instruction manual or like.
Speaker 4: A warning, Oh why what can happen?
Speaker 1: You know, like watch out for these signs that you're
Speaker 1: thinking like a physicist, or like, hey, would you like
Speaker 1: to think like a physicist? Here's steps one, two, five.
Speaker 4: Well, I guess if it was the former, which titled
Speaker 4: how to Not Think Like a Physicist, How to Avoid
Speaker 4: Thinking like a Physicist.
Speaker 1: We're going to get into the positives, I'm sure, but
Speaker 1: you know, there is this lore that sometimes physicists oversimplify things.
Speaker 1: They're like, come into a new field, They're like, oh,
Speaker 1: these can just approximate this with a sphere, maybe a
Speaker 1: line on it or whatever. There's this urban legend that
Speaker 1: physicists being too simplistic, or the cause of the two
Speaker 1: thousand and eight financial collapse, for example. So you know,
Speaker 1: there are potentially some dangers to applying physics thinking to
Speaker 1: the broader world.
Speaker 4: Daniel, I wonder if you're overestimating how much people think
Speaker 4: about physicists.
Speaker 1: Probably I definitely don't have a clear view of that.
Speaker 4: I mean, I think for an urban legend to exist,
Speaker 4: you sort of need urban people talking about it.
Speaker 1: Maybe that's just an urban legend within physics, maybe nobody else.
Speaker 4: Yeah, just have issues.
Speaker 1: We definitely do.
Speaker 4: But it's an interesting question to ask if you're thinking
Speaker 4: about following a career in physics or wondering what is
Speaker 4: the job up and tail and what kind of mindset
Speaker 4: do you have to have in order to do it?
Speaker 4: At a research university or to become one, or to
Speaker 4: get a degree in.
Speaker 1: It, or if you're just an armchair physicist, if you
Speaker 1: like thinking about the nature of the universe and making progress,
Speaker 1: and over the years, maybe while listening to this podcast,
Speaker 1: you've been putting together your own personal mental model of
Speaker 1: the universe, asking questions, trying to click it together, coming
Speaker 1: to a holistic understanding of how things work. In that case,
Speaker 1: you might have picked up a few of the tricks
Speaker 1: of thinking like a physicist.
Speaker 4: Well, as usually, we were wondering how many people out
Speaker 4: there had thought about this question, had maybe wondered what
Speaker 4: it's like to be a professional physicist and what kind
Speaker 4: of mental skills you need to be one.
Speaker 1: Thanks very much to everybody who answers these random questions.
Speaker 1: Love hearing your thoughts. Please don't be shy. If you
Speaker 1: want to join the group, just write to me to
Speaker 1: Questions at Danielandjorge dot com.
Speaker 4: So think about it for a second. What do you
Speaker 4: think it takes to think like a physicist. Here's what
Speaker 4: people have to say.
Speaker 5: A physicist must think of at least are and apply
Speaker 5: it to the infinitely large universe, and that's not easy
Speaker 5: to do. Hence the podcast for the rest of us.
Speaker 6: What if that's an expression. I haven't heard of it before,
Speaker 6: so it's value. I think it probably would refer to
Speaker 6: someone being very practical, someone following the scientific method, very dogmatic, accurate,
Speaker 6: but then some theoretical physicists that are a bit wacky
Speaker 6: in what they come up with, so possibly a little
Speaker 6: bit of that too.
Speaker 4: I think like a physicist is to be asking questions
Speaker 4: and be relentless in your quest for an answer.
Speaker 1: I'd say thinking like a physicist means being curious and
Speaker 1: searching for answers through trial and error and experiments.
Speaker 7: Means there's our podcast about physicists, and to do it
Speaker 7: with your cartoonist friend.
Speaker 8: Basically, to think like a physicist means if you discover something,
Speaker 8: you get to really terrible name that doesn't make sense.
Speaker 7: I think it means to contemplate matter and energy and
Speaker 7: their interactions with one another.
Speaker 9: Well, from the episodes that I have listened to so far,
Speaker 9: I would say that to think like a physicist means
Speaker 9: to be inquisitive, to try to make connections between different
Speaker 9: aspects facets of life, and wondering why and.
Speaker 1: Trying to.
Speaker 9: Better understand and explain the phenomena we see throughout our
Speaker 9: daily lives.
Speaker 4: All right, I like some of these answers. I guess
Speaker 4: we're done because one of them said, we just need
Speaker 4: to start on a podcast about physics.
Speaker 1: And then give everything you discover a terrible name. These
Speaker 1: are some juicy answers.
Speaker 4: I guess people have been listening to our podcast.
Speaker 1: I love these answers because there's so meta. They tell
Speaker 1: me basically what people have learned from listening to the
Speaker 1: podcast for all these years. It's fantastic.
Speaker 4: Well, hopefully people are thinking a little bit more like scientists,
Speaker 4: like rational thinkers because of this podcast, and also maybe
Speaker 4: learning a little bit more about the universe and how
Speaker 4: it all works down to the atomic level and the
Speaker 4: galactic level.
Speaker 1: Yeah, and not just absorbing facts and little bits of knowledge,
Speaker 1: pieces of information, but also training yourself into how to
Speaker 1: accumulate more information, how to fit those pieces of information together,
Speaker 1: how to think about them. Science is more than just
Speaker 1: what we've learned. It's how we're going to learn more.
Speaker 4: How are we driving the distinction here between physicis and
Speaker 4: just a regular scientist or do you mean how to
Speaker 4: think like a scientist?
Speaker 1: Yeah, it's a great question. I don't know the answer
Speaker 1: to that. I'm probably not even the right person to
Speaker 1: answer the question of how do physicists think? Because I'm
Speaker 1: stuck in that mindset. I can't really see outside of
Speaker 1: it to understand how other people think. But when I
Speaker 1: mean chemists or biologists or economists, I do notice that
Speaker 1: answer and ask questions in a different way. There's something
Speaker 1: I have more in common with other physicists than I
Speaker 1: have with other side scientists. So there's something to it.
Speaker 4: All right, Well, let's dig into it. What do you
Speaker 4: think is specific about how physicists think.
Speaker 1: I think some of it comes from the fundamental motivation
Speaker 1: and the assumptions that underlie physics. Like the goal is big.
Speaker 1: We want to understand the universe. We want to figure
Speaker 1: it out. And the assumptions are pretty basic. They're like, look,
Speaker 1: the universe is understand a bull, and we can describe
Speaker 1: it with mathematical laws. We can build a mental model.
Speaker 1: The model should follow those laws, and we can use
Speaker 1: it to like predict the future and to understand the
Speaker 1: nature of the universe. You know, inherent in that is
Speaker 1: that we are simplifying the universe. We're taking all these
Speaker 1: observations and the weaving them together into a story. That's
Speaker 1: what the mathematical model is. We're saying, here's how this works,
Speaker 1: here's what's really happening behind the scene. So there's sort
Speaker 1: of like an ambition there to say, like we can
Speaker 1: describe the basic elements of the universe, whereas, and again
Speaker 1: I'm not an expert in other fields, you know, they
Speaker 1: feel a little bit more zoomed out, so they're not
Speaker 1: always as ambitious about like the fundamental understanding. They're describing
Speaker 1: things as sort of a higher level, which again still
Speaker 1: requires mathematical modeling and great precision. It's not a question
Speaker 1: of like precision or rigor just a question of like
Speaker 1: the ambition the context of the questions you're asking.
Speaker 4: Well, well, are you saying that other scientists are not
Speaker 4: as ambitious?
Speaker 1: I think maybe philosophically, physics and at least fundamental physics
Speaker 1: and particle physics is asking more ambitious questions than other fields. Yeah,
Speaker 1: I think they have deeper and broader implications again, philosophically.
Speaker 4: Right, right, So you think your topic of research is
Speaker 4: more important than other scientists because you're a physicist. I'm
Speaker 4: just saying there might be a little bit of bias here.
Speaker 1: A Daniel, No, it's totally reasonable to dig into that.
Speaker 1: I wouldn't say more important. You know, somebody who's developing
Speaker 1: new techniques to develop green energy, for example, they're not
Speaker 1: answering deep and fundamental questions about the nature of reality,
Speaker 1: but they're improving people's lives and maybe saving the planet,
Speaker 1: So that's arguably much more important. I think in terms
Speaker 1: of the philosophical context of our lives, particle physics and
Speaker 1: fundamental physics is answering those questions. Whether that's important or
Speaker 1: not is totally subjective, you know, whether it has value.
Speaker 1: Every kind of science is answering different kinds of questions,
Speaker 1: giving different kinds of insight into how the universe works.
Speaker 1: For me, at least one of the appeals of fundamental
Speaker 1: physics are these philosophical implications of it.
Speaker 4: Right, Well, I think, you know, most scientists would agree
Speaker 4: that what they're doing is also trying to understand and
Speaker 4: explain the world. I wonder if maybe a lot of
Speaker 4: the difference is just in the topic and the kinds
Speaker 4: of things that you're looking at the scope of it,
Speaker 4: or the kinds of phenomena you're looking at.
Speaker 1: Yeah, I think that everybody is doing the thing they
Speaker 1: think is most interesting and most exciting, And that's very personal, right.
Speaker 1: The person who's like crouching in a rainforest watching spiders
Speaker 1: crawl up twigs for hours and hours a day is
Speaker 1: deeply fascinated by that and chose to do that instead
Speaker 1: of economics or psychiatry or whatever for a reason, And
Speaker 1: that's totally cool. You're right, and the choice of topic
Speaker 1: is very very personal. But I think the choice of
Speaker 1: topic also sometimes leads to a different way of thinking.
Speaker 1: Like I think, because we're trying to ask fundamental questions
Speaker 1: and deep questions about the universe, we feel like we
Speaker 1: can touch onto some sort of mathematical purity, that there
Speaker 1: is maybe mathematics that describes this that we can drill
Speaker 1: down into and reveal. You know, somebody who's studying like hurricanes.
Speaker 1: You know, we don't have any mathematics that describes hurricanes.
Speaker 1: So we can do some simulations, but we're sort of
Speaker 1: at a loss because of all the chaos and the details.
Speaker 1: But when you zoom down into the fundamental firmament of
Speaker 1: the universe, we hope maybe there is some mathematics there
Speaker 1: that can describe what's going on. And so that's I
Speaker 1: think why physicists tend to build these mental mathematical models
Speaker 1: sometimes too simplified, you know, hence the famous spherical cowjoke,
Speaker 1: which I don't know, maybe that's only a famous joke
Speaker 1: within physics, you tell me.
Speaker 4: I've never heard of that before. But uh, but you know,
Speaker 4: I think all scientists would say that what they're doing
Speaker 4: is fundamental as well. Like, if you're studying spiders, you're
Speaker 4: probably thinking about the different ways that life can form,
Speaker 4: or the different factors that go into creating life and
Speaker 4: the factors that shape live. That seems pretty fundamental as well.
Speaker 1: Yeah, and ambitious, And if anything, I think you probably
Speaker 1: have a lot more insight into this than I do,
Speaker 1: or than most people, because you interact with so many
Speaker 1: different kinds of scientists, and obviously you've been spending a
Speaker 1: lot of time learning about physics and decoding the brains
Speaker 1: of physicists but also other scientists, and so from your perspective,
Speaker 1: I'd be very curious to hear, like, do you think
Speaker 1: physicists think differently? Is the mind of physicists trained at
Speaker 1: different skills? Do they take a different approach or all
Speaker 1: scientists just one category?
Speaker 4: For you? You know, I think that if you're a scientist,
Speaker 4: you're probably trying to figure out how the world and
Speaker 4: the universe works. You're just asking questions about different phenomena
Speaker 4: in it. You know, if you're someone who studies hurricanes,
Speaker 4: you're trying to understand how certain physical processes work and
Speaker 4: how they can come together to create large effects. For example,
Speaker 4: that seems pretty fundamental as well or as fundamental as
Speaker 4: asking you know what an atom is made of?
Speaker 1: Yeah, can spiders come together to make hurricanes? Wouldn't that
Speaker 1: be awesome? And shouldn't we pitch that show to the.
Speaker 4: Discovery Channel Spider Nado's.
Speaker 1: Spider Cane.
Speaker 4: So I don't know, sorry, Signce Spider Natos sounds like
Speaker 4: a winner.
Speaker 1: Yeah, Well, I can't tell you whether it's fundamentally different
Speaker 1: from the way other scientists think, because I'm not other scientists.
Speaker 1: Maybe you can comment, but I can try to keep
Speaker 1: you a little bit of an insight into the way
Speaker 1: I approach a problem or the way I think about problems.
Speaker 1: And that's this reliance on building a model. You know,
Speaker 1: I look at a science problem like where is that
Speaker 1: ball gonna land after it comes off the bat? Try
Speaker 1: to predict that? And I think, well to get that
Speaker 1: exactly right is way too complicated, and there's so many
Speaker 1: factors involved. There's the wind speed, there's that bird flying by,
Speaker 1: there's tufts in the air, et cetera. And so I
Speaker 1: build a simpler model of the universe. I say, toss
Speaker 1: out the real universe. Can we come up with a
Speaker 1: simpler version of the universe and ask the question in
Speaker 1: that universe, but build a model in such a way
Speaker 1: that the answer in the simple universe is still relevant
Speaker 1: to reality. So can we extract the crucial details of
Speaker 1: the problem, put those into our model, and then use
Speaker 1: that to answer the question. So you know, you don't care,
Speaker 1: for example, about the color of the ball, you don't
Speaker 1: care whether some kid in the stand is eating ice cream.
Speaker 1: None of these details about glorious reality matter to answering
Speaker 1: this question. So you build a simpler model specific to
Speaker 1: that question because it's good at answering that question, not
Speaker 1: every other question, And you use that to answer the question.
Speaker 1: And you know you can argue philosophically like is that
Speaker 1: model real, what does it mean about the universe if
Speaker 1: it works, et cetera, et cetera. But that's sort of
Speaker 1: to me the core of thinking like a physicist is
Speaker 1: building a little mental model and then using that to
Speaker 1: answer your questions.
Speaker 4: Yeah, I think you're basically describing what any scientist does.
Speaker 4: You know, chemists, biologists, they all work off models. I
Speaker 4: mean probably the word model is the most used word
Speaker 4: in all of science. Yeah, you know, biologists make models
Speaker 4: about evolution, about gene interactions, about how molecules interact, or
Speaker 4: how a species propagate, and things like that. But I
Speaker 4: wonder if the difference with you is that you're making
Speaker 4: models about the physical world or about baseballs, for example,
Speaker 4: and not spiders.
Speaker 1: Spiders are just way too complicated. There's no way for
Speaker 1: me to build a model of a spider. I have
Speaker 1: no idea exactly, and I know how to make the
Speaker 1: approximation so that I can describe a baseball. I know
Speaker 1: what to ignore. Maybe that's just my physics intuition, but
Speaker 1: I don't know how to do that for a spider.
Speaker 1: And I want to push back a little bit. I
Speaker 1: do think there's a difference between the models built by
Speaker 1: physicists and those built biologists, for example. I mean, in biology,
Speaker 1: we know that every model we build is effective. It's
Speaker 1: not fundamental, it's describing some emergent phenomenon like butterflies or spiders.
Speaker 1: Something we know is not an inherent object in the universe,
Speaker 1: but made out of those bits it comes together through
Speaker 1: a special arrangement. So biology isn't describing something inherent to
Speaker 1: the universe. It's just approximately describing how things work during
Speaker 1: special conditions where like spiders and butterflies happen to emerge
Speaker 1: because they don't always right. There's a long time in
Speaker 1: the universe without spiders and butterflies, and so those rules
Speaker 1: don't apply in those scenarios. But physics is trying to
Speaker 1: figure out the fundamental laws, those that always apply in
Speaker 1: all circumstances that are inherent to the universe. And that
Speaker 1: difference in goal, I think leads to a different way
Speaker 1: of thinking, you know, good or bad. It leads to
Speaker 1: a hubris that we can describe anything with simple laws,
Speaker 1: and it leads to different approaches and in different scientific culture,
Speaker 1: so that physicists are kind of recognizable to others and
Speaker 1: also to each other.
Speaker 4: Well, you're married to a biologist, how does your way
Speaker 4: of thinking different from your spouses?
Speaker 1: Yeah, I think something that's different in between the way
Speaker 1: that I think about things in the way biologists like
Speaker 1: my wife think about things is we're definitely much more
Speaker 1: focused on questions of like uncertainty and making things quantitative
Speaker 1: in order to try to extract some knowledge. Sometimes the
Speaker 1: things we're dealing with are abstract or indirect. You know,
Speaker 1: we're talking about tiny particles or things we can't ever
Speaker 1: see or even struggle to visualize. And so to help
Speaker 1: us guide our thinking, we rely really heavily on the uncertainty.
Speaker 1: How well do we know this? What can we say
Speaker 1: about this? Because we don't have much intuition, We can't
Speaker 1: like always got check our answers and say, is that
Speaker 1: reasonable that the top quark lives for ten to the
Speaker 1: minus twenty three seconds? I mean, you can't see that anyway.
Speaker 1: Whereas you know, my wife, she can look at stuff
Speaker 1: and oh is it growing? And did we get this right?
Speaker 1: Is this virus killing that bacteria? Is somebody's got health
Speaker 1: improving when they eat more chia seeds, this kind of stuff.
Speaker 4: But she works with models as well, right, Her.
Speaker 1: Grad students are really good looking. Yes, they're like models.
Speaker 4: Yeah, yeah, well in comparison to physicists.
Speaker 1: And ooh, you're right though that models is a very
Speaker 1: abused word. Like I also work in the machine learning community,
Speaker 1: and their model means to make very, very different than
Speaker 1: a model in physics, than a model in fashion, and
Speaker 1: so it's a very generic word, unfortunately.
Speaker 4: But you think that maybe it's something to do with
Speaker 4: the way that you look at the world and you
Speaker 4: formulate models. But I guess I'm trying to say that
Speaker 4: I think that's what all scientists do, right across different fields.
Speaker 1: Yeah, so maybe physicists have more in common with other
Speaker 1: scientists than I ever imagined. Happy to.
Speaker 4: Sounds like you need to talk to people outside your
Speaker 4: department a little more, baby, besides your spouse. How horten
Speaker 4: to you interact with economists or chemists.
Speaker 1: Economists very rarely, only if I run into them at
Speaker 1: the park. Chemists and computer scientists and engineers much more common.
Speaker 1: We sometimes have problems in common, you know, working on
Speaker 1: electronics for a new technology you want to bury in
Speaker 1: the ice in Antarctica, we need to understand the engineering
Speaker 1: details of it, or thinking about how to apply machine
Speaker 1: learning techniques we've developed for neutron stars to the problem
Speaker 1: of like predicting organic synthesis, these kind of things. So, yeah,
Speaker 1: definitely interact with the more physical science and engineering people
Speaker 1: more often than like psychiatrists, but I also talk to
Speaker 1: philosophers quite a bit. I don't know if they qualify
Speaker 1: as scientists.
Speaker 4: Do they? I think they're not. Usually they're not in
Speaker 4: the same department for a reason, isn't it.
Speaker 1: It's fascinating though, Actually people in the philosophy of physics
Speaker 1: department here, they all have their PhDs in physics rather
Speaker 1: than in philosophy.
Speaker 4: Well so they're physicists who have a philosophy degree in
Speaker 4: the philosophy of science physics.
Speaker 1: A doctor of philosophy of physicists, but now they're professors
Speaker 1: in philosophy of physics.
Speaker 4: It sounds like what is it? The snake finally ate
Speaker 4: its tail. It is interesting to think about how people
Speaker 4: who are paid to do physics in particular think, and
Speaker 4: what kinds of what makes them a tick I guess,
Speaker 4: and how does that color how they see the world,
Speaker 4: and so to get more insight into that, Danielle, you
Speaker 4: interviewed a couple of physicists and one ex physicists.
Speaker 1: Yeah, that's right. I talked to one physics who's made
Speaker 1: it her mission to explain to people how physicists think
Speaker 1: about uncertainty, and another whose job is to guide physicists
Speaker 1: into the real world to find positions outside of academic
Speaker 1: physics and research.
Speaker 4: Well, it sounds like these are sort of like physics
Speaker 4: translators or physics counselors.
Speaker 1: Yeah, exactly, trying to bridge the gap between physicists and
Speaker 1: actual human beings.
Speaker 4: All right, Well, when we come back, we'll listen to
Speaker 4: Daniel talking to two physicists whose jobs it is to
Speaker 4: translate what physicists think and do to the rest of
Speaker 4: the universe. So we'll dig into that, But first let's
Speaker 4: take a quick break.
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Speaker 4: BARB, we're asking the question how to think like a physicist,
Speaker 4: and apparently that involves talking to more.
Speaker 1: Physicists group Think like a Physicist.
Speaker 4: All right, well, you got to interview to interesting people here, Daniels.
Speaker 4: First one is doctor Jen Kyle. What does Jen Kyle do?
Speaker 1: Jen Kyle is a theoretical physicist, but she also runs
Speaker 1: the YouTube channel Think Like a Physicist, where she tries
Speaker 1: to explain to you how to use the techniques and
Speaker 1: tricks of physics to think about the world and also
Speaker 1: to decode science results so you can get an understanding
Speaker 1: for whether that newsflash you just read about black holes
Speaker 1: is real or not?
Speaker 4: And did she talk to non physicist to figure out
Speaker 4: if a physicists thinking a unique.
Speaker 1: Way through her YouTube channel? So yeah, via the comments section.
Speaker 4: Oh boy, and we all know how productive those can be.
Speaker 1: Great insights in the comment section as always.
Speaker 4: All right, well, here's Daniel's interview with particle physicists and
Speaker 4: YouTuber Jen Kyle.
Speaker 1: So it's my pleasure to introduce the podcast doctor Jen Kyle. Jen,
Speaker 1: thanks very much for joining us today.
Speaker 7: Hi, great to be here.
Speaker 1: Great tell us a little bit about yourself. What's your
Speaker 1: background with your training, what are you up to now?
Speaker 7: Ah? Well, I'm a theoretical particle physicist. I've done mostly
Speaker 7: work on beyond the standard model physics. I've looked at
Speaker 7: some things on dark matter and possible new new theories
Speaker 7: of flavor in the cork and Lepton sectors, and I
Speaker 7: basically dabbled in physics beyond what we know now.
Speaker 1: Great, so you are definitely a trained and practicing physicist.
Speaker 1: So tell me what does it mean to you to
Speaker 1: think like a physicist? Can you remember learning how to
Speaker 1: do that? Can you compare the way you think now
Speaker 1: to the way you thought before you went to grad school?
Speaker 1: What does it mean to think like a physicist?
Speaker 7: I would definitely say it was not something that one
Speaker 7: learns in one day. It's more of a practice that
Speaker 7: you learn over many years. And I would say that
Speaker 7: a large part of thinking like a physicist is knowing
Speaker 7: how to draw conclusions from the universe and observations that
Speaker 7: we make of it, but also always keeping in mind
Speaker 7: how uncertain those conclusions that we draw from our observations
Speaker 7: can possibly be.
Speaker 1: What do you mean uncertain, Like we have a hunch
Speaker 1: and we're not sure, Oh, we don't have enough information,
Speaker 1: or we could be confused. What do you mean by.
Speaker 7: Uncertain well, basically, we draw conclusions about the universe from
Speaker 7: making observations and making measurements. So let's say that we
Speaker 7: have some amazing new idea that someone has come up with,
Speaker 7: but it hasn't been tested. It will make predictions about
Speaker 7: the universe, and oftentimes these are predictions about the values
Speaker 7: of certain quantities that we can measure, like the lifetime
Speaker 7: of a particle or the rate of a certain process
Speaker 7: that happens at the large Hadron collider. And we want
Speaker 7: to test this new amazing hypothesis, so we go and
Speaker 7: measure those quantities. And when we measure those quantities, we
Speaker 7: use experimental apparatuses and techniques, but it's not possible to
Speaker 7: ever have a perfect experiment. Whenever you get a measured
Speaker 7: value of a quantity, it's always going to differ at
Speaker 7: least a little bit from the true value of the
Speaker 7: quantity that you're trying to measure. So, if you try
Speaker 7: to measure the electron mass, you will get a measured
Speaker 7: value of the electron mass, but it's not going to
Speaker 7: be exactly the true value of the electron mass.
Speaker 1: So let's make a little bit more concrete instead of
Speaker 1: thinking about particle physics. Let's say somebody gives me a coin,
Speaker 1: and I have a theory that this coin is not fair,
Speaker 1: that it's going to favor heads right sixty six percent
Speaker 1: or something, and then I can do an experiment to see,
Speaker 1: well is it a fair coin by flipping it right
Speaker 1: five hundred times. So I think you're saying that there's
Speaker 1: uncertainty because even if I flip it a thousand times,
Speaker 1: I'm never going to know precisely what the real probability
Speaker 1: is because I'm not flipping an infinite number of times.
Speaker 1: There's always some randomness.
Speaker 7: Is that what you're saying exactly?
Speaker 1: Okay, So there's uncertainty in our measurements because we don't
Speaker 1: take infinitely long experiments and we don't have infinite amounts
Speaker 1: of data. What are some other ways that we can
Speaker 1: be wrong or uncertain about our conclusions?
Speaker 7: Well, there are lots of ways that error can sneak
Speaker 7: into measurements. For example, we make measurements using some kind
Speaker 7: of experimental measurement apparatus. So, for example, let's say if
Speaker 7: we're trying to measure any quantity, we're using some kind
Speaker 7: of experimental apparatus to do it, and that apparatus is
Speaker 7: going to have a finite resolution of some kind. So,
Speaker 7: for example, let's say you're trying to measure the size
Speaker 7: of an object in a room. You use a ruler,
Speaker 7: and that ruler has a finite gradation on it. You
Speaker 7: can't see down to the micron size using a ruler,
Speaker 7: so there's automatically some level of uncertainty that's going to
Speaker 7: come in because of effects like that. You may also
Speaker 7: for very complicated measurements, like, for example, if you're trying
Speaker 7: to measure a cross section the large Hadron collider, you
Speaker 7: have very complicated measuring devices and you have to simulate
Speaker 7: various parts of the not only the physics that you're
Speaker 7: trying to understand, but the device, and those simulations will
Speaker 7: never match up exactly well with reality.
Speaker 1: So I think what you're saying is that sometimes to
Speaker 1: do these experiments we have to use devices we don't
Speaker 1: even really understand exactly how they work. Like if I'm
Speaker 1: measuring an electron the lartadron collider, and I have some
Speaker 1: device to measure an electrons energy, it's complicated to measure
Speaker 1: an electrons energy, and I don't exactly know what happens
Speaker 1: when an electron slams into a block of copper and
Speaker 1: creates a huge shower of other particles. It's complicated physics,
Speaker 1: and I could be wrong about what's going on in
Speaker 1: my own experimental device that I built and designed, right.
Speaker 7: Yes, In fact, we don't entirely understand our own measuring
Speaker 7: devices perfectly, so we have to model them and simulate
Speaker 7: them and sometimes compare those simulations to data in order
Speaker 7: to current to improve those simulations and get a better
Speaker 7: measurement of whatever it is we're trying to measure.
Speaker 1: Right, So, like back to the coin example. You know,
Speaker 1: it's easy to look at a coin and say, oh
Speaker 1: it's heads or oh it's tails, But say it was harder, right, Say,
Speaker 1: I couldn't just look at the coin. I needed to
Speaker 1: have some little device that told me if it was
Speaker 1: heads or tails, and that device I didn't really know
Speaker 1: how it worked, and it wasn't always sure it was correct.
Speaker 1: That would lead some like uncertainty into my measurement, right,
Speaker 1: because it could be wrong, or I could think that
Speaker 1: it's correct, but it's it's incorrect in some other ways.
Speaker 7: Yes, And it might be using some pattern recognition software
Speaker 7: that doesn't handle like certain light levels very well or
Speaker 7: something like that. So yeah, it could make a mistake
Speaker 7: every once in a while, until you you've got heads,
Speaker 7: when you've got tails, or vice versa.
Speaker 1: Yeah, and so in physics, we're very quantitative about this, right,
Speaker 1: We're very specific when we measure something. We say, oh,
Speaker 1: there's a two percent chance we've been wrong, or a
Speaker 1: zero point zer or is or zero zero one percent
Speaker 1: chance we're wrong. Why are we such sticklers about this
Speaker 1: in physic Why are we such nerds about measuring precisely
Speaker 1: how wrong we might be in physics? What do you think?
Speaker 7: Well? I think that part of it is that physics
Speaker 7: was one of the first fields to do a lot
Speaker 7: of measurements. So if you're only doing ten measurements and
Speaker 7: you think you'll screw up like one out of a thousand,
Speaker 7: you're probably not too worried that you're that you're going
Speaker 7: to produce a wrong result, or produce a result that
Speaker 7: had a large statistical fluctuation where you didn't you didn't
Speaker 7: screw up anything, and you're you're apparatus performed exactly correctly.
Speaker 7: But nonetheless you've got very unlucky. If you think that
Speaker 7: that probability is small and you're only making like ten measurements,
Speaker 7: you're not too worried that you're going to publish a
Speaker 7: result that's going to lead people down a wrong path.
Speaker 7: But in particle physics, we make thousands of measurements, most
Speaker 7: of which you never hear about in the news because
Speaker 7: unfortunately most of them agree with the standard model. But
Speaker 7: because we make so many, there's going to be some
Speaker 7: just out of statistical fluctuations that happen to appear to
Speaker 7: disagree a lot with what we expect, And so it's
Speaker 7: very important to have a very strict criterion for deciding
Speaker 7: when something disagrees with what we expect so much that
Speaker 7: it must be interesting.
Speaker 1: Yeah, I think that's probably true. Do you think it's
Speaker 1: also because some of the things we're probing are sort
Speaker 1: of invisible, so that our measurements are always going to
Speaker 1: be indirect, you know, Like if somebody discovers a new
Speaker 1: kind of turtle in biology, they're like, here's the turtle,
Speaker 1: Like I can show you, look, this is a turtle,
Speaker 1: Like nobody's confused about whether it's a turtle. But if
Speaker 1: we're saying, hey, I discovered the squigglyon, it's not like
Speaker 1: I can say I've got a pile of squigglyons here
Speaker 1: they are let's all play with them. I have to
Speaker 1: show you data, and the data has statistics, and we
Speaker 1: have to make inference, and so it's always frustratingly indirect.
Speaker 1: And I wonder if that's one reason why we have
Speaker 1: to be such nerds about whether or not we've been confused,
Speaker 1: because there's so many different steps between the physical reality
Speaker 1: and the actual measurements we make.
Speaker 7: Yeah, it's also the case that in particle physics, we're
Speaker 7: also dealing with looking for processes in colliders that can
Speaker 7: look a lot like other processes that we aren't actually
Speaker 7: interested in. So it's not so much like we go
Speaker 7: out into the world and we find a new turtle
Speaker 7: and we bring it back and show people and say
Speaker 7: this is a new turtle. It's more like, we go
Speaker 7: out into the world and we find a new turtle
Speaker 7: that looks very, very similar to a lot of other turtles,
Speaker 7: and we bring that turtle and another thirty turtles back,
Speaker 7: and we show the collection of turtles to our colleagues,
Speaker 7: and we have to convince them that that one turtle
Speaker 7: really is special, it's.
Speaker 1: Not just the same turtle all the way down. Yeah, exactly.
Speaker 1: And then we do experiments with those turtles, flipping them
Speaker 1: to see if they're fair coins in that. So this
Speaker 1: is the way that does this think about things. We're
Speaker 1: really focused on what we've measured, how well we know it,
Speaker 1: quantifying that uncertainty different ways we can be wrong when
Speaker 1: we communicate our results to the public. This is a challenge,
Speaker 1: right to express to them here's what we think, but
Speaker 1: here's how much wrong we might be. What do you
Speaker 1: think are the usual stumbling blocks for people who haven't
Speaker 1: spent their lives learning to think like a physicist for
Speaker 1: understanding uncertainties and what we mean by uncertainties when we
Speaker 1: talk about them.
Speaker 7: Well, I think one problem is that most of the
Speaker 7: time in real life, when we're talking about needing to
Speaker 7: know the value of some quantity.
Speaker 1: We were not hold on, are you contrasting physics with
Speaker 1: real life? Is that what you just did here? Are
Speaker 1: you saying physics is not really for me?
Speaker 7: They're the same thing. But in the ordinary life, where
Speaker 7: we go outside and and you know, do things where
Speaker 7: we're not looking at a computer screen, we do get
Speaker 7: values for various quantities. Like if we're driving our car,
Speaker 7: we do look at our speedometer hopefully and see what
Speaker 7: speed we're getting. And generally the outside world isn't very
Speaker 7: it's not used to giving us uncertainties on the numbers
Speaker 7: that we get. So we look at that speedometer and
Speaker 7: it tells us we're going fifty seven miles an hour,
Speaker 7: but it doesn't put an error bar on it. And
Speaker 7: also when we're learning things about either physics or anything
Speaker 7: else in our education, at least in our earlier education,
Speaker 7: usually the idea is, here are the principles that we
Speaker 7: work from. What can we figure out from it? But
Speaker 7: we don't actually stop and think, well, what are the
Speaker 7: experimental results that led to us having those principles, and
Speaker 7: what were the errors on those principles? What were the
Speaker 7: uncertainties on those principles? And you know, how well does
Speaker 7: that principle work with the situation I'm trying to trying
Speaker 7: to study at the moment. Am I actually using the
Speaker 7: right set of scientific principles for the situation at hand?
Speaker 7: Or am I introducing some uncertainties that maybe maybe I
Speaker 7: need to think about. So I would say that the
Speaker 7: main stumbling block is that we just aren't exposed to it.
Speaker 7: It's it's hard to come by.
Speaker 1: Yeah, I see. So maybe when you get pulled over,
Speaker 1: you can tell the officer like, look, it said it
Speaker 1: was I was doing sixty. I don't know why your
Speaker 1: machine says I was doing eighty five. Maybe there's some
Speaker 1: mistakes somewhere, right, Sometimes we have a little bit of
Speaker 1: intuitive grasp of like, maybe there's fuzz in the numbers.
Speaker 1: But you're right, we're rarely like measuring the uncertainties in
Speaker 1: quote unquote real life. So for people who are not
Speaker 1: trained like a physicist and don't nerd out about statistics
Speaker 1: all the time, it's a sort of intuitive or easy
Speaker 1: way to start to think think about these uncertainties. What
Speaker 1: do you recommend I know you have a wonderful YouTube
Speaker 1: channel where you teach people to think like a physicist
Speaker 1: and think about uncertainties. How should people get started thinking
Speaker 1: about uncertainties like a physicist?
Speaker 7: Well, if you want to think about it the way
Speaker 7: physicists do, I guess I would explain how physicists arrive
Speaker 7: at those uncertainties. So, like a physicist who's conducting some
Speaker 7: kind of an experiment, they are going to want to
Speaker 7: produce a result, and they're going to want to produce
Speaker 7: an error bar that goes with that result, that tells
Speaker 7: you and what the uncertainty on that result is.
Speaker 1: Let's stop there firm and describe exactly what you mean.
Speaker 1: They're like the error bar. So if I say I've
Speaker 1: measured my speed to be seventy miles an hour with
Speaker 1: an air bar of five, what does that mean? What
Speaker 1: does the error bar mean? What am I saying when
Speaker 1: I say five?
Speaker 7: So the error bar, if you're at least thinking about
Speaker 7: it from a physicist point of view, is you've thought
Speaker 7: about what the possible sources of error that can come in,
Speaker 7: the ways that you could be wrong, the ways that
Speaker 7: you could measure it incorrectly, and you've done some kind
Speaker 7: of analysis or thinking about it to add those sources
Speaker 7: together and figure out, roughly typically how much you would
Speaker 7: be wrong by.
Speaker 1: So does that mean that if I measure my speed
Speaker 1: to be seventy plus or minus five, that the true
Speaker 1: speed is definitely within sixty five to seventy five?
Speaker 11: Like?
Speaker 1: Does the error bar completely define the possible extent of
Speaker 1: the truth?
Speaker 7: Absolutely not. It's a typical value. It's a typical value
Speaker 7: for the difference between the true value of something and
Speaker 7: the value that we measure. And we don't know whether
Speaker 7: the value we measure is above the true value or
Speaker 7: below it. And we don't know if the difference between
Speaker 7: the true value and our measured value is larger than
Speaker 7: that error bar or smaller than that error bar in
Speaker 7: an instance of a specific measurement. What that error bar
Speaker 7: means is that's a typical value for how the true
Speaker 7: value in the measured value would would disagree.
Speaker 1: Right, And so if we quote seventy plus or minus five,
Speaker 1: or let's talk about you know, politics, Joe Biden's polling
Speaker 1: numbers are forty four percent with a uncertainty of three percent. Right,
Speaker 1: that doesn't mean that his true value is between you know,
Speaker 1: forty four plus three and forty four minus three. It
Speaker 1: means that there's a sixty percent chance that it is,
Speaker 1: and then therefore there's a thirty two percent chance that
Speaker 1: it isn't. Right. So the airbar tells us, as you say,
Speaker 1: roughly the size of the expected difference between the truths
Speaker 1: and the measured value. But it doesn't bound it, right,
Speaker 1: It doesn't tell us it's exactly within that. I see
Speaker 1: this sort of misunderstanding all the time in political journalism.
Speaker 1: You know, where they have two candidates and if they're
Speaker 1: separated by ten points and the uncertainty is four points.
Speaker 1: Then they say, okay, it's definitely a lead, but you know,
Speaker 1: it still could be the opposite, or two candidates who
Speaker 1: are new or each other, but within the statistical uncertainty,
Speaker 1: they call it a tie, even though if one of
Speaker 1: them has a larger value, we're pretty sure that you know,
Speaker 1: we're somewhat sure at least that they have more support.
Speaker 1: I think there's a lot of misunderstanding about what this
Speaker 1: error bar means. It seems so much more definitive right
Speaker 1: than the way that we meet it. It's really, as
Speaker 1: you say, just a typical value. It tells you roughly
Speaker 1: the scale of how far off you might be. So
Speaker 1: when people are out there reading a scientific result, right
Speaker 1: when they're not measuring their speedometer, when they're reading a
Speaker 1: paper about a new particle and they come across something,
Speaker 1: what should they be asking themselves? What they should what
Speaker 1: should they be looking for in that article? To help
Speaker 1: understand how uncertain are physicists about this new, squeakly unparticle.
Speaker 7: Well, I mean, at the most basic level, if the
Speaker 7: result is measuring something and saying this value was larger
Speaker 7: than what we were expecting from our prediction, If the
Speaker 7: particle didn't exist. The first question is to ask, well,
Speaker 7: what was the difference between what was observed and what
Speaker 7: was expected if the particle didn't exist, And then how
Speaker 7: does that difference compare to the quoted uncertainty. So if
Speaker 7: that difference is a lot larger than the quoted uncertainty,
Speaker 7: then we would tend to think that something interesting is
Speaker 7: going on. You know, maybe it's particle discovery. Hopefully it's
Speaker 7: particle discovery, but it always could be that something has
Speaker 7: gone wrong with the experiment that we don't understand. On
Speaker 7: the other hand, if the difference between what's observed and
Speaker 7: what's expected from the no new particle hypothesis, if that
Speaker 7: difference is not much larger than the uncertainty, or maybe
Speaker 7: it's only a couple times the uncertainty, then it's probably
Speaker 7: a little bit too early to get excited. We need
Speaker 7: more data and we need more results and possibly more
Speaker 7: experiments to look at it before we say anything definitive.
Speaker 1: Right, So then let's make a concrete go back to
Speaker 1: our coin that we're tossing or the turtle that we're flipping.
Speaker 1: Let's say I flip the coin two times and I
Speaker 1: get too heads, so it's one hundred percent heads, right,
Speaker 1: And then I go off and I write a paper saying, look,
Speaker 1: my coin is one hundred percent heads. It's totally unfair.
Speaker 1: And you're the reviewer. You might look and say, all right,
Speaker 1: you know, but the prediction for a fair coin is
Speaker 1: fifty percent, and the prediction for an unfair coin is
Speaker 1: you know, something above that. But the uncertainty on your
Speaker 1: measurement is huge because you only flipped it twice, right, So, yes,
Speaker 1: you measured one hundred percent heads, but you could have
Speaker 1: also gotten fifty percent heads or seventy five percent heads
Speaker 1: or whatever. And so you're saying if I go back
Speaker 1: and then flip it a million times and I still
Speaker 1: get a million heads, that that's very different, right, And
Speaker 1: I think people can understand that that's much more compelling.
Speaker 1: If you get a million heads in a row, it's
Speaker 1: very unlikely to be a fair coin. And that's the difference, right,
Speaker 1: that there's a smaller uncertainty on my measurement of one
Speaker 1: hundred percent heads if I flip it a million times
Speaker 1: and if I flip it two times. And so the
Speaker 1: two different hypotheses of like a fair coin fifty percent
Speaker 1: heads and an unfair coin and one hundred percent heads.
Speaker 1: The difference there is now large compared to the uncertainty,
Speaker 1: whereas it was small when I only flipped it twice.
Speaker 7: Yeah, when you only flip it twice, I mean, even
Speaker 7: if the coin is fair, the probability is twenty five percent,
Speaker 7: it's going to come up heads both times. So it's
Speaker 7: important to not jump the gun and think that you've
Speaker 7: discovered something amazing when you might just have a quarter exactly.
Speaker 7: On the other hand, if you flip the coin ten
Speaker 7: times and it comes up heads each time, well then
Speaker 7: you know, you start to think maybe something's up. And
Speaker 7: if you do it twenty times, then you might start
Speaker 7: to really think that's something up. And certainly, if you
Speaker 7: flip it a million times then you're pretty darn certain
Speaker 7: something's use.
Speaker 1: Exactly. But I think it's fascinating that even now, for example,
Speaker 1: we can't say one hundred percent definitively that the Higgs
Speaker 1: boson exists, like we've taken so much data, we have
Speaker 1: so much evidence, and yet still it could all be
Speaker 1: a fluctuation, right, It could all just be We could
Speaker 1: be that situation where we flip the a fair coin
Speaker 1: a million times and gotten a million heads in a row.
Speaker 1: It can happen, and we could have been fooled by
Speaker 1: our data. We don't have like a pile of Higgs
Speaker 1: bosons we can point to and say these are them, folks.
Speaker 1: We just have, you know, basically the result of flipping
Speaker 1: a bunch of coins and seeing it come out weird
Speaker 1: compared to our prediction for no Higgs boson. So in principle,
Speaker 1: we know we don't really know that any particle is
Speaker 1: out there, though, I guess as we continue to make
Speaker 1: collisions and analyze data, we get more and more certain.
Speaker 1: But it's sort of like approaching the speed of light, right,
Speaker 1: you can never actually get there.
Speaker 7: That's right. You can never be absolutely certain of any
Speaker 7: scientific result that you produce. But on the other hand,
Speaker 7: you can also not be absolutely certain that this chair
Speaker 7: sitting next to you actually exists, because of course your
Speaker 7: your eyes could have malfunctioned, you could be dreaming. So
Speaker 7: certainty is is a dream. It's an illusion. It's not
Speaker 7: something we can ever achieve.
Speaker 1: Exactly right, I'm not one hundred certain we're having this conversation, Yeah, exactly, great, Well,
Speaker 1: so tell us more about your project. Think like a
Speaker 1: physicist where people can go to learn more about it
Speaker 1: and learn more about thinking like a physicist.
Speaker 7: Yeah, so I have a YouTube channel. It's called think
Speaker 7: like a Physicist, And the idea behind my channel is
Speaker 7: I wanted to take the statistical methods, especially also the
Speaker 7: other methods the physicists use, but especially the statistical methods
Speaker 7: the physicists use, and I wanted to explain them in
Speaker 7: a way that I hope non scientists can understand. And
Speaker 7: the idea is that I would like for people when
Speaker 7: they read about a scientific result and it has an
Speaker 7: error bar on it, that they would be able to
Speaker 7: have a better understanding of what that error bar means,
Speaker 7: and also that that way they can understand scientific results
Speaker 7: in context. For example, if you hear that one experiment
Speaker 7: does a measurement of a certain quantity and it agrees
Speaker 7: with the standard model, and then three years later you
Speaker 7: hear that another experiment measured the same quantity and they
Speaker 7: got a different result, you know it might be because
Speaker 7: the second experiment had a smaller error bar than the
Speaker 7: first one did, and so you can understand results in
Speaker 7: context better. So basically, I go through a lot of
Speaker 7: the basic statistical techniques that physicists use, and I hope
Speaker 7: that I explained them in a way that people can understand,
Speaker 7: and so yeah, I would very much like the public
Speaker 7: to know more about these topics so that they can
Speaker 7: understand what we do a bit better.
Speaker 1: Great, tell us one more time where people can find you.
Speaker 7: Yeah, my YouTube channel is called Think like a Physicist.
Speaker 1: Great. Well, thanks very much Jen for coming on podcast
Speaker 1: today and thinking like a Physicist with me. I appreciate it.
Speaker 7: Thank you so much. It's been great.
Speaker 4: All right, interesting interview. I like how you talked about
Speaker 4: uncertainties and how you know this concept, you know, spills
Speaker 4: into our everyday lives, especially when it comes to things
Speaker 4: like policies, But people don't seem to have a pretty
Speaker 4: good understanding of that. Maybe they should talk to statisticians,
Speaker 4: not physicists or politicians.
Speaker 1: How to think like a statistician exactly?
Speaker 4: Yeah, how to probably think like a statistician?
Speaker 1: How statisticians likely think?
Speaker 4: Yeah, likely think or think likely?
Speaker 1: The likelihood of me finding a good joke is low.
Speaker 4: Yeah, we'll make that the null hypothesis. All right, And
Speaker 4: an interesting perspective though about how to think like a physicist. Now,
Speaker 4: let's talk to someone whose job it is to I
Speaker 4: guess reintroduce physicists out into the world, sort of like
Speaker 4: those wildlife experts who have to retrain animals to live
Speaker 4: in the wild. Is that is that kind of her job?
Speaker 1: Yeah, exactly, Or re educate prisoners who are emotion.
Speaker 4: Oh my goodness, I guess I could eat me a
Speaker 4: sort of like a prison. There are walls, towers, you know,
Speaker 4: small rooms where people are sit all day.
Speaker 1: The food is terrible.
Speaker 4: Do you have does your door have bars in it
Speaker 4: as well? And the ever sentence is like six or
Speaker 4: seven years?
Speaker 1: Right, Oh, I got a lifetime sentence over here.
Speaker 4: You did a capital discovery. All right, Well, we'll get
Speaker 4: to Daniel's interview with physicist Kathy Kopik about what physicists
Speaker 4: can do outside of physics. So let's dig into that.
Speaker 4: But first, let's take another quick break.
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Speaker 4: All right, we're asking the question how to think like
Speaker 4: a physicist? That sounds like a great T shirt. Think
Speaker 4: like a physicist, yeah, or a bumper sticker and in
Speaker 4: the bag says snap like a physicist too. Well, Daniel,
Speaker 4: you got to talk to another physicist who sort of
Speaker 4: does something else that's kind of interesting.
Speaker 1: Yeah. Kathy Kopeik is an old friend of mine. She
Speaker 1: and I did experimental particle physics together many years ago,
Speaker 1: but then she ventured out into the world instead of
Speaker 1: continuing in physics research, and for many years her job
Speaker 1: was to help people who have PhDs in physics find
Speaker 1: jobs outside of physics, mostly in data science and in
Speaker 1: machine learning industry, which has been gobbling up a lot
Speaker 1: of physics PhDs.
Speaker 4: Well does she do this for a company or is
Speaker 4: a consultant or what?
Speaker 1: Yeah, there's a company called Insight Data Science, which was
Speaker 1: like a boot camp. It would take people from physics,
Speaker 1: give them a little bit of an introduction into the
Speaker 1: tools of business or industry, or at least help them
Speaker 1: translate their experience so they knew how to talk about it.
Speaker 1: I find that one of the biggest barrier between fields
Speaker 1: is just vocabulary. You know, everybody talks about the same
Speaker 1: thing using different words, and so if you just learn
Speaker 1: to translate your work, your expertise into somebody else's language,
Speaker 1: you can help them understand how you might be useful
Speaker 1: to their company.
Speaker 4: Right, Right, you just have to say things like I
Speaker 4: worked on a model to understand the universe, and then
Speaker 4: all scientists will understand you.
Speaker 1: I'm going to circle back and connect with stakeholders so
Speaker 1: that we can maximize shareholder profit. Right, that's my attempt to.
Speaker 4: Speak corporate world. That's how you think they talk in
Speaker 4: corporate America.
Speaker 1: I mean based on the sitcoms I watch. I mean
Speaker 1: research I've done. Then?
Speaker 4: Yes, Uh? Is that part of thinking like a physicist
Speaker 4: is doing your research on TV and YouTube?
Speaker 1: That's just part of living man.
Speaker 4: Now, you said Kathy used to do that. What does
Speaker 4: she do now?
Speaker 1: Yeah? Now Kathy has a bunch of jobs. She's teaching
Speaker 1: at Berkeley and at Stanford, and she has her own
Speaker 1: consulting company helping people find physicists to work in their teams.
Speaker 4: All right, well, here is Daniel's interview with doctor Kathy
Speaker 4: Kopeck on how to think like a physicists and how
Speaker 4: to get a job as a physicist, or how to
Speaker 4: pretend you're not a physicist to get a job. Is
Speaker 4: that is that?
Speaker 1: Yeah? Yeah, to get a non physics job if you
Speaker 1: are a physicist.
Speaker 4: There you go.
Speaker 1: All right. So then it's my great pleasure to introduce
Speaker 1: to the podcast my friend and colleague, doctor Kathy Copik. Kathy,
Speaker 1: thanks very much for joining us today.
Speaker 11: Oh thanks so much. I'm really excited.
Speaker 1: Tell us a little bit about who you are, what
Speaker 1: your background is. You have a special and unusual journey.
Speaker 11: Oh yeah, thanks sure. So I was a physicist and am
Speaker 11: a physicist. I don't know if we talk in the
Speaker 11: past or present tense, but I worked in experimental particle
Speaker 11: physicists for a long time, first actually in California and
Speaker 11: b Bar, then outside Chicago on the CDF experiment at Formulab.
Speaker 11: Then I was at CERN for a long time, as
Speaker 11: were you, working on the Atlas experiment. With Columbia and
Speaker 11: then with Berkeley. So I just I was in physics
Speaker 11: for a long time, studying the smallest things, and then
Speaker 11: I worked in the last ten years a lot on
Speaker 11: helping teams outside of academia think about how they use
Speaker 11: data in lots of ways, and how they hire their teams.
Speaker 11: I worked for about seven years at the Insight Data
Speaker 11: Science Fellows Program, working with a lot of scientists making
Speaker 11: a transition from working in science to working in tech
Speaker 11: in business, and worked with literally thousands of people making
Speaker 11: career transitions to literally hundreds of companies. And now I
Speaker 11: work as a consultant Fieldwork partners with a friend and
Speaker 11: we help teams do the same kind of things as consultants.
Speaker 1: So this may seem like an obvious question, but why
Speaker 1: are people making a transition? You're getting a PhD in
Speaker 1: particle physics, You're studying the secrets of the universe. Why
Speaker 1: are people then going to work for healthcare companies or whatever?
Speaker 11: Sure, yeah, I say two main reasons. One is genuine interest.
Speaker 11: You know, people are excited about and curious about lots
Speaker 11: of things. It's one of the things that drives them
Speaker 11: to be scientists in the first place. And I talked
Speaker 11: to lots of people who are interviewing with our programs
Speaker 11: to make that transition, and people were like, you know,
Speaker 11: I've done this thing for a long time and I
Speaker 11: really like doing it, and now I'm interested in doing
Speaker 11: something else, and so I think there is definitely genuine
Speaker 11: interest and curiosity about what it's like. And then I
Speaker 11: think on the other side, you know, the job market
Speaker 11: for academics is very hard getting that next position, that
Speaker 11: next position. Both it's very challenging. There's fewer and fewer
Speaker 11: positions at every level, and so naturally people have to
Speaker 11: exit academia, and also there's often fewer choice, like less
Speaker 11: choice at each level, so you know where you're going
Speaker 11: to live, what you're going to work on, who you're
Speaker 11: going to work with. Getting those positions is pretty tough,
Speaker 11: and so not just in physics, but in all fields
Speaker 11: across academia. People transition out after their undergrad after their PhD,
Speaker 11: after post docs, and sometimes at the faculty level as well.
Speaker 1: So we're always telling our students, hey, come to a
Speaker 1: PhD in physics because you're going to learn important skills
Speaker 1: about thinking and you're going to train yourself to be
Speaker 1: a smart person. And those skills are broadly applicable. And
Speaker 1: I've never worked outside of academia, so I don't know
Speaker 1: if I've been lying to people. Tell me, have I
Speaker 1: been lying to people? What skills do physics PhDs learn
Speaker 1: that are actually useful outside of particle physics?
Speaker 11: Sure? Sure, I do not think you are lying to people.
Speaker 11: I do think those skills are genuinely useful, and you
Speaker 11: can tell when you see where people go on to
Speaker 11: work after they've been in physics a lot of times
Speaker 11: in physics, and also that's from other places. The skills
Speaker 11: that people learn, I think there's three main things. The
Speaker 11: first one is just trying to figure out how to
Speaker 11: break a problem into smaller problems and questions, thinking about like, Okay,
Speaker 11: there's this big question we have, like what's the smallest
Speaker 11: thing in the universe, the thing that both you and
Speaker 11: I worked on and so have the big question? But
Speaker 11: then okay, how do I break that down into things
Speaker 11: that can be measured or things that we can write
Speaker 11: a theoretical model for. So breaking big questions into small
Speaker 11: questions it's a really important skill if you want to
Speaker 11: ask questions about the universe, but also if you want
Speaker 11: to ask questions about a business, or you know, how
Speaker 11: how many beds in a hospital are likely to be
Speaker 11: available on a given day given the procedures and things
Speaker 11: that are coming up, and how uncertain is it that
Speaker 11: people will get discharged on a certain day if you're
Speaker 11: trying to build a model of anything, not just in
Speaker 11: science but also in the real world, breaking big problem
Speaker 11: into small questions is a big, big skill.
Speaker 1: Let me drill into that a little bit. I understand
Speaker 1: it's important to know, like how to get started on
Speaker 1: a problem. You're working for a company and they give
Speaker 1: you this project. They're like, build us this widget that
Speaker 1: does that thing, and you need to know what to
Speaker 1: do on day one so that after day ninety you're there.
Speaker 1: Why is that something that physicists in particular are good at, Like,
Speaker 1: how does studying the nature of the universe make you
Speaker 1: good at learning how to break down problems?
Speaker 11: Yeah, a lot of the things that physicists are good
Speaker 11: at are think scientists in general are good at. I'm
Speaker 11: asking a question breaking it into problems, But physics in particular,
Speaker 11: I think both people who are drawn to physics and
Speaker 11: physics education reinforce the same thing, which is not just
Speaker 11: being a little bit curious, but being like really curious.
Speaker 11: You know, they're not just stopping at some level that's
Speaker 11: like a service level or where there's maybe approximations or things.
Speaker 11: You're like really continuing to either you personally because that's
Speaker 11: how you you think about the world, or in your
Speaker 11: education working with your teachers and mentors, are like really
Speaker 11: really really drilling down to these questions, to the really
Speaker 11: basic pieces of it. And I think that is unique
Speaker 11: to physics. It's you know, the people who study physics
Speaker 11: have chosen to kind of like continue down that path
Speaker 11: of questions to where you know, there's things are not
Speaker 11: even alive anymore. You're studying one atom, or studying how
Speaker 11: galaxies form, or some like very complicated basic question about
Speaker 11: the universe. So I think it's true everybody takes a
Speaker 11: question and breaks it into smaller questions in science, but
Speaker 11: in physics really really trying to get to the most
Speaker 11: basic things about how the world works.
Speaker 1: Right, all right? So I interrupted you. You were telling us
Speaker 1: the good things that physicists learned, and number one is
Speaker 1: breaking things into pieces, and number two was.
Speaker 11: Breaking things into pieces. Number two, I think, especially in
Speaker 11: experimental physics, working with very large general purpose data sets
Speaker 11: and a lot of parts of science. You know, every
Speaker 11: experimental science people have data sets. Sometimes they're very large,
Speaker 11: but a lot of scientists create those data sets themselves
Speaker 11: in a smaller group, so they have you know, they're
Speaker 11: trying to study one thing about how a certain bacteria
Speaker 11: does something, or you know, in their own lab, and
Speaker 11: they kind of know, oh, maybe the data from July
Speaker 11: is no good because the temperature was off or something.
Speaker 11: You know, they know the data often better because they
Speaker 11: created it. In physics, especially in experimental particle physics where
Speaker 11: we both worked, but also in astrophysics and lots of
Speaker 11: other areas of physics, people have these very collaborative general
Speaker 11: purpose data sets that are meant not just to answer
Speaker 11: one question, but you can ask so many questions from them.
Speaker 11: And they're messy. They're built, these detectors that are built,
Speaker 11: and we have problems, some parts not working. Maybe that's
Speaker 11: showing up in some initial variables, also in some calculated
Speaker 11: variables on the road. You have to make corrections. Working
Speaker 11: with that kind of general purpose data is a real
Speaker 11: skill because that real world data that you might study
Speaker 11: if you're working at a business or nonprofit or asking
Speaker 11: some questions about non academic data very similar to So
Speaker 11: that's a skill I think people learn in physics. And
Speaker 11: then a third one I would say, is this collaboration
Speaker 11: working in. You know, not all collaborations are as big
Speaker 11: as the ones that we worked on most or not,
Speaker 11: but working in everybody who's working in physics and in
Speaker 11: science is really trying to figure out what's already been done.
Speaker 11: Who has domain knowledge that might help me figure out
Speaker 11: the piece of it that I'm working on. How do
Speaker 11: I share what I'm working on in a way that
Speaker 11: can make sense to build some collaboration. How do I
Speaker 11: share my results back? How do I write about and
Speaker 11: speak about what I learned in a way that's going
Speaker 11: to help advance the research on this question? So all
Speaker 11: of those I think are really.
Speaker 1: Important, standing like what it's like to think like a physicist.
Speaker 1: I think one thing that's helpful is understanding where physicists
Speaker 1: find their skills useful. So you told us the kind
Speaker 1: of skills we learn. But where do people who have
Speaker 1: been trained in particle physics end up making impacts in
Speaker 1: the world outside of particle physics? Where are these skills helpful?
Speaker 11: Yeah? I think really everywhere, And I'm not just like
Speaker 11: trying to make it seem just everywhere, but in all
Speaker 11: the kinds of tech companies that you can think of
Speaker 11: that are working today, like people are doing interesting work.
Speaker 11: Also small places, nonprofits I mentioned initially. I mentioned this,
Speaker 11: like people working at a hospital to try to figure
Speaker 11: out how to build a system that helps predict when
Speaker 11: patients are going to be coming in or not. People
Speaker 11: are working in pharmaceuticals, just really in every area I
Speaker 11: think people are working. I mean, yeah, there's there's so
Speaker 11: many experiment particle physicists to so many of us that
Speaker 11: people go in and people are driven and curious to
Speaker 11: work on so many things that yeah, just lots of places.
Speaker 1: And you know, physics is very good broad training, but
Speaker 1: we're not learning everything when people go out into the
Speaker 1: world and try to work on actual practical problems with
Speaker 1: real deliverables and stuff. What are some sort of blind
Speaker 1: spots which some things that physicists don't learn that are
Speaker 1: useful in the rest of the world.
Speaker 11: Yeah, I think that all of those advantages, those superpowers
Speaker 11: that I talked about have some kind of reverse kryptonite,
Speaker 11: which is like being very curious and very detail oriented
Speaker 11: and driven to like get to the very bottom of
Speaker 11: the question is a good instinct. In physics, it's important.
Speaker 11: But sometimes in the business world, you don't have the
Speaker 11: time or resources to like get really to the very
Speaker 11: bottom of something, and you have to kind of step
Speaker 11: back and make an approximation. Or maybe we're only going
Speaker 11: to run this thing for a week and we're going
Speaker 11: to get as far as we're going to get. But
Speaker 11: at the end, what we're trying to do is like
Speaker 11: recommend the next song for someone, or recommend the next
Speaker 11: for someone to watch. And so it actually it's okay
Speaker 11: if like we don't understand everything about this, and so
Speaker 11: sometimes taking that step back and being like, you know,
Speaker 11: this isn't a six month project or a six year project.
Speaker 11: This is like a six week project, and we're going
Speaker 11: to build something and we're gonna ship it and it's
Speaker 11: going to be good enough for that need, you know.
Speaker 11: And there are areas where that's true, and then there
Speaker 11: are areas, you know, where like in health and healthcare,
Speaker 11: where you don't want to make errors. And so I
Speaker 11: think people kind of through their personality might choose areas
Speaker 11: where it's okay to you know, recommend the next song
Speaker 11: for someone. They might not enjoy as much. Where it's
Speaker 11: not okay to recommend, you know, a medication to someone
Speaker 11: that's not the right fit for them, right if it's
Speaker 11: you know, and there's still would usually in a in a
Speaker 11: healthcare setting there would be a doctor that would be
Speaker 11: the prescriber. But if you have a tool that's very
Speaker 11: biased or making wrong predictions for something that's really important
Speaker 11: like healthcare, you know, there's less room for error.
Speaker 1: So you've helped a lot of people figure out how
Speaker 1: to go from particles to someplace in the real world
Speaker 1: where they can make a contribution. How do you do that?
Speaker 1: How do you like get to know somebody and figure out,
Speaker 1: like what are their strengths and weaknesses and how does
Speaker 1: it fit. I mean, you're basically like the Yinta of
Speaker 1: particle physics and jobs. But tell us about your process.
Speaker 11: Sure, sure, everybody is very different. That's one thing that
Speaker 11: I enjoy about it. So, you know, some people need
Speaker 11: to grow or change in one area where other folks
Speaker 11: it's very different for them. I think the first thing
Speaker 11: that I try to ask is what motivates people, what
Speaker 11: they're excited by. You know, some people are very excited
Speaker 11: by the impact in the real world and the people
Speaker 11: that might use or be helped by the thing they're
Speaker 11: working on. Other folks are very excited about the technical
Speaker 11: tools themselves, like getting to use the most advanced tools
Speaker 11: and models and getting to work on something technically very exciting.
Speaker 11: Other people are have worked very deeply and you know,
Speaker 11: worked ten years on one thing and are actually looking
Speaker 11: to do something more broad like they're oh, I want
Speaker 11: to learn about a lot of things. Some people love
Speaker 11: to interact with the lot of people. Some people want
Speaker 11: to be a little bit more like I kind of
Speaker 11: want to be given the thing and do my own thing.
Speaker 11: And so I think there's very different work for people
Speaker 11: depending on what they like and what they're interested in.
Speaker 11: And so once you know a little bit more about that,
Speaker 11: like what are the constraints around the kind of jobs
Speaker 11: that people are looking for, then I think it's easy
Speaker 11: to recommend specific like okay, well, and based on geography too,
Speaker 11: like there's just different kinds of jobs in different places
Speaker 11: in North America and the world, and so okay, well,
Speaker 11: for you, it sounds like you're excited about this and
Speaker 11: you're living here and these are your experiences helping people
Speaker 11: describe what they've done and what they want to do next.
Speaker 11: People usually don't need to build new skills. They have
Speaker 11: a lot of skills. It's just they need to have
Speaker 11: some kind of exploration of the space of available things,
Speaker 11: what they want, what they have, how they can describe
Speaker 11: what they've done, and maybe demonstrate it in a different way,
Speaker 11: you know, by talking about it differently. Those are the
Speaker 11: main things I think I would do.
Speaker 1: So I've seen a lot of physicists end up like
Speaker 1: on Wall Street or in data science. These seem to
Speaker 1: be places like where that community has an appetite for
Speaker 1: the like, oh, yeah, we like hiring physicists or whatever. Yeah,
Speaker 1: but tell us some other places where physicists might end
Speaker 1: up some you know, maybe unusual or bizarre places physics
Speaker 1: PhDs end up working in.
Speaker 11: Yeah, that's a good question. I do think people end
Speaker 11: up in a lot of places that basically anywhere where
Speaker 11: people are like building some models to help a system
Speaker 11: run better. So it could be you know, things education,
Speaker 11: educational software. People are trying to build ways to help
Speaker 11: kids learn to read and learn to do math. There's
Speaker 11: all kinds of games that people work on. Anything that
Speaker 11: you buy or sell clothes or you know, any sort
Speaker 11: of products any sort of recommendations for things that you're
Speaker 11: that people are working on. Anything in the healthcare industry.
Speaker 11: I talked about that a lot already. Anything in the
Speaker 11: kind of broad tech you see, there's a ton of
Speaker 11: work right now in AI, certainly large language models. A
Speaker 11: lot of people from physics are working on those tools
Speaker 11: at all the places you can imagine, there's really a
Speaker 11: lot of a lot of places. I can't think of
Speaker 11: one like fun especially funny, like, oh, here's one thing
Speaker 11: you can think of, but really in every area media, fashion,
Speaker 11: people are working in all sorts of areas.
Speaker 1: People working on like optimizing you know, underwear sizes and
Speaker 1: stuff like this. For sure, for sure, that's particle physics
Speaker 1: at work.
Speaker 11: That's right, it's funny and it's a joke. But it's
Speaker 11: also true that like I don't know, for me, finding
Speaker 11: clothes that fit is actually really nice.
Speaker 1: Yes, it's an important unsolved fund. You can make a
Speaker 1: real impact in people's lives.
Speaker 11: I mean, it's like a little bit silly, but it's
Speaker 11: also true that there's a lot of I think there's
Speaker 11: a lot of systems where people have just done the
Speaker 11: same thing forever and having a fresh take on it
Speaker 11: can be helpful.
Speaker 1: Yeah, everybody's got like their favorite pair of jeans or
Speaker 1: their favorite pair of underwear, and there's a reason they
Speaker 1: fit right. It feels good. So there's this lore going
Speaker 1: around that I hear a lot that one of the
Speaker 1: reasons behind the two thousand and eight financial collapse was
Speaker 1: that Wall Street went a little bit crazy with its
Speaker 1: modeling and that there were these crazy quants and most
Speaker 1: of them were ex physicists who didn't really understand the
Speaker 1: system and just like up A, wrote a bunch of
Speaker 1: code that went crazy and destroyed people's lives. So what
Speaker 1: do they have to say that did this just call
Speaker 1: the cause the financial collapse or not?
Speaker 11: Probably not alone. I'll say that the do you think
Speaker 11: there's a superpower kryptonite that physicists are very interested in,
Speaker 11: you know, going down to their root causes, the basic
Speaker 11: How do you take this problem and break it into
Speaker 11: the basic parts? And I think that the kryptonite version
Speaker 11: of that is like thinking that you can do that
Speaker 11: in any field, for any topic without necessarily consulting and
Speaker 11: learning about the domaining knowledge of the practitioners or people
Speaker 11: that have worked in that area. There's a famous data
Speaker 11: science person, Drew Conway who used to say, physicists, we're
Speaker 11: like kind of like will to beasts that would like
Speaker 11: run into an area that seems interesting, like biophysics. Right,
Speaker 11: it's like, oh, there's something interesting there. As here comes
Speaker 11: a lot of old, you know, ex physicists who are like,
Speaker 11: we'll solve all the problems. And so when I would
Speaker 11: give talks to physicis, I would say, don't be a wildbeast, like,
Speaker 11: don't run into air area to a new area. So
Speaker 11: these maybe these two thousand and eight physicists are kind
Speaker 11: of just like I know, I'll break down this problem
Speaker 11: into these parts, and look what I'm doing, isn't it cool?
Speaker 11: But if there was a little bit more domain knowledge
Speaker 11: or thought around how could this go wrong? How might
Speaker 11: this affect people who? Why might we not do this?
Speaker 11: Could have avoided some bad outcomes?
Speaker 1: All right, So maybe we're not totally guilty, just partially.
Speaker 11: Yeah.
Speaker 1: So a lot of our audience are folks who like
Speaker 1: physics and like thinking about physics and have been listening
Speaker 1: to the pod and learning to think like a physicist
Speaker 1: and applying you know, that mental model to questions about
Speaker 1: the universe. But what would be your advice for somebody
Speaker 1: out there who wants to take advantage of this way
Speaker 1: of thinking, somebody who's not necessarily trained as a physicist
Speaker 1: but wants to learn to think like a physicist. What
Speaker 1: would be your advice for learning to think that way?
Speaker 11: Yeah, I think there's this. I'm sure you talk about
Speaker 11: Drake's equation, which is used for thinking about where extraterrestrial
Speaker 11: life might be in the Milky Way?
Speaker 1: Right?
Speaker 11: Is that right? You probably know much more of that.
Speaker 11: So that's the thing where you kind of are taking
Speaker 11: these pieces. Anybody can look up the Drake equation or
Speaker 11: Drake's equation and taking these pieces and trying to put
Speaker 11: it together to get one answer. And I went to
Speaker 11: a business class where people were talking about using this
Speaker 11: same sort of thing to model businesses or other processes
Speaker 11: where it's just trying to think about anybody can think
Speaker 11: about what are the parts that come together to h
Speaker 11: to create some answer or some prediction. And so just
Speaker 11: take thinking about that. Breaking something up into things that
Speaker 11: you can measure individually or you can think about individually,
Speaker 11: can really help solve a problem, whether it's a science problem,
Speaker 11: business problem, any kind of problems.
Speaker 1: All right, And so then last question a bit of
Speaker 1: a personal one. What do you miss most about actively
Speaker 1: working in physics? I would say about being a physicist,
Speaker 1: because I think you're always a physicist once you're trying,
Speaker 1: like once a Jedi, always in Jedi. But what do
Speaker 1: you miss most about like working on particle physics other
Speaker 1: than working with me? Obviously I was going to say.
Speaker 11: I mean, you're joking, but I think I really really did.
Speaker 11: There's a very special, fun, exciting environment of being at
Speaker 11: the lab in these big experiments and both that you
Speaker 11: know at SLACK in California, at Fermi Lab, Brooke Gaven
Speaker 11: at CERN that these labs just it's really literally people
Speaker 11: from all over the world and having lunch together, and
Speaker 11: the big cafeteria. Cerns called our One restaurant one a
Speaker 11: very creative name. I don't know if it still is.
Speaker 11: It's not named after someone now, is it still our one? Yeah?
Speaker 11: So our one. So if you're there for lunch or
Speaker 11: for coffee or the end of the day, it's just
Speaker 11: really fun to run into so many people that you've
Speaker 11: worked with over your whole career, people who are getting
Speaker 11: into the field, people who are very senior. You never
Speaker 11: know who's going to be there, just having some food,
Speaker 11: drinking coffee, and getting to talk to people about what
Speaker 11: they're working on and also what they're doing and how
Speaker 11: they are. It's very very fund memories of hanging out
Speaker 11: there with with all sorts of people and yeah, no,
Speaker 11: it's a great time. So I would say just missing
Speaker 11: being with with with all the people that we used
Speaker 11: to work with and getting to meet new people. That's
Speaker 11: a really truly international environment too, really fun.
Speaker 1: It is fun to hear conversations in so many different languages.
Speaker 1: I like running into the same really old Nobel Prize
Speaker 1: winners over and over again, introducing myself every single time
Speaker 1: because they don't remember me because they're like one hundred
Speaker 1: and fifty years old. And I also remember one of
Speaker 1: the first times I was at our one and you
Speaker 1: had some special trick for making an iced coffee. When
Speaker 1: you showed it to me and Katrina and for the
Speaker 1: rest of the summer we were like, oh, let's get
Speaker 1: a Kathy. We called it a Kathy and pacino a coppacina.
Speaker 4: That's right, Yeah, yeah, I did.
Speaker 11: I've made up created a TGI Fridays. I don't know
Speaker 11: if you're looking for sponsorships. Daniel Tgi Fridays. The restaurant
Speaker 11: when I was a server there, created the copacina Copacino delicious.
Speaker 1: Thank you got us through that summer.
Speaker 11: Yeah, they don't have They didn't have cappuccino machines that started.
Speaker 11: They had espresso machines, but no, no cappuccino machines, so
Speaker 11: you got to figure it out.
Speaker 1: All right. Well, thanks very much for sharing with us
Speaker 1: how to think like a physicist and how to drink
Speaker 1: coffee like a physicist. Really appreciate it.
Speaker 11: We'll put the recipe people can.
Speaker 1: That's in the show notes show notes exactly. All right,
Speaker 1: thanks Kathy.
Speaker 4: All right, interesting talk there, Daniel. It seems like she's
Speaker 4: basically saying we all have skills.
Speaker 1: Everybody's skills are different at least.
Speaker 6: Yeah.
Speaker 1: I think she probably aligns with you to think that,
Speaker 1: like scientists are all curious thinkers and mental model builders,
Speaker 1: and not even all physicists are the same. We all
Speaker 1: think differently and enjoy different parts of the process.
Speaker 4: M and that can help you get a job, right,
Speaker 4: because these are all skills that we could all use
Speaker 4: in every field.
Speaker 1: Probably, yeah, exactly. And so in the end, thinking like
Speaker 1: a physicist is just like thinking like a scientist, being
Speaker 1: a curious person, trying to understand the world, being methodical
Speaker 1: about it, trying not to fool yourself with what the
Speaker 1: data is telling you.
Speaker 4: Yeah, and just trying to maximize your functionality to the stakeholders.
Speaker 1: Exactly, maximize shareholder revenue.
Speaker 4: Mike andmized physicists employment.
Speaker 1: Try not to cause any more financial collapses. Please.
Speaker 4: All right, Well, an interesting discussion about thinking like a scientist,
Speaker 4: thinking like a physicist. What are the commonalities and how
Speaker 4: things might be a little bit unique for people who
Speaker 4: pursue physics as a career.
Speaker 1: And for those of you out there not pursuing physics
Speaker 1: as a career but who have discovered a love for physics,
Speaker 1: keep doing it, keep thinking like a physicist or a scientist,
Speaker 1: and keep being curious about the world and trying to
Speaker 1: make the whole thing click together in your mind.
Speaker 4: Yeah, but mostly just keep thinking, please.
Speaker 1: And keep listening to the pod. Thanks everybody, We.
Speaker 4: Hope you enjoyed that. Thanks for joining us. See you
Speaker 4: next time.
Speaker 1: For more science and curiosity. Come find us on social media,
Speaker 1: where we answer questions and post videos. We're on Twitter,
Speaker 1: disc Org, Insta, and now TikTok. Thanks for listening. And
Speaker 1: remember that Daniel and Jorge Explain the Universe is a
Speaker 1: production of iHeartRadio. For more podcasts from iHeartRadio, visit the
Speaker 1: iHeartRadio app, Apple Podcasts, or wherever you listen to your
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