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Speaker 1: Andy Daniel, Did you always want to be a paid physicist?

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Speaker 2: Definitely not. When I was a kid, I did not

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Speaker 2: want to be a physicist.

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Speaker 1: Really, you knew what it was, but you knew you

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Speaker 1: didn't want to be one.

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Speaker 2: I don't think I understood what a scientist was well enough.

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Speaker 2: But when I was a kid, I wanted to be

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Speaker 2: an explorer. I wanted to get on a ship and

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Speaker 2: find some new island and name it after myself.

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Speaker 1: You just want to get out of Los Alamos.

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Speaker 2: Main purpose here, Yeah, though you can't really take a

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Speaker 2: ship out of Los Alamos because it's landlocked. So there

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Speaker 2: were some basic problems in my thinking.

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Speaker 1: Well, you could take a train and then a ship.

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Speaker 1: But don't they say everyone's a physicists, especially little kids.

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Speaker 2: Yeah, I think everybody is a scientist because they're curious

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Speaker 2: about the world. And in the end I discovered that

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Speaker 2: being a physicist it's kind of like being an explorer,

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Speaker 2: except instead of discovering new continents, we're trying to discover

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Speaker 2: new frontiers of knowledge.

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Speaker 1: Instead of surfing waves out there in the sea, you're

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Speaker 1: surfing the couch.

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Speaker 2: Mostly, I'm clickly clacking my way to new shores of knowledge.

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Speaker 1: Just don't get scurvy on your couch.

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Speaker 2: I got a bowl of limes here next to me.

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Speaker 1: Okay for with the tequila and the margaritas. That's for

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Speaker 1: after work, after work work these days? What's the difference. Hi,

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Speaker 1: I'm Hori. I'm a cartoonist and the author of Oliver's

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Speaker 1: Great Big Universe.

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Speaker 2: Hi, I'm Daniel. I'm a particle physicist and a professor

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Speaker 2: at UC Irvine, and I want to teach people to

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Speaker 2: think like a physicist.

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Speaker 1: Wait, I'm confused. If everyone's a physicist, aren't just teaching

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Speaker 1: people to think like humans?

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Speaker 2: Yeah, basically, I'm done. I can retire. It's after work time, margarita.

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Speaker 1: I know, let's get the shots going.

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Speaker 2: No, I think everybody does have curiosity. But you know,

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Speaker 2: it took us a while to figure out some tips

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Speaker 2: and some tricks to effectively extract knowledge from the universe

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Speaker 2: rather than just like, you know, making up cute stories

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Speaker 2: to satisfy our curiosity.

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Speaker 1: Right, it probably took a while to get paid to

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Speaker 1: do it too.

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Speaker 2: Yeah, that's certainly true. A lot of the big names

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Speaker 2: in the history of science were men of leisure, you know,

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Speaker 2: operating on their trust funds or daddy's bank account.

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Speaker 1: Who do you think was the first professional physicists?

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Speaker 2: You know, science as a profession is not actually that old.

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Speaker 2: It's something like in the late eighteen hundreds that people

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Speaker 2: started to call themselves scientists and get paid to do it.

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Speaker 2: There are money to hire people to do this kind

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Speaker 2: of research. Until then, it was you know, natural philosophers

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Speaker 2: and people just sort of like curious, poking around in

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Speaker 2: their own laboratories. Yeah, but scientists as a job is

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Speaker 2: not much more than like one hundred years old.

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Speaker 1: WHOA. So even the word science is relatively new.

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Speaker 2: Yeah, exactly. If you ask like Gaos or Newton or

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Speaker 2: leading It or Aristotle, they certainly would not call themselves

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Speaker 2: a scientist. That's a new word.

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Speaker 1: Or maybe they did it on purpose. They're like science,

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Speaker 1: No thanks, it's a new fangled thing that all the

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Speaker 1: kids are talking about. I prefer to be a natural philosopher.

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Speaker 1: But anyways, welcome to our podcast, Daniel and Jorge Explain

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Speaker 1: the Universe, a production of iHeartRadio.

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Speaker 2: In which we do our best to demonstrate what it's

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Speaker 2: like to think like a physicist. We take a physicist

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Speaker 2: approach to dismantling the whole universe, understanding all of its

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Speaker 2: little bits, building mental mathematical models to try to explain it,

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Speaker 2: asking questions of those models, and then wondering what does

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Speaker 2: it all mean anyway?

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Speaker 1: Yeah, because, as we talked about before, the universe belongs

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Speaker 1: to everyone, and asking questions is everyone's job, but a

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Speaker 1: few people get to do it as a career.

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Speaker 2: Get to Yes, exactly. It's definitely a treat and a privilege.

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Speaker 1: Well, you get paid to do it, I guess, and

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Speaker 1: to do that, there's a certain mindset you have to have,

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Speaker 1: right in order to be part of the profession.

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Speaker 2: Yeah, there definitely is a way of thinking that's sort

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Speaker 2: of like a physicist way of thinking. And I see

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Speaker 2: this because people who are trained as physicists and then

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Speaker 2: go out into the world and work in other areas

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Speaker 2: chemistry or engineering or computer science still take with them

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Speaker 2: a certain mindset, a certain way of approaching problems, which

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Speaker 2: can be really really helpful and useful or also sometimes

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Speaker 2: frustrating for their colleagues.

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Speaker 1: Yeah, no, I can totally relate. I think that also

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Speaker 1: the same is for engineers. You know, anyone who studied

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Speaker 1: engineering definitely thinks like an engineer is trying to think

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Speaker 1: it a certain way and a certain mindset about tackling

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Speaker 1: problems for sure.

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Speaker 2: Yeah, absolutely take an engineering approach to cartooning.

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Speaker 1: For example, Yeah, whenever I draw a bridge, I mean

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Speaker 1: I really put some calculations behind it, why to make

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Speaker 1: sure it doesn't fall down?

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Speaker 2: Yeah, I know all those cartoons could be injured. I mean,

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Speaker 2: think about their families.

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Speaker 1: Yeah. I usually build in a safety factor of like

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Speaker 1: two or three to every cartoon, just in case. But yeah,

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Speaker 1: but professional physicists do think about things in a very

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Speaker 1: different way than the rest of us. And so that's

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Speaker 1: the question we'll be exploring today. So to the on

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Speaker 1: the podcast, we'll be taggling how to think like a physicist?

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Speaker 2: And I'm not sure if this should be like an

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Speaker 2: instruction manual or like.

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Speaker 1: A warning, Oh why what can happen?

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Speaker 2: You know, like watch out for these signs that you're

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Speaker 2: thinking like a physicist, or like, hey, would you like

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Speaker 2: to think like a physicist? Here's steps one, two, five.

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Speaker 1: Well, I guess if it was the former, we should

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Speaker 1: title the eposite, hard to Not Think like a Physicist?

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Speaker 1: How to avoid thinking like a physicist.

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Speaker 2: We're going to get into the positives, I'm sure, but

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Speaker 2: you know, there is this lore that sometimes physicists oversimplify things.

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Speaker 2: They're like, come into a new field, They're like, oh,

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Speaker 2: these can just approximate this with a sphere, maybe a

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Speaker 2: line on it or whatever. There's this urban legend that

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Speaker 2: physicists being too simplistic or the cause of the two

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Speaker 2: thousand and eight financial collapse, for example. So you know,

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Speaker 2: there are potentially some dangers to applying physics thinking to

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Speaker 2: the broader world.

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Speaker 1: Daniel, I wonder if you're overestimating how much people think

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Speaker 1: about physicists.

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Speaker 2: Probably I definitely don't have a clear view of that.

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Speaker 1: I mean, I think for an urban legend to exist,

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Speaker 1: do you sort of need urban people talking.

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Speaker 2: About Maybe that's just an urban legend within physics, maybe

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Speaker 2: nobody else.

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Speaker 1: It's like a yeah, just have issues.

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Speaker 2: We definitely do.

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Speaker 1: But it's an interesting question to ask if you're thinking

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Speaker 1: about following a career in physics, or wondering what is

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Speaker 1: the job entail and what kind of mindset do you

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Speaker 1: have to have in order to do it at a

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Speaker 1: research university or to become one, or to get a

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Speaker 1: degree in.

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Speaker 2: It, or if you're just an armchair physicist, if you

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Speaker 2: like thinking about the nature of the universe and making

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Speaker 2: progress and over the years, maybe while listening to this podcast,

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Speaker 2: you've been putting together your own personal mental model of

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Speaker 2: the universe, asking questions, trying to click it together, coming

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Speaker 2: to a holistic understanding of how things work. In that case,

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Speaker 2: you might have picked up a few of the tricks

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Speaker 2: of thinking like a physicist.

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Speaker 1: Well, as usually, we were wondering how many people out

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Speaker 1: there had thought about this question, had maybe wondered what

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Speaker 1: it's like to be a professional physicist and what kind

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Speaker 1: of mental skills you need to be one.

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Speaker 2: Thanks very much to everybody who answers these random questions.

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Speaker 2: Love hearing your thoughts. Please don't be shy if you

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Speaker 2: want to join the group, just write to me to

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Speaker 2: Questions at Danielandjorge dot com.

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Speaker 1: So think about it for a second. What do you

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Speaker 1: think it takes to think like a physicist? Here's what

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Speaker 1: people have to say.

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Speaker 3: A physicist must think of small and apply it to

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Speaker 3: the infinitely large universe, and that's not easy to do.

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Speaker 3: Hence the podcast for the rest of us.

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Speaker 4: What if that's an expression? I haven't heard of it before,

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Speaker 4: so it's value. I think it probably would refer to

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Speaker 4: someone being very practical, someone following the scientific method very dogmatic, accurate.

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Speaker 4: But then some theoretical physicists that are a bit wacky

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Speaker 4: in what they come up with, so possibly a little

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Speaker 4: bit of that too.

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Speaker 2: I think like a physicist is to be asking questions

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Speaker 2: and be relentless in your quest for an answer. I'd

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Speaker 2: say thinking like a physicist means being curious and searching

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Speaker 2: for answers through trial and error and experiments.

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Speaker 5: This means there's our podcast about physicists, and to do

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Speaker 5: it with your cartoonist friend.

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Speaker 6: Basically, to think like a physicist means if you discover something,

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Speaker 6: you get to really terrible name that doesn't make sense.

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Speaker 5: I think it means to contemplate matter and energy and

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Speaker 5: their interactions with one another.

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Speaker 7: Well, from the episodes that I have listened to so far,

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Speaker 7: I would that to think like a physicist means to

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Speaker 7: be inquisitive, to try to make connections between different aspects

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Speaker 7: facets of life, and wondering why and.

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Speaker 2: Trying to.

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Speaker 7: Better understand and explain the phenomena we see throughout our

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Speaker 7: daily lives.

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Speaker 1: All right, I like some of these answers. I guess

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Speaker 1: we're done because one of them said, we just need

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Speaker 1: to start on a podcast about physics.

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Speaker 2: And then give everything you discover a terrible name. These

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Speaker 2: are some juicy answers.

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Speaker 1: I guess people have been listening to our podcast.

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Speaker 2: I love these answers because there's so meta. They tell

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Speaker 2: me basically what people have learned from listening to the

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Speaker 2: podcast for all these years. It's fantastic.

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Speaker 1: Well, hopefully people are thinking a little bit more like scientists,

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Speaker 1: like rational thinkers because of this podcast, and also maybe

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Speaker 1: learning a little bit more about the universe and how

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Speaker 1: it all works down to the atomic level and the

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Speaker 1: galactic level.

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Speaker 2: Yeah, and not just absorbing facts and little bits of knowledge,

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Speaker 2: pieces of information, but also training yourself into how to

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Speaker 2: accumulate more information, how to fit those pieces of information together,

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Speaker 2: how to think about them. Science is more than just

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Speaker 2: what we've learned. It's how we're going to learn more.

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Speaker 1: How are we driving the distinction here between physicis and

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Speaker 1: just a regular scientist or do you mean how to

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Speaker 1: think like a scientist?

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Speaker 2: Yeah, it's a great question. I don't know the answer

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Speaker 2: to that. I'm probably not even the right person to

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Speaker 2: answer the question of how do physicists think because I'm

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Speaker 2: stuck in that mindset. I can't really see outside of

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Speaker 2: it to understand how other people think. But when I

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Speaker 2: mean chemists or biologists or economists, I do notice that

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Speaker 2: answer and ask questions in a different way. There's something

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Speaker 2: I have more in common with other physicists than I

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Speaker 2: have with other scientists. So there's something to it.

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Speaker 1: All, right, Well, let's dig into it. What do you

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Speaker 1: think is specific about how physicists think.

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Speaker 2: I think some of it comes from the fundamental motivation

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Speaker 2: and the assumptions that underlie physics. Like the goal is big.

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Speaker 2: We want to understand the universe. We want to figure

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Speaker 2: it out. And the assumptions are pretty basic. They're like, look,

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Speaker 2: the universe is understand a bull, and we can't describe

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Speaker 2: it with mathematical laws. We can build a mental model.

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Speaker 2: The model should follow those laws, and we can use

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Speaker 2: it to like predict the future and to understand the

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Speaker 2: nature of the universe. You know, inherent in that is

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Speaker 2: that we are simplifying the universe. We're taking all these

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Speaker 2: observations and the weaving them together into a story. That's

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Speaker 2: what the mathematical model is. We're saying, here's how this works,

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Speaker 2: here's what's really happening behind the scene. So there's sort

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Speaker 2: of like an ambition there to say, like we can

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Speaker 2: describe the basic elements of the universe, whereas, and again

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Speaker 2: I'm not an expert in other fields, you know, they

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Speaker 2: feel a little bit more zoomed out, so they're not

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Speaker 2: always as ambitious about like the fundamental understanding. They're describing

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Speaker 2: things as sort of a higher level, which again still

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Speaker 2: requires mathematical modeling and great precision. It's not a question

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Speaker 2: of like precision or rigors, just a question of like

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Speaker 2: the ambition the context of the questions you're asking.

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Speaker 1: Well, well, are you saying that other scientists are not

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Speaker 1: as ambitious?

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Speaker 2: I think maybe philosophically, physics and at least fundamental physics

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Speaker 2: and particle physics is asking more ambitious questions than other fields. Yeah,

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Speaker 2: I think they have deeper and broader implications again philosophically.

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Speaker 1: Right, right, So you think your topic of research is

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Speaker 1: more important than other scientists because you're a physicist. I'm

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Speaker 1: just saying there might be a little bit of a

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Speaker 1: bias here at Daniel.

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Speaker 2: No, it's totally reasonable to dig into that. I wouldn't

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Speaker 2: say more important. You know, somebody who's developing new techniques

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Speaker 2: to develop green energy, for example, they're not answering deep

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Speaker 2: and fundamental questions about the nature of reality. But they're

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Speaker 2: improving people's lives and maybe saving the planet, so that's

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Speaker 2: arguably much more important. But I think in terms of

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Speaker 2: the philosophical context of our lives, particle physics and fundamental

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Speaker 2: physics is answering those questions. Whether that's important or not

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Speaker 2: is totally subjective, you know, whether it has value. Every

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Speaker 2: kind of science is answering different kinds of questions, giving

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Speaker 2: different kinds of insight into how the universe works. For me,

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Speaker 2: at least one of the appeals of fundamental physics are

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Speaker 2: these philosophical implications of it.

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Speaker 1: Right, Well, I think, you know, most scientists would agree

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Speaker 1: that what they're doing is also trying to understand and

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Speaker 1: explain the world. I wonder if maybe a lot of

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Speaker 1: the difference is just in the topic and the kinds

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Speaker 1: of things that you're looking at the scope of it,

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Speaker 1: or the kinds of phenomena you're looking at.

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Speaker 2: Yeah, I think that everybody is doing the thing they

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Speaker 2: think is most interesting and most exciting, and that's very personal.

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Speaker 2: Right the person who's like crouching in a rainforest watching

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Speaker 2: spiders crawl up twigs for hours and hours a day,

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Speaker 2: is deeply fascinated by that and chose to do that

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Speaker 2: instead of economics or psychiatry or whatever for a reason.

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Speaker 2: And that's totally cool. So you're right, and the choice

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Speaker 2: of topic is very, very personal. But I think the

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Speaker 2: choice of topic also sometimes leads to a different way

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Speaker 2: of thinking. Like I think, because we're trying to ask

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Speaker 2: fundamental questions and deep questions about the universe, we feel

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Speaker 2: like we can touch onto some sort of mathematical purity,

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Speaker 2: that there is maybe mathematics that describes this that we

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Speaker 2: can drill down into and reveal, you know, somebody who's

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Speaker 2: studying like hurricanes. You know, we don't have any mathematics

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Speaker 2: that describes hurricanes. So we can do some simulations, but

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Speaker 2: we're sort of at a loss because of all the

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Speaker 2: chaos and the details. But when you zoom down into

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Speaker 2: the fundamental firmament of the universe, we hope maybe there

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Speaker 2: is some mathematics there that can describe what's going on.

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Speaker 2: And so that's I think why physicists tend to build

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Speaker 2: these mental mathematical models, sometimes too simplified, you know, hence

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Speaker 2: the famous spherical cowjoke, which I don't know, maybe that's

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Speaker 2: only a famous joke within physics.

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Speaker 1: You tell me, I've never heard of that before. But

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Speaker 1: you know, I think all scientists would say that what

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Speaker 1: they're doing is fundamental as well. Like, if you're studying spiders,

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Speaker 1: you're probably thinking about the different ways that life can form,

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Speaker 1: or the different factors that go into creating life and

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Speaker 1: the factors that shape live That seems pretty fundamental as well. Yeah,

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Speaker 1: and ambitious.

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Speaker 2: M h. And if anything, I think you probably have

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Speaker 2: a lot more insight into this than I do, or

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Speaker 2: than most people, because you interact with so many different

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Speaker 2: kinds of scientists and obviously you've been spending a lot

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Speaker 2: of time learning about physics and decoding the brains of

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Speaker 2: physicists but also other scientists, And so from your perspective,

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Speaker 2: I'd be very curious to hear, like, do you think

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Speaker 2: physicists think differently? Is the mind of physicists trained at

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Speaker 2: different skills? Do they take a different approach or all

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Speaker 2: scientists just one category?

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Speaker 1: For you? You know, I think that if you're a scientist,

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Speaker 1: you're probably trying to figure out how the world and

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Speaker 1: the universe works. You're just asking questions about different phenomena

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Speaker 1: in it. You know, if you're someone who studies hurricanes.

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Speaker 1: You're trying to understand how certain physical processes work and

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Speaker 1: how they can come together to create large effects. For example,

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Speaker 1: that seems pretty fundamental as well or as fundamental as

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Speaker 1: asking you know what an atom is made of of?

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Speaker 2: Yeah, can spiders come together to make hurricanes? Wouldn't that

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Speaker 2: be awesome? And shouldn't we pitch that show to the

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Speaker 2: Discovery Channel?

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Speaker 1: Spider NATO's.

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Speaker 2: Spider Cane.

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Speaker 1: So I don't know, Sorry, science Spider NATO's sounds like

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Speaker 1: a winner.

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Speaker 2: Yeah, well, I can't tell you whether it's fundamentally different

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Speaker 2: from the way other scientists think, because I'm not other scientists.

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Speaker 2: Maybe you can comment, but I can try to keep

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Speaker 2: you a little bit of an insight into the way

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Speaker 2: I approach a problem with the way I think about problems,

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Speaker 2: and that's this reliance on building a model. You know.

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Speaker 2: I look at a science problem like where is that

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Speaker 2: ball going to land after it comes off the bat?

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Speaker 2: Try to predict that? And I think, well, to get

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Speaker 2: that exactly right is way too complicated, And there's so

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Speaker 2: many factors involved. There's the wind speed, there's that bird

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Speaker 2: flying by, there's tufts in the air, et cetera, and

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Speaker 2: so I build a simpler model of the universe. I say,

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Speaker 2: toss out the real universe. Can we come up with

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Speaker 2: a simpler version of the universe and ask the question

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Speaker 2: in that universe, but build a model in such a

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Speaker 2: way that the answer in the simpler universe is still

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Speaker 2: relevant to reality. So can we extract the crucial details

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Speaker 2: of the problem, put those into our model, and then

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Speaker 2: use that to answer the question. So you know, you

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Speaker 2: don't care, for example, about the color of the ball,

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Speaker 2: you don't care whether some kid in the stand is

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Speaker 2: eating ice cream. None of these details about glorious reality

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Speaker 2: matter to answering this question. So you build the simpler

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Speaker 2: model specific to that question because it's good at answering

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Speaker 2: that question, not every other question. And you use that

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Speaker 2: to answer the question. And you know, you can argue

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Speaker 2: philosophically like is that model real, what does it mean

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Speaker 2: about the universe if it works, et cetera, et cetera.

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Speaker 2: But that's sort of to me, the core of thinking

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Speaker 2: like a physicist is building a little mental model and

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Speaker 2: then using that to answer your questions.

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Speaker 1: Yeah, I think you're basically describing what any scientist does.

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Speaker 1: You know, chemists, biologists, they all work off models. I

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Speaker 1: mean probably the word model is the most used word

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Speaker 1: in all of science. Yeah, you know, biologists make models

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Speaker 1: about evolution, about gene interactions, about how molecules interact, or

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Speaker 1: how a species propagated, and things like that. But I

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Speaker 1: wonder if the difference with you is that you're making

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Speaker 1: models about the physical world or about baseballs, for example,

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Speaker 1: and not spiders.

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Speaker 2: Spiders are just way too complicated. There's no way for

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Speaker 2: me to build a model of a spider. I have

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Speaker 2: no idea exactly, and I know how to make the

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Speaker 2: approximation so that I can describe a baseball. I know

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Speaker 2: what to ignore. Maybe that's just my physics intuition, but

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Speaker 2: I don't know how to do that for a spider,

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Speaker 2: and I want to push back a little bit. I

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Speaker 2: do think there's a difference between the models built by

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Speaker 2: physicists and those built biologists, for example. I mean, in biology,

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Speaker 2: we know that every model we build is effective. It's

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Speaker 2: not fundamental. It's describing some emerging phenomenon like butterflies or spiders.

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Speaker 2: Something we know is not an inherent object in the universe,

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Speaker 2: but made out of those bits. It comes together through

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Speaker 2: a special arrangement. So biology isn't describing something inherent to

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Speaker 2: the universe. It's just approximately describing how things work during

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Speaker 2: special conditions where like spiders and butterflies happen to emerge

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Speaker 2: because they don't always right. There's a long time in

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Speaker 2: the universe without spiders and butterflies, and so those rules

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Speaker 2: don't apply in those scenarios. But physics is trying to

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Speaker 2: figure out the fundamental laws, those that always apply in

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Speaker 2: all circumstances that are inherent to the universe. And that

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Speaker 2: difference in goal, I think leads to a different way

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Speaker 2: of thinking, you know, good or bad. It leads to

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Speaker 2: a hubrist that we can describe anything with simple laws,

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Speaker 2: and it leads to different approaches and in different scientific culture,

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Speaker 2: so that physicists are kind of recognizable to others and

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Speaker 2: also to each other.

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Speaker 1: Well, you're married to a biologist, how does your way

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Speaker 1: of thinking different from your spouses?

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Speaker 2: Yeah, I think something that's different in between the way

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Speaker 2: that I think about things and the way biologists like

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Speaker 2: my wife think about things is we're definitely much more

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Speaker 2: focused on questions of like uncertainty and making things quantitative

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Speaker 2: in order to try to extract some knowledge. Sometimes the

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Speaker 2: things we're dealing with are abstract or indirect. You know,

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Speaker 2: we're talking about tiny particles or things we can't ever

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Speaker 2: see or even struggle to visualize. And so to help

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Speaker 2: us guide our thinking, we rely really heavily on the uncertainty.

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Speaker 2: How well do we know this? What can we say

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Speaker 2: about this? Because we don't have much intuition, we can't

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Speaker 2: like always got check our answers and say, is that

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Speaker 2: reasonable that the top quark lives for ten to the

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Speaker 2: minus twenty three seconds? I mean, you can't see that anyway.

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Speaker 2: Whereas you know, my wife she can look at stuff

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Speaker 2: and oh is it growing? And did we get this right?

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Speaker 2: Is this virus killing that bacteria? Is somebody's got health

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Speaker 2: improving when they eat more chia seeds, this kind of stuff.

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Speaker 1: But she works with models as well, right, Her.

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Speaker 2: Grad students are really good looking. Yes, they're like models.

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Speaker 1: Yeah, yeah, well in comparison to physicists, I'm sure.

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Speaker 2: Ooh, you're right though, that models is a very abused word,

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Speaker 2: Like I also work in the machine learning community, and

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Speaker 2: their model means to make very very different than a

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Speaker 2: model in physics, than a model in fashion, and so

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Speaker 2: it's a very generic word unfortunately.

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Speaker 1: But you think that maybe it's something to do with

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Speaker 1: the way that you look at the world and you

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Speaker 1: formulate models. But I guess I'm trying to say that

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Speaker 1: I think that's what all scientists do, right, across different fields.

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Speaker 2: Yeah, so maybe physicists have more in common with other

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Speaker 2: scientists than I ever imagined. Happy to.

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Speaker 1: Sounds like you need to talk to people outside your

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Speaker 1: department a little more, baby, besides your spouse, horten to.

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Speaker 1: You interact with the economists or chemists.

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Speaker 2: Economists very rarely, only if I run into them at

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Speaker 2: the park. Chemists and computer scientists and engineers much more common.

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Speaker 2: We sometimes have problems in common, you know, working on

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Speaker 2: electronics for a new technology we want to bury in

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Speaker 2: the ice in Antarctica, we need to understand the engineering

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Speaker 2: details of it, or thinking about how to apply machine

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Speaker 2: learning techniques we've developed for neutron stars to the problem

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Speaker 2: of like predicting organic synthesis, these kind of things. So, yeah,

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Speaker 2: definitely interact with the more physical science and engineering people

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Speaker 2: more often than like psychiatrists. But I also talk to

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Speaker 2: philosophers quite a bit. I don't know if they qualify

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Speaker 2: as scientists.

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Speaker 1: Do they I think they're not usually They're not in

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Speaker 1: the same department for a reason, isn't it.

439
00:21:49,840 --> 00:21:53,040
Speaker 2: It's fascinating though, Actually people in the philosophy of physics

440
00:21:53,080 --> 00:21:56,440
Speaker 2: department here, they all have their PhDs in physics rather

441
00:21:56,480 --> 00:21:57,520
Speaker 2: than in philosophy.

442
00:21:57,680 --> 00:22:01,719
Speaker 1: Well, so they're physicists who have alosophy degree in the

443
00:22:01,760 --> 00:22:04,160
Speaker 1: philosophy of science physics.

444
00:22:05,080 --> 00:22:08,440
Speaker 2: That a doctor of philosophy of physicists, but now they're

445
00:22:08,520 --> 00:22:10,680
Speaker 2: professors in philosophy of physics.

446
00:22:12,680 --> 00:22:15,840
Speaker 1: It sounds like what is it the snake finally ate

447
00:22:15,880 --> 00:22:20,359
Speaker 1: its tale. It is interesting to think about how people

448
00:22:20,359 --> 00:22:23,080
Speaker 1: who are paid to do physics in particular think and

449
00:22:23,400 --> 00:22:26,080
Speaker 1: what kinds of what makes them a tick I guess,

450
00:22:26,119 --> 00:22:28,800
Speaker 1: and how does that color how they see the world,

451
00:22:29,320 --> 00:22:31,679
Speaker 1: and so to get more insight into that, Danielle, you

452
00:22:31,680 --> 00:22:35,040
Speaker 1: interviewed a couple of physicists and one ex physicists.

453
00:22:35,119 --> 00:22:37,920
Speaker 2: Yeah, that's right. I talked to one physicist who's made

454
00:22:37,920 --> 00:22:41,400
Speaker 2: it her mission to explain to people how physicists think

455
00:22:41,480 --> 00:22:46,480
Speaker 2: about uncertainty, and another whose job is to guide physicists

456
00:22:46,520 --> 00:22:50,040
Speaker 2: into the real world to find positions outside of academic

457
00:22:50,080 --> 00:22:51,080
Speaker 2: physics and research.

458
00:22:51,480 --> 00:22:53,760
Speaker 1: Well, it sounds like these are sort of like physics

459
00:22:53,800 --> 00:22:56,879
Speaker 1: translators or physics counselors.

460
00:22:59,720 --> 00:23:03,480
Speaker 2: Yeah, exactly, trying to bridge the gap between physicists and

461
00:23:03,640 --> 00:23:04,639
Speaker 2: actual human beings.

462
00:23:04,680 --> 00:23:06,080
Speaker 1: All right, well, when we come back, we'll listen to

463
00:23:06,200 --> 00:23:11,000
Speaker 1: Daniel talking to two physicists whose jobs it is to

464
00:23:11,040 --> 00:23:14,479
Speaker 1: translate what physicists think and do to the rest of

465
00:23:14,560 --> 00:23:17,760
Speaker 1: the universe. So we'll dig into that, but first let's

466
00:23:17,760 --> 00:23:18,760
Speaker 1: take a quick break.

467
00:23:31,320 --> 00:23:31,480
Speaker 8: Bar.

468
00:23:31,520 --> 00:23:34,520
Speaker 1: We're asking the question how to think like a physicist

469
00:23:35,040 --> 00:23:37,359
Speaker 1: and apparently that involves talking to more.

470
00:23:37,240 --> 00:23:40,840
Speaker 2: Physicists group think like a physicist?

471
00:23:44,240 --> 00:23:46,280
Speaker 1: All right, well, you got to interview you too interesting

472
00:23:46,320 --> 00:23:50,520
Speaker 1: people here, Daniel. First one is doctor Jen Kyle. What

473
00:23:50,600 --> 00:23:51,359
Speaker 1: does Jen Kyle do?

474
00:23:51,800 --> 00:23:55,240
Speaker 2: Jen Kyle is a theoretical physicist, but she also runs

475
00:23:55,320 --> 00:23:58,520
Speaker 2: the YouTube channel Think like a Physicist, where she tries

476
00:23:58,560 --> 00:24:00,920
Speaker 2: to explain to you how do you use the techniques

477
00:24:00,920 --> 00:24:03,480
Speaker 2: and tricks of physics to think about the world and

478
00:24:03,640 --> 00:24:06,840
Speaker 2: also to decode science results so you can get an

479
00:24:06,920 --> 00:24:10,399
Speaker 2: understanding for whether that newsflash you just read about black

480
00:24:10,440 --> 00:24:12,080
Speaker 2: holes is real or not?

481
00:24:12,520 --> 00:24:14,320
Speaker 1: And did she talk to non physicists to figure out

482
00:24:14,359 --> 00:24:17,160
Speaker 1: if a physicists thinking of unique way.

483
00:24:18,200 --> 00:24:21,119
Speaker 2: Through her YouTube channel? So yeah, via the comments section.

484
00:24:21,520 --> 00:24:25,560
Speaker 1: Oh boy, and we all know how productive. Those can be.

485
00:24:26,880 --> 00:24:29,480
Speaker 2: Great insights in the comment section as always.

486
00:24:29,680 --> 00:24:32,800
Speaker 1: All right, well, here's Daniels interview with particle physicists and

487
00:24:32,960 --> 00:24:34,479
Speaker 1: YouTuber Jen Kyle.

488
00:24:38,720 --> 00:24:42,880
Speaker 2: So it's my pleasure to introduce the podcast doctor Jen Kyle. Jen,

489
00:24:42,920 --> 00:24:44,359
Speaker 2: thanks very much for joining us today.

490
00:24:44,480 --> 00:24:45,800
Speaker 9: Hi, great to be here.

491
00:24:46,840 --> 00:24:49,879
Speaker 2: Great. Tell us a little bit about yourself. What's your

492
00:24:49,880 --> 00:24:52,120
Speaker 2: background with your training? What are you up to now?

493
00:24:52,520 --> 00:24:52,800
Speaker 9: Ah?

494
00:24:52,840 --> 00:24:59,800
Speaker 5: Well, I'm a theoretical particle physicist. I've done mostly work

495
00:25:00,359 --> 00:25:03,720
Speaker 5: beyond the standard model physics. I've looked at some things

496
00:25:03,720 --> 00:25:10,320
Speaker 5: on dark matter and possible new theories of flavor in

497
00:25:10,359 --> 00:25:14,560
Speaker 5: the Cork and Lepton sectors, and I basically dabbled in

498
00:25:14,680 --> 00:25:16,320
Speaker 5: physics beyond what we know now.

499
00:25:16,720 --> 00:25:20,639
Speaker 2: Great, so you are definitely a trained and practicing physicist.

500
00:25:20,760 --> 00:25:22,919
Speaker 2: So tell me what does it mean to you to

501
00:25:23,200 --> 00:25:26,679
Speaker 2: think like a physicist? Can you remember learning how to

502
00:25:26,760 --> 00:25:28,920
Speaker 2: do that? Can you compare the way you think now

503
00:25:28,960 --> 00:25:31,479
Speaker 2: to the way you thought before you went to grad school?

504
00:25:31,600 --> 00:25:33,600
Speaker 2: What does it mean to think like a physicist?

505
00:25:34,960 --> 00:25:38,800
Speaker 5: I would definitely say it was not something that one

506
00:25:38,920 --> 00:25:42,200
Speaker 5: learns in one day. It's more of a practice that

507
00:25:43,600 --> 00:25:47,560
Speaker 5: you learn over many years. And I would say that

508
00:25:48,240 --> 00:25:54,320
Speaker 5: a large part of thinking like a physicist is knowing

509
00:25:54,359 --> 00:25:59,080
Speaker 5: how to draw conclusions from the universe and observations that

510
00:25:59,119 --> 00:26:02,959
Speaker 5: we make of it, but also always keeping in mind

511
00:26:03,200 --> 00:26:07,960
Speaker 5: how uncertain those conclusions that we draw from our observations

512
00:26:07,960 --> 00:26:08,760
Speaker 5: can possibly be.

513
00:26:09,920 --> 00:26:12,320
Speaker 2: What do you mean uncertain, Like we have a hunch

514
00:26:12,359 --> 00:26:14,480
Speaker 2: and we're not sure. H we don't have enough information,

515
00:26:14,800 --> 00:26:17,120
Speaker 2: or we could be confused. What do you mean by.

516
00:26:17,119 --> 00:26:23,240
Speaker 5: Uncertain Well, basically, we draw conclusions about the universe from

517
00:26:23,600 --> 00:26:28,119
Speaker 5: making observations and making measurements. So let's say that we

518
00:26:28,200 --> 00:26:31,560
Speaker 5: have some amazing new idea that someone has come up with,

519
00:26:31,680 --> 00:26:36,160
Speaker 5: but it hasn't been tested. It will make predictions about

520
00:26:36,359 --> 00:26:40,160
Speaker 5: the universe, and oftentimes these are predictions about the values

521
00:26:40,200 --> 00:26:43,160
Speaker 5: of certain quantities that we can measure, like the lifetime

522
00:26:43,160 --> 00:26:46,440
Speaker 5: of a particle or the rate of a certain process

523
00:26:46,480 --> 00:26:52,120
Speaker 5: that happens at the large Hadron collider. And we want

524
00:26:52,160 --> 00:26:55,560
Speaker 5: to test this new amazing hypothesis, so we go and

525
00:26:55,600 --> 00:26:56,879
Speaker 5: measure those quantities.

526
00:26:57,840 --> 00:26:59,159
Speaker 9: And when we measure those.

527
00:26:59,080 --> 00:27:05,840
Speaker 5: Quantities, we use experimental apparatuses and techniques, but it's not

528
00:27:06,000 --> 00:27:10,560
Speaker 5: possible to ever have a perfect experiment. Whenever you get

529
00:27:10,640 --> 00:27:12,760
Speaker 5: a measured value of a quantity, it's always going to

530
00:27:12,800 --> 00:27:15,200
Speaker 5: differ at least a little bit from the true value

531
00:27:15,240 --> 00:27:16,959
Speaker 5: of the quantity that you're trying to measure. So if

532
00:27:16,960 --> 00:27:19,879
Speaker 5: you try to measure the electron mass, you will get

533
00:27:20,240 --> 00:27:22,280
Speaker 5: a measured value of the electron mass, but it's not

534
00:27:22,320 --> 00:27:24,800
Speaker 5: going to be exactly the true value of the electron mass.

535
00:27:25,040 --> 00:27:27,000
Speaker 2: So let's make a little bit more concrete instead of

536
00:27:27,040 --> 00:27:29,760
Speaker 2: thinking about particle physics. Let's say somebody gives me a

537
00:27:29,800 --> 00:27:32,840
Speaker 2: coin and I have a theory that this coin is

538
00:27:32,880 --> 00:27:36,199
Speaker 2: not fair, that it's going to favor heads right sixty

539
00:27:36,240 --> 00:27:38,760
Speaker 2: six percent or something, and then I can do an

540
00:27:38,760 --> 00:27:41,240
Speaker 2: experiment to see, well, is it a fair coin by

541
00:27:41,240 --> 00:27:44,360
Speaker 2: flipping it right five hundred times. So I think you're

542
00:27:44,400 --> 00:27:47,520
Speaker 2: saying that there's uncertainty because even if I flip it

543
00:27:47,600 --> 00:27:51,160
Speaker 2: a thousand times, I'm never going to know precisely what

544
00:27:51,240 --> 00:27:54,320
Speaker 2: the real probability is because I'm not flipping an infinite

545
00:27:54,400 --> 00:27:56,760
Speaker 2: number of times. There's always some randomness. Is that what

546
00:27:56,800 --> 00:28:02,359
Speaker 2: you're saying exactly? Okay, So there's uncertainty in our measurements

547
00:28:02,480 --> 00:28:05,360
Speaker 2: because we don't take infinitely long experiments and we don't

548
00:28:05,359 --> 00:28:08,240
Speaker 2: have infinite amounts of data. What are some other ways

549
00:28:08,240 --> 00:28:11,480
Speaker 2: that we can be wrong or uncertain about our conclusions.

550
00:28:12,920 --> 00:28:16,080
Speaker 5: Well, there are lots of ways that error can sneak

551
00:28:16,119 --> 00:28:22,880
Speaker 5: into measurements. For example, we make measurements using some kind

552
00:28:22,880 --> 00:28:25,040
Speaker 5: of experimental measurement apparatus.

553
00:28:25,280 --> 00:28:27,520
Speaker 9: So, for example, let's say if.

554
00:28:27,359 --> 00:28:31,239
Speaker 5: We're trying to measure any quantity, we're using some kind

555
00:28:31,280 --> 00:28:35,960
Speaker 5: of experimental apparatus to do it, and that apparatus is

556
00:28:36,000 --> 00:28:39,040
Speaker 5: going to have a finite resolution of some kind. So

557
00:28:39,080 --> 00:28:43,320
Speaker 5: for example, let's say you're trying to measure the size of.

558
00:28:43,120 --> 00:28:46,480
Speaker 9: An object in a room. You use a ruler, and

559
00:28:46,760 --> 00:28:47,640
Speaker 9: that ruler.

560
00:28:47,360 --> 00:28:51,040
Speaker 5: Has a finite gradation on it. You can't see down

561
00:28:51,080 --> 00:28:55,400
Speaker 5: to the micron size using a ruler, so there's automatically

562
00:28:55,440 --> 00:28:58,440
Speaker 5: some level of uncertainty that's going to come in because

563
00:28:58,440 --> 00:29:05,040
Speaker 5: of effects like that. You may also for very complicated measurements, like,

564
00:29:05,640 --> 00:29:07,920
Speaker 5: for example, if you're trying to measure a cross section

565
00:29:08,000 --> 00:29:13,320
Speaker 5: at the large hadron collider, you have very complicated measuring

566
00:29:13,520 --> 00:29:18,160
Speaker 5: devices and you have to simulate various parts of the

567
00:29:18,320 --> 00:29:20,360
Speaker 5: not only the physics that you're trying to understand, but

568
00:29:20,440 --> 00:29:26,000
Speaker 5: the device, and those simulations will never match up exactly

569
00:29:26,080 --> 00:29:27,000
Speaker 5: well with reality.

570
00:29:27,400 --> 00:29:29,640
Speaker 2: So I think what you're saying is that sometimes to

571
00:29:29,720 --> 00:29:32,440
Speaker 2: do these experiments, we have to use devices we don't

572
00:29:32,440 --> 00:29:35,840
Speaker 2: even really understand exactly how they work. Like if I'm

573
00:29:35,880 --> 00:29:39,040
Speaker 2: measuring an electron the lartadron collider, and I have some

574
00:29:39,240 --> 00:29:42,800
Speaker 2: device to measure an electrons energy, it's complicated to measure

575
00:29:42,800 --> 00:29:45,120
Speaker 2: an electrons energy, and I don't exactly know what happens

576
00:29:45,120 --> 00:29:47,600
Speaker 2: when an electron slams into a block of copper and

577
00:29:47,680 --> 00:29:51,280
Speaker 2: creates a huge shower of other particles. It's complicated physics,

578
00:29:51,320 --> 00:29:53,960
Speaker 2: and I could be wrong about what's going on in

579
00:29:53,960 --> 00:29:57,520
Speaker 2: my own experimental device that I built and designed. Right.

580
00:29:57,600 --> 00:30:00,680
Speaker 5: Yes, in fact, we don't entirely understan stand our own

581
00:30:00,720 --> 00:30:04,520
Speaker 5: measuring devices perfectly, so we have to model them and

582
00:30:04,560 --> 00:30:08,600
Speaker 5: simulate them, and sometimes compare those simulations to data in

583
00:30:08,680 --> 00:30:12,200
Speaker 5: order to current to improve those simulations and get a

584
00:30:12,240 --> 00:30:14,720
Speaker 5: better measurement of whatever it is we're trying to measure.

585
00:30:15,320 --> 00:30:17,960
Speaker 2: Right, So, like back to the coin example. You know

586
00:30:18,040 --> 00:30:19,840
Speaker 2: it's easy to look at a coin and say, oh

587
00:30:19,840 --> 00:30:22,680
Speaker 2: it's heads or oh it's tails, But say it was harder, right,

588
00:30:22,720 --> 00:30:25,160
Speaker 2: Say I couldn't just look at the coin. I needed

589
00:30:25,160 --> 00:30:27,600
Speaker 2: to have some little device that told me if it

590
00:30:27,600 --> 00:30:29,600
Speaker 2: was heads or tails, and that device I didn't really

591
00:30:29,640 --> 00:30:31,920
Speaker 2: know how it worked, and I wasn't always sure it

592
00:30:31,960 --> 00:30:35,560
Speaker 2: was correct. That would lead some like uncertainty into my measurement, right,

593
00:30:35,600 --> 00:30:38,560
Speaker 2: because it could be wrong, or I could think that

594
00:30:38,640 --> 00:30:41,240
Speaker 2: it's correct, but it's it's incorrect in some other ways.

595
00:30:41,600 --> 00:30:44,480
Speaker 5: Yes, and it might be using some pattern recognition software

596
00:30:44,520 --> 00:30:48,280
Speaker 5: that doesn't handle like certain light levels very well or

597
00:30:48,280 --> 00:30:48,960
Speaker 5: something like that.

598
00:30:49,040 --> 00:30:50,920
Speaker 9: So, yeah, it could make a mistake every.

599
00:30:50,720 --> 00:30:52,400
Speaker 5: Once in a while until you you've got heads, when

600
00:30:52,440 --> 00:30:54,000
Speaker 5: you've got tails or vice versa.

601
00:30:54,160 --> 00:30:57,200
Speaker 2: Yeah, And so in physics, we're very quantitative about this, right,

602
00:30:57,400 --> 00:30:59,880
Speaker 2: We're very specific when we measure something. We say, oh,

603
00:31:00,120 --> 00:31:02,800
Speaker 2: is a two percent chance we've been wrong, or a

604
00:31:02,920 --> 00:31:05,720
Speaker 2: zero point zer zer zer one percent chance we're wrong.

605
00:31:06,080 --> 00:31:08,480
Speaker 2: Why are we such sticklers about this in physics? Why

606
00:31:08,480 --> 00:31:12,480
Speaker 2: are we such nerds about measuring precisely how wrong we

607
00:31:12,560 --> 00:31:13,920
Speaker 2: might be in physics? What do you think?

608
00:31:14,960 --> 00:31:15,240
Speaker 9: Well?

609
00:31:15,320 --> 00:31:18,960
Speaker 5: I think that part of it is that physics was

610
00:31:19,000 --> 00:31:21,760
Speaker 5: one of the first fields to do a lot of measurements.

611
00:31:23,120 --> 00:31:26,720
Speaker 5: So if you're only doing ten measurements and you think

612
00:31:27,520 --> 00:31:31,280
Speaker 5: you'll screw up like one out of a thousand, you're

613
00:31:31,320 --> 00:31:34,680
Speaker 5: probably not too worried that you're that you're going to

614
00:31:36,040 --> 00:31:38,920
Speaker 5: produce a wrong result, or produce a result that had

615
00:31:39,000 --> 00:31:42,320
Speaker 5: a large statistical fluctuation where you didn't you didn't screw

616
00:31:42,360 --> 00:31:46,200
Speaker 5: up anything, and you're you're apparatus performed exactly correctly. But

617
00:31:46,320 --> 00:31:49,560
Speaker 5: nonetheless you've got very unlucky if you think that that

618
00:31:49,640 --> 00:31:52,920
Speaker 5: probability is small and you're only making like ten measurements,

619
00:31:53,000 --> 00:31:55,160
Speaker 5: you're not too worried that you're going to publish a

620
00:31:55,240 --> 00:31:57,400
Speaker 5: result that's going to lead people down a wrong path.

621
00:31:58,320 --> 00:32:03,760
Speaker 5: But in particle physics, we make thousands of measurements, most

622
00:32:03,760 --> 00:32:05,800
Speaker 5: of which you never hear about in the news because

623
00:32:05,840 --> 00:32:07,560
Speaker 5: unfortunately most of them agree.

624
00:32:07,240 --> 00:32:08,160
Speaker 9: With the standard model.

625
00:32:09,240 --> 00:32:11,520
Speaker 5: But because we make so many, there's going to be

626
00:32:11,600 --> 00:32:17,920
Speaker 5: some just out of statistical fluctuations that happen to appear

627
00:32:18,040 --> 00:32:21,520
Speaker 5: to disagree a lot with what we expect, and so

628
00:32:22,200 --> 00:32:26,960
Speaker 5: it's very important to have a very strict criterion for

629
00:32:27,960 --> 00:32:31,520
Speaker 5: deciding when something disagrees with what we expect so much

630
00:32:31,680 --> 00:32:33,040
Speaker 5: that it must be interesting.

631
00:32:34,320 --> 00:32:36,120
Speaker 2: Yeah, I think that's probably true. Do you think it's

632
00:32:36,160 --> 00:32:39,800
Speaker 2: also because some of the things we're probing are sort

633
00:32:39,800 --> 00:32:43,200
Speaker 2: of invisible, so that our measurements are always going to

634
00:32:43,240 --> 00:32:46,000
Speaker 2: be indirect, you know, like if somebody discovers a new

635
00:32:46,040 --> 00:32:49,160
Speaker 2: kind of turtle in biology, they're like, here's the turtle.

636
00:32:49,240 --> 00:32:51,000
Speaker 2: Like I can show you, look, this is a turtle.

637
00:32:51,040 --> 00:32:53,760
Speaker 2: Like nobody's confused about whether it's a turtle. But if

638
00:32:53,760 --> 00:32:55,920
Speaker 2: we're saying, hey, I discovered the squigglyon, it's not like

639
00:32:55,960 --> 00:32:58,080
Speaker 2: I can say, I've got a pile of squiglyons. Here

640
00:32:58,080 --> 00:33:00,640
Speaker 2: they are. That's all play with them to show you data,

641
00:33:00,760 --> 00:33:03,040
Speaker 2: and the data has statistics, and we have to make inference,

642
00:33:03,040 --> 00:33:06,280
Speaker 2: and so it's always frustratingly indirect. And I wonder if

643
00:33:06,320 --> 00:33:09,280
Speaker 2: that's one reason why we have to be such nerds

644
00:33:09,320 --> 00:33:12,000
Speaker 2: about whether or not we've been confused, because there's so

645
00:33:12,040 --> 00:33:15,560
Speaker 2: many different steps between the physical reality and the actual

646
00:33:15,560 --> 00:33:16,480
Speaker 2: measurements we make.

647
00:33:16,960 --> 00:33:21,000
Speaker 5: Yeah, it's also the case that in particle physics we're

648
00:33:21,080 --> 00:33:25,640
Speaker 5: also dealing with looking for processes in colliders that can

649
00:33:25,720 --> 00:33:28,440
Speaker 5: look a lot like other processes that we aren't actually

650
00:33:28,480 --> 00:33:29,200
Speaker 5: interested in.

651
00:33:29,600 --> 00:33:31,840
Speaker 9: So it's not so much like we.

652
00:33:31,720 --> 00:33:33,440
Speaker 5: Go out into the world and we find a new

653
00:33:33,480 --> 00:33:35,400
Speaker 5: turtle and we bring it back and show people and

654
00:33:35,440 --> 00:33:38,200
Speaker 5: say this is a new turtle. It's more like we

655
00:33:38,240 --> 00:33:40,080
Speaker 5: go out into the world, and we find a new

656
00:33:40,120 --> 00:33:43,360
Speaker 5: turtle that looks very, very similar to a lot of

657
00:33:43,400 --> 00:33:47,200
Speaker 5: other turtles, and we bring that turtle and another thirty

658
00:33:47,240 --> 00:33:51,960
Speaker 5: turtles back, and we show the collection of turtles to

659
00:33:52,040 --> 00:33:55,720
Speaker 5: our colleagues, and we have to convince them that that

660
00:33:55,800 --> 00:33:57,200
Speaker 5: one turtle really is special.

661
00:33:58,880 --> 00:34:01,600
Speaker 2: It's not just the same turtle all the way down. Yeah, exactly.

662
00:34:02,040 --> 00:34:04,320
Speaker 2: And then we do experiments with those turtles, flipping them

663
00:34:04,320 --> 00:34:07,360
Speaker 2: to see if they're fair coins in that. So this

664
00:34:07,480 --> 00:34:10,080
Speaker 2: is the way that physicists think about things. We're really

665
00:34:10,120 --> 00:34:12,719
Speaker 2: focused on what we've measured, how well we know it,

666
00:34:12,880 --> 00:34:17,439
Speaker 2: quantifying that uncertainty different ways we can be wrong when

667
00:34:17,480 --> 00:34:20,800
Speaker 2: we communicate our results to the public. This is a challenge,

668
00:34:20,880 --> 00:34:23,800
Speaker 2: right to express to them here's what we think, but

669
00:34:23,880 --> 00:34:26,960
Speaker 2: here's how much wrong we might be. What do you

670
00:34:27,000 --> 00:34:30,080
Speaker 2: think are the usual stumbling blocks for people who haven't

671
00:34:30,080 --> 00:34:33,000
Speaker 2: spent their lives learning to think like a physicist for

672
00:34:33,200 --> 00:34:36,840
Speaker 2: understanding uncertainties and what we mean by uncertainties when we

673
00:34:36,880 --> 00:34:37,560
Speaker 2: talk about them.

674
00:34:38,000 --> 00:34:42,319
Speaker 5: Well, I think one problem is that most of the

675
00:34:42,360 --> 00:34:45,680
Speaker 5: time in real life, when we're talking about needing to

676
00:34:45,760 --> 00:34:48,800
Speaker 5: know the value of some quantity, we.

677
00:34:48,640 --> 00:34:51,359
Speaker 2: Were not hold on, are you contrasting physics with real

678
00:34:51,400 --> 00:34:54,520
Speaker 2: life is that what you just did here, are you

679
00:34:54,520 --> 00:34:55,920
Speaker 2: saying physics is not real for me?

680
00:34:56,040 --> 00:34:56,799
Speaker 9: The same thing?

681
00:35:01,080 --> 00:35:04,080
Speaker 5: But in the ordinary life, where we go outside and

682
00:35:06,200 --> 00:35:09,480
Speaker 5: you know, do things where we're not looking at a

683
00:35:09,480 --> 00:35:16,200
Speaker 5: computer screen, we do get values for various quantities. Like

684
00:35:16,760 --> 00:35:18,839
Speaker 5: if we're driving our car, we do look at our

685
00:35:18,880 --> 00:35:23,840
Speaker 5: speedometer hopefully and see what speed we're getting. And generally

686
00:35:23,960 --> 00:35:30,640
Speaker 5: the outside world isn't very it's not used to giving

687
00:35:30,680 --> 00:35:32,840
Speaker 5: us uncertainties on the numbers that we get. So we

688
00:35:32,840 --> 00:35:34,879
Speaker 5: look at that speedometer and it tells us we're going

689
00:35:35,160 --> 00:35:37,319
Speaker 5: fifty seven miles an hour, but it doesn't put an

690
00:35:37,400 --> 00:35:41,440
Speaker 5: error bar on it. And also when we're learning things

691
00:35:41,560 --> 00:35:45,000
Speaker 5: about either physics or anything else.

692
00:35:44,840 --> 00:35:48,319
Speaker 9: In our in our education, at least in.

693
00:35:48,280 --> 00:35:51,960
Speaker 5: Our earlier education, usually the idea is, here are the

694
00:35:52,000 --> 00:35:53,479
Speaker 5: principles that we work from.

695
00:35:53,600 --> 00:35:55,080
Speaker 9: What can we figure out from it?

696
00:35:55,600 --> 00:35:58,080
Speaker 5: But we don't actually stop and think, well, what are

697
00:35:58,239 --> 00:36:01,320
Speaker 5: the experimental results that led to us having those principles,

698
00:36:01,320 --> 00:36:04,799
Speaker 5: and what were the errors on those principles, what were

699
00:36:04,800 --> 00:36:08,400
Speaker 5: the uncertainties on those principles? And you know, how well

700
00:36:08,520 --> 00:36:11,799
Speaker 5: does that principle work with the situation I'm trying to

701
00:36:12,719 --> 00:36:15,440
Speaker 5: trying to study at the moment. Am I actually using

702
00:36:15,520 --> 00:36:20,000
Speaker 5: the right the right set of scientific principles for the

703
00:36:20,040 --> 00:36:23,839
Speaker 5: situation at hand, or am I introducing some uncertainties that maybe.

704
00:36:25,520 --> 00:36:27,720
Speaker 9: Maybe I need to think about.

705
00:36:28,680 --> 00:36:31,839
Speaker 5: So I would say that the main stumbling block is

706
00:36:32,800 --> 00:36:37,919
Speaker 5: that we just aren't exposed to it. It's it's hard

707
00:36:37,960 --> 00:36:38,439
Speaker 5: to come by.

708
00:36:39,080 --> 00:36:41,799
Speaker 2: Yeah, I see. So maybe when you get pulled over,

709
00:36:42,040 --> 00:36:44,440
Speaker 2: you can tell the officer like, look, it said it

710
00:36:44,520 --> 00:36:46,480
Speaker 2: was I was doing sixty. I don't know why your

711
00:36:46,600 --> 00:36:48,799
Speaker 2: machine says I was doing eighty five. Maybe there's some

712
00:36:48,840 --> 00:36:51,400
Speaker 2: mistakes somewhere, right, Sometimes we have a little bit of

713
00:36:51,440 --> 00:36:54,560
Speaker 2: intuitive grasp of like maybe there's fuzz in the numbers.

714
00:36:55,120 --> 00:36:58,120
Speaker 2: But you're right, we're rarely like measuring the uncertainties in

715
00:36:58,480 --> 00:37:01,400
Speaker 2: quote unquote real life. So for people who are not

716
00:37:01,600 --> 00:37:04,239
Speaker 2: trained like a physicist and don't nerd out about statistics

717
00:37:04,239 --> 00:37:06,480
Speaker 2: all the time, well, it's a sort of intuitive or

718
00:37:06,480 --> 00:37:10,120
Speaker 2: easy way to start to think about these uncertainties. What

719
00:37:10,160 --> 00:37:12,640
Speaker 2: do you recommend? I know you have a wonderful YouTube

720
00:37:12,680 --> 00:37:14,759
Speaker 2: channel where you teach people to think like a physicist

721
00:37:14,760 --> 00:37:18,200
Speaker 2: and think about uncertainties. How should people get started thinking

722
00:37:18,239 --> 00:37:20,040
Speaker 2: about uncertainties like a physicist.

723
00:37:20,280 --> 00:37:22,279
Speaker 5: Well, if you want to think about it the way

724
00:37:22,400 --> 00:37:26,759
Speaker 5: physicists do, I guess I would explain how physicists arrive

725
00:37:26,840 --> 00:37:29,680
Speaker 5: at those uncertainties. So, like a physicist who is conducting

726
00:37:29,719 --> 00:37:33,239
Speaker 5: some kind of an experiment, they are going to want

727
00:37:33,280 --> 00:37:35,120
Speaker 5: to produce a result, and they're going to want to

728
00:37:35,160 --> 00:37:37,440
Speaker 5: produce an error bar that goes with that result.

729
00:37:37,440 --> 00:37:39,799
Speaker 9: That tells you and what the uncertainty on that result is.

730
00:37:40,200 --> 00:37:42,480
Speaker 2: Let's stop there, firm and describe exactly what you mean.

731
00:37:42,480 --> 00:37:45,640
Speaker 2: They're like the error bar. So if I say I've

732
00:37:45,800 --> 00:37:48,719
Speaker 2: measured my speed to be seventy miles an hour with

733
00:37:48,760 --> 00:37:51,480
Speaker 2: an air bar of five, what does that mean? What

734
00:37:51,520 --> 00:37:53,279
Speaker 2: does the err bar mean? What am I saying when

735
00:37:53,320 --> 00:37:53,879
Speaker 2: I say five?

736
00:37:54,200 --> 00:37:58,640
Speaker 5: So the error bar, if you're at least thinking about

737
00:37:58,640 --> 00:38:01,879
Speaker 5: it from a physicist point of view, is you've thought

738
00:38:01,920 --> 00:38:06,080
Speaker 5: about what the possible sources of error that can come

739
00:38:06,120 --> 00:38:08,800
Speaker 5: in the ways that you could be wrong, the ways

740
00:38:08,800 --> 00:38:12,560
Speaker 5: that you could measure it incorrectly, and you've done some

741
00:38:12,680 --> 00:38:16,200
Speaker 5: kind of analysis or thinking about it to add those

742
00:38:16,239 --> 00:38:21,319
Speaker 5: sources together and figure out roughly typically how much you

743
00:38:21,400 --> 00:38:22,640
Speaker 5: would be wrong by.

744
00:38:23,320 --> 00:38:26,280
Speaker 2: So does that mean that if I measure my speed

745
00:38:26,280 --> 00:38:28,719
Speaker 2: to be seventy plus or minus five, that the true

746
00:38:28,800 --> 00:38:33,160
Speaker 2: speed is definitely within sixty five to seventy five, Like,

747
00:38:33,200 --> 00:38:36,279
Speaker 2: does the error bar completely define the possible extent of

748
00:38:36,320 --> 00:38:36,760
Speaker 2: the truth.

749
00:38:37,080 --> 00:38:42,720
Speaker 5: Absolutely not. It's a typical value. It's a typical value

750
00:38:42,760 --> 00:38:45,640
Speaker 5: for the difference between the true value of something and

751
00:38:45,680 --> 00:38:50,400
Speaker 5: the value that we measure. And we don't know whether

752
00:38:50,480 --> 00:38:53,239
Speaker 5: the value we measure is above the true value or

753
00:38:53,239 --> 00:38:56,200
Speaker 5: below it. And we don't know if the difference between

754
00:38:56,200 --> 00:38:59,080
Speaker 5: the true value and our measured value is larger than

755
00:38:59,120 --> 00:39:00,799
Speaker 5: that error bar or smaller.

756
00:39:00,440 --> 00:39:01,080
Speaker 9: Than that error bar.

757
00:39:01,160 --> 00:39:04,600
Speaker 5: And an instance of a specific measurement, what that error

758
00:39:04,640 --> 00:39:08,320
Speaker 5: bar means is that's a typical value for how the

759
00:39:08,360 --> 00:39:11,360
Speaker 5: true value in the measured value would disagree.

760
00:39:12,080 --> 00:39:15,520
Speaker 2: Right, And so if we quote seventy plus or minus five,

761
00:39:15,640 --> 00:39:18,800
Speaker 2: or let's talk about you know, politics, Joe Biden's pulling

762
00:39:18,880 --> 00:39:24,319
Speaker 2: numbers are forty four percent with a uncertainty of three percent. Right,

763
00:39:24,560 --> 00:39:29,279
Speaker 2: that doesn't mean that his true value is between you know,

764
00:39:29,320 --> 00:39:32,520
Speaker 2: forty four plus three and forty four minus three. It

765
00:39:32,680 --> 00:39:35,920
Speaker 2: means that there's a sixty percent chance that it is.

766
00:39:36,440 --> 00:39:38,719
Speaker 2: And then therefore there's a thirty two percent chance that

767
00:39:38,800 --> 00:39:42,279
Speaker 2: it isn't right. So the airbar tells us, as you say,

768
00:39:42,800 --> 00:39:46,560
Speaker 2: roughly the size of the expected difference between the truths

769
00:39:46,560 --> 00:39:48,920
Speaker 2: and the measured value. But it doesn't bound it right.

770
00:39:48,960 --> 00:39:51,759
Speaker 2: It doesn't tell us it's exactly within that. I see

771
00:39:51,760 --> 00:39:54,920
Speaker 2: this sort of misunderstanding all the time in political journalism.

772
00:39:55,400 --> 00:39:58,000
Speaker 2: You know, where they have two candidates and if they're

773
00:39:58,000 --> 00:40:02,000
Speaker 2: separated by ten points and the uncertainty is four points,

774
00:40:02,280 --> 00:40:04,520
Speaker 2: then they say, Okay, it's definitely a lead, but you know,

775
00:40:04,600 --> 00:40:07,719
Speaker 2: it still could be the opposite, or two candidates who

776
00:40:07,760 --> 00:40:11,160
Speaker 2: are near each other but within the statistical uncertainty, they

777
00:40:11,160 --> 00:40:13,279
Speaker 2: call it a tie, even though if one of them

778
00:40:13,320 --> 00:40:15,520
Speaker 2: has a larger value, we're pretty sure that you know,

779
00:40:15,560 --> 00:40:18,640
Speaker 2: we're somewhat sure at least that they have more support.

780
00:40:19,080 --> 00:40:21,319
Speaker 2: I think there's a lot of misunderstanding about what this

781
00:40:21,480 --> 00:40:24,600
Speaker 2: error bar means. It seems so much more definitive right

782
00:40:24,680 --> 00:40:26,359
Speaker 2: than the way that we meet it. It's really, as

783
00:40:26,360 --> 00:40:29,440
Speaker 2: you say, just a typical value. It tells you roughly

784
00:40:29,480 --> 00:40:33,239
Speaker 2: the scale of how far off you might be. So

785
00:40:33,280 --> 00:40:36,239
Speaker 2: when people are out there reading a scientific result, right

786
00:40:36,239 --> 00:40:39,239
Speaker 2: when they're not measuring their speedometer, when they're reading a

787
00:40:39,280 --> 00:40:42,000
Speaker 2: paper about a new particle and they come across something,

788
00:40:42,520 --> 00:40:45,560
Speaker 2: what should they be asking themselves? They should what should

789
00:40:45,560 --> 00:40:48,280
Speaker 2: they be looking for in that article to help understand

790
00:40:48,480 --> 00:40:52,080
Speaker 2: how uncertain are physicists about this new squeakly unparticle.

791
00:40:52,360 --> 00:40:56,000
Speaker 5: Well, I mean, at the most basic level, if the

792
00:40:56,040 --> 00:41:00,000
Speaker 5: result is measuring something and saying this value was large

793
00:41:00,280 --> 00:41:02,600
Speaker 5: than what we were expecting from our prediction if the

794
00:41:02,600 --> 00:41:06,560
Speaker 5: particle didn't exist, the first question is to ask, well,

795
00:41:06,840 --> 00:41:09,319
Speaker 5: what was the difference between what was observed and what

796
00:41:09,400 --> 00:41:12,400
Speaker 5: was expected if the particle didn't exist, And then how

797
00:41:12,440 --> 00:41:16,000
Speaker 5: does that difference compare to the quoted uncertainty.

798
00:41:16,800 --> 00:41:20,560
Speaker 9: So if that difference is a lot larger.

799
00:41:20,160 --> 00:41:22,839
Speaker 5: Than the quoted uncertainty, then we would tend to think

800
00:41:22,840 --> 00:41:25,520
Speaker 5: that something interesting is going on. You know, maybe it's

801
00:41:25,560 --> 00:41:29,400
Speaker 5: particle discovery. Hopefully it's particle discovery, but it always could

802
00:41:29,440 --> 00:41:31,880
Speaker 5: be that something has gone wrong with the experiment that

803
00:41:31,920 --> 00:41:35,920
Speaker 5: we don't understand. On the other hand, if the difference

804
00:41:35,960 --> 00:41:40,439
Speaker 5: between what's observed and what's expected from the no new

805
00:41:40,480 --> 00:41:44,799
Speaker 5: particle hypothesis, if that difference is not much larger than

806
00:41:44,800 --> 00:41:47,520
Speaker 5: the uncertainty, or maybe it's only a couple times the uncertainty,

807
00:41:48,040 --> 00:41:51,440
Speaker 5: then it's probably a little bit too early to get excited.

808
00:41:51,880 --> 00:41:54,120
Speaker 5: We need more data and we need more results and

809
00:41:54,160 --> 00:41:57,399
Speaker 5: possibly more experiments to look at it before we say

810
00:41:57,440 --> 00:41:58,280
Speaker 5: anything definitive.

811
00:41:59,120 --> 00:42:01,399
Speaker 2: Right, then make a concrete and go back to our

812
00:42:01,840 --> 00:42:03,920
Speaker 2: coin that we're tossing, or the turtle that we're flipping.

813
00:42:04,360 --> 00:42:07,440
Speaker 2: Let's say I flip the coin two times and I

814
00:42:07,480 --> 00:42:11,120
Speaker 2: get two heads, so it's one hundred percent heads, right,

815
00:42:11,840 --> 00:42:14,239
Speaker 2: And then I go off and I write a paper saying, look,

816
00:42:14,360 --> 00:42:17,080
Speaker 2: my coin is one hundred percent heads. It's totally unfair.

817
00:42:17,080 --> 00:42:19,919
Speaker 2: And you're the reviewer. You might look and say, all right,

818
00:42:20,000 --> 00:42:22,239
Speaker 2: you know, but the prediction for a fair coin is

819
00:42:22,280 --> 00:42:24,719
Speaker 2: fifty percent, and the prediction for an unfair coin is

820
00:42:25,040 --> 00:42:27,799
Speaker 2: you know, something above that. But the uncertainty on your

821
00:42:27,840 --> 00:42:30,480
Speaker 2: measurement is huge because you only flipped it twice, right,

822
00:42:30,560 --> 00:42:32,960
Speaker 2: So yes, you measured one hundred percent heads, but you

823
00:42:32,960 --> 00:42:35,120
Speaker 2: could have also gotten fifty percent heads or seventy five

824
00:42:35,120 --> 00:42:38,360
Speaker 2: percent heads or whatever. And so you're saying if I

825
00:42:38,400 --> 00:42:41,200
Speaker 2: go back and then flip it a million times and

826
00:42:41,239 --> 00:42:44,879
Speaker 2: I still get a million heads, that that's very different, right,

827
00:42:44,920 --> 00:42:47,520
Speaker 2: And I think people can understand that that's much more compelling.

828
00:42:47,840 --> 00:42:49,440
Speaker 2: If you get a million heads in a row, it's

829
00:42:49,560 --> 00:42:52,840
Speaker 2: very unlikely to be a fair coin. And that's the difference,

830
00:42:52,920 --> 00:42:56,160
Speaker 2: right that there's a smaller uncertainty on my measurement of

831
00:42:56,160 --> 00:42:58,480
Speaker 2: one hundred percent heads if I flip it a million

832
00:42:58,520 --> 00:43:01,719
Speaker 2: times and if I flip it two times times, And

833
00:43:01,760 --> 00:43:04,520
Speaker 2: so the two different hypotheses of like a fair coin

834
00:43:04,760 --> 00:43:06,879
Speaker 2: fifty percent heads and an unfair coin and one hundred

835
00:43:06,880 --> 00:43:10,279
Speaker 2: percent heads. The difference there is now large compared to

836
00:43:10,320 --> 00:43:13,120
Speaker 2: the uncertainty, whereas it was small when I only flipped

837
00:43:13,120 --> 00:43:13,520
Speaker 2: it twice.

838
00:43:13,960 --> 00:43:16,319
Speaker 5: Yeah, when you only flip it twice, I mean, even

839
00:43:16,400 --> 00:43:19,040
Speaker 5: if the coin is fair, the probability is twenty five

840
00:43:19,040 --> 00:43:21,160
Speaker 5: percent it's going to come up heads both times. So

841
00:43:22,400 --> 00:43:24,600
Speaker 5: it's it's important to not jump the gun and think

842
00:43:24,600 --> 00:43:26,840
Speaker 5: that you've discovered something amazing when you might.

843
00:43:26,760 --> 00:43:30,080
Speaker 9: Just have a quarter exactly.

844
00:43:30,200 --> 00:43:32,080
Speaker 5: On the other hand, if you flip the coin ten

845
00:43:32,120 --> 00:43:35,160
Speaker 5: times and it comes up heads each time, well then

846
00:43:35,320 --> 00:43:39,040
Speaker 5: you know you start to think maybe something's up. And

847
00:43:39,120 --> 00:43:41,960
Speaker 5: if you do it twenty times, then you might start

848
00:43:42,000 --> 00:43:44,759
Speaker 5: to really think that's something up. And certainly, if you

849
00:43:44,840 --> 00:43:47,439
Speaker 5: flip it a million times, then you're pretty darn certain

850
00:43:47,600 --> 00:43:48,200
Speaker 5: something's else.

851
00:43:49,840 --> 00:43:54,800
Speaker 2: Exactly, but I think it's fascinating that even now, for example,

852
00:43:55,120 --> 00:43:58,760
Speaker 2: we can't say one hundred percent definitively that the Higgs

853
00:43:58,800 --> 00:44:01,880
Speaker 2: boson exists. Like we've taken so much data, we have

854
00:44:02,120 --> 00:44:05,880
Speaker 2: so much evidence, and yet still it could all be

855
00:44:05,920 --> 00:44:08,719
Speaker 2: a fluctuation, right, It could all just be We could

856
00:44:08,760 --> 00:44:11,360
Speaker 2: be that situation where we flipped a fair coin a

857
00:44:11,400 --> 00:44:13,719
Speaker 2: million times and gotten a million heads in a row.

858
00:44:13,800 --> 00:44:16,560
Speaker 2: It can happen, and we could have been fooled by

859
00:44:16,560 --> 00:44:18,320
Speaker 2: our data. We don't have like a pile of Higgs

860
00:44:18,360 --> 00:44:20,800
Speaker 2: bosons we can point to and say these are them, folks.

861
00:44:21,200 --> 00:44:23,840
Speaker 2: We just have, you know, basically the result of flipping

862
00:44:23,840 --> 00:44:26,600
Speaker 2: a bunch of coins and seeing it come out weird

863
00:44:27,400 --> 00:44:30,279
Speaker 2: compared to our prediction for no Higgs boson. So in

864
00:44:30,320 --> 00:44:33,360
Speaker 2: principle we know we don't really know that any particle

865
00:44:33,440 --> 00:44:35,840
Speaker 2: is out there, though I guess as we continue to

866
00:44:36,120 --> 00:44:39,080
Speaker 2: make collisions and analyze data we get more and more certain.

867
00:44:39,320 --> 00:44:41,080
Speaker 2: But it's sort of like approaching the speed of light, right,

868
00:44:41,120 --> 00:44:42,640
Speaker 2: you can never actually get there.

869
00:44:42,800 --> 00:44:43,359
Speaker 9: That's right.

870
00:44:43,840 --> 00:44:47,560
Speaker 5: You can never be absolutely certain of any scientific result

871
00:44:47,640 --> 00:44:49,960
Speaker 5: that you produce. But on the other hand, you can

872
00:44:50,000 --> 00:44:53,120
Speaker 5: also not be absolutely certain that this chair sitting next

873
00:44:53,160 --> 00:44:56,359
Speaker 5: to you actually exists, because of course your eyes could

874
00:44:56,360 --> 00:44:58,560
Speaker 5: have malfunctioned, you could be dreaming.

875
00:44:59,400 --> 00:45:04,440
Speaker 9: So one percent certainty is a dream. It's an illusion.

876
00:45:04,480 --> 00:45:06,080
Speaker 9: It's not something we can ever achieve.

877
00:45:06,120 --> 00:45:09,239
Speaker 2: Exactly right, I'm not one hundred percent shortain we're having

878
00:45:09,239 --> 00:45:13,799
Speaker 2: this conversation. Yeah, exactly, Great, Well, so tell us more

879
00:45:14,000 --> 00:45:16,799
Speaker 2: about your project Think like a Physicist, where people can

880
00:45:16,840 --> 00:45:19,120
Speaker 2: go to learn more about it and learn more about

881
00:45:19,160 --> 00:45:20,360
Speaker 2: thinking like a physicist.

882
00:45:20,640 --> 00:45:20,920
Speaker 9: Yeah.

883
00:45:20,960 --> 00:45:23,799
Speaker 5: So I have a YouTube channel. It's called Think like

884
00:45:23,800 --> 00:45:26,640
Speaker 5: a Physicist. And the idea behind my channel is I

885
00:45:26,680 --> 00:45:30,560
Speaker 5: wanted to take the statistical methods, especially also the other

886
00:45:30,640 --> 00:45:33,640
Speaker 5: methods the physicists use, but especially the statistical methods the

887
00:45:33,640 --> 00:45:36,880
Speaker 5: physicists use, and I wanted to explain them in a

888
00:45:36,880 --> 00:45:41,680
Speaker 5: way that I hope non scientists can understand. And the

889
00:45:41,760 --> 00:45:44,640
Speaker 5: idea is that I would like for people when they

890
00:45:44,640 --> 00:45:46,919
Speaker 5: read about a scientific result and it has an error

891
00:45:46,920 --> 00:45:48,640
Speaker 5: bar on it, that they would be able to have

892
00:45:48,680 --> 00:45:52,160
Speaker 5: a better understanding of what that error bar means, and

893
00:45:52,320 --> 00:45:57,239
Speaker 5: also that that way they can understand scientific results in context.

894
00:45:57,280 --> 00:46:03,120
Speaker 5: For example, if you hear that one experiment does a

895
00:46:03,320 --> 00:46:05,800
Speaker 5: measurement of a certain quantity and it agrees with the

896
00:46:05,840 --> 00:46:09,640
Speaker 5: standard model. And then three years later you hear that

897
00:46:09,719 --> 00:46:13,200
Speaker 5: another experiment measured the same quantity and they got a

898
00:46:13,239 --> 00:46:16,200
Speaker 5: different result. You know, it might be because the second

899
00:46:16,239 --> 00:46:19,879
Speaker 5: experiment had a smaller error bar than the first one did,

900
00:46:20,880 --> 00:46:23,640
Speaker 5: and so you can understand results in context better.

901
00:46:25,160 --> 00:46:27,640
Speaker 9: So basically, I go through a.

902
00:46:27,600 --> 00:46:33,799
Speaker 5: Lot of the basic statistical techniques that physicists use, and

903
00:46:33,880 --> 00:46:35,719
Speaker 5: I hope that I explained them in a way that

904
00:46:35,760 --> 00:46:40,680
Speaker 5: people can understand. And so, yeah, I would very much

905
00:46:40,840 --> 00:46:43,120
Speaker 5: like the public to know more about these topics so

906
00:46:43,120 --> 00:46:44,799
Speaker 5: that they can understand what we do a bit better.

907
00:46:45,200 --> 00:46:47,200
Speaker 2: Great, tell us one more time where people can find you.

908
00:46:47,719 --> 00:46:50,279
Speaker 5: Yeah, my YouTube channel is called think like a Physicist.

909
00:46:50,520 --> 00:46:52,960
Speaker 2: Great, well, thanks very much Jen for coming on podcast

910
00:46:53,000 --> 00:46:55,759
Speaker 2: today and thinking like a Physicist with me. I appreciate it.

911
00:46:55,800 --> 00:46:57,320
Speaker 9: Thank you so much. It's been great.

912
00:46:57,960 --> 00:47:00,640
Speaker 1: All right, interesting interview. I like the she talked about

913
00:47:00,719 --> 00:47:04,080
Speaker 1: uncertainties and how you know this concept, you know, spills

914
00:47:04,120 --> 00:47:07,239
Speaker 1: into our everyday lives, especially when it comes to things

915
00:47:07,280 --> 00:47:10,040
Speaker 1: like policies. But people don't seem to have a pretty

916
00:47:10,360 --> 00:47:13,160
Speaker 1: good understanding of that. Maybe they should talk to statisticians,

917
00:47:14,120 --> 00:47:16,120
Speaker 1: not physicists or politicians.

918
00:47:16,160 --> 00:47:18,480
Speaker 2: How to think like a statistician exactly.

919
00:47:18,800 --> 00:47:21,680
Speaker 1: Yeah, how to probably think like a statistician?

920
00:47:23,760 --> 00:47:25,399
Speaker 2: How statisticians likely think?

921
00:47:26,320 --> 00:47:29,000
Speaker 1: Yeah, likely think or think likely?

922
00:47:31,480 --> 00:47:33,680
Speaker 2: The likelihood of me finding a good joke is low.

923
00:47:35,040 --> 00:47:40,520
Speaker 1: Yeah, we'll make that the null hypothesis. All right, and

924
00:47:40,560 --> 00:47:43,400
Speaker 1: an interesting perspective though, about how to think like a physicist. Now,

925
00:47:43,480 --> 00:47:46,480
Speaker 1: let's talk to someone whose job it is to, I guess,

926
00:47:46,520 --> 00:47:49,640
Speaker 1: reintroduce physicists out into the world, sort of like those

927
00:47:49,680 --> 00:47:53,719
Speaker 1: wildlife experts who have to retrain animals to live in

928
00:47:53,719 --> 00:47:55,719
Speaker 1: the wild. Is that is that kind of her job?

929
00:47:57,000 --> 00:47:59,960
Speaker 2: Yeah, exactly, Or re educate prisoners who are emerging.

930
00:48:00,680 --> 00:48:05,080
Speaker 1: Oh, oh my goodness, I guess I could eat me

931
00:48:05,080 --> 00:48:08,000
Speaker 1: a sort of like a prison. There are walls, towers,

932
00:48:08,760 --> 00:48:12,120
Speaker 1: you know, small rooms where people are sitting all day.

933
00:48:12,719 --> 00:48:13,640
Speaker 2: The food is terrible.

934
00:48:14,280 --> 00:48:15,879
Speaker 1: Do you have does your door have bars in it

935
00:48:16,000 --> 00:48:21,680
Speaker 1: as well? And the average sentence is like sixty seven years? Right?

936
00:48:21,880 --> 00:48:23,480
Speaker 2: Oh, I got a lifetime sentence over here.

937
00:48:26,640 --> 00:48:31,160
Speaker 1: You did a capital discovery. All right, Well, we'll get

938
00:48:31,200 --> 00:48:35,839
Speaker 1: to Daniel's interview with physicists Kathy Kopick about what physicists

939
00:48:35,920 --> 00:48:39,520
Speaker 1: can do outside of physics. So let's dig into that.

940
00:48:39,560 --> 00:48:54,640
Speaker 1: But first, let's take another quick break. All right, we're

941
00:48:54,680 --> 00:48:58,360
Speaker 1: asking the question how to think like a physicist? That

942
00:48:58,440 --> 00:49:01,200
Speaker 1: sounds like a great T shirt, Think like.

943
00:49:01,160 --> 00:49:04,239
Speaker 2: A physicist, yeah, or a bumper sticker and in.

944
00:49:04,200 --> 00:49:09,239
Speaker 1: The bags is snap like a physicist too? Well, Danny,

945
00:49:09,280 --> 00:49:12,279
Speaker 1: you got to talk to another physicist who sort of

946
00:49:12,360 --> 00:49:14,000
Speaker 1: does something else that's kind of interesting.

947
00:49:14,200 --> 00:49:17,000
Speaker 2: Yeah. Kathy Kopeik is an old friend of mine. She

948
00:49:17,040 --> 00:49:19,640
Speaker 2: and I did experimental particle physics together many years ago,

949
00:49:19,719 --> 00:49:22,480
Speaker 2: but then she ventured out into the world instead of

950
00:49:22,480 --> 00:49:25,640
Speaker 2: continuing in physics research, and for many years her job

951
00:49:25,880 --> 00:49:30,200
Speaker 2: was to help people who have PhDs in physics find

952
00:49:30,320 --> 00:49:33,759
Speaker 2: jobs outside of physics, mostly in data science and in

953
00:49:33,840 --> 00:49:36,520
Speaker 2: machine learning industry, which has been gobbling up a lot

954
00:49:36,520 --> 00:49:37,600
Speaker 2: of physics PhDs.

955
00:49:37,840 --> 00:49:39,640
Speaker 1: Well, she did this for a company or is it

956
00:49:39,719 --> 00:49:40,719
Speaker 1: consultant or what.

957
00:49:41,160 --> 00:49:43,839
Speaker 2: Yeah, there was a company called Insight Data Science, which

958
00:49:43,880 --> 00:49:46,520
Speaker 2: was like a boot campo take people from physics, give

959
00:49:46,560 --> 00:49:48,880
Speaker 2: them a little bit of an introduction into the tools

960
00:49:48,880 --> 00:49:51,960
Speaker 2: of business or industry, or at least help them translate

961
00:49:52,000 --> 00:49:53,879
Speaker 2: their experience so they knew how to talk about it.

962
00:49:54,200 --> 00:49:56,360
Speaker 2: I find that one of the biggest barriers between fields

963
00:49:56,480 --> 00:49:59,839
Speaker 2: is just vocabulary. You know, everybody talks about the same

964
00:49:59,840 --> 00:50:01,719
Speaker 2: thing and using different words, and so if you just

965
00:50:01,800 --> 00:50:05,840
Speaker 2: learn to translate your work, your expertise into somebody else's language,

966
00:50:05,920 --> 00:50:07,920
Speaker 2: you can help them understand how you might be useful

967
00:50:07,920 --> 00:50:08,600
Speaker 2: to their company.

968
00:50:09,920 --> 00:50:12,080
Speaker 1: Right, Right, You just have to say things like I

969
00:50:12,120 --> 00:50:16,399
Speaker 1: worked on a model to understand the universe, and then

970
00:50:16,440 --> 00:50:17,759
Speaker 1: all scientists will understand you.

971
00:50:19,560 --> 00:50:23,400
Speaker 2: I'm gonna circle back and connect with stakeholders so that

972
00:50:23,440 --> 00:50:26,560
Speaker 2: we can maximize shareholder profit. Right, that's my attempt to.

973
00:50:26,560 --> 00:50:32,160
Speaker 1: Speak corporate world. That's how you think they talk in

974
00:50:32,280 --> 00:50:33,200
Speaker 1: corporate America.

975
00:50:33,280 --> 00:50:35,200
Speaker 2: I mean based on the sitcoms I watch, I mean

976
00:50:35,280 --> 00:50:36,080
Speaker 2: research I've done.

977
00:50:36,120 --> 00:50:40,600
Speaker 1: Then, yes, is that part of thinking like a physicist

978
00:50:40,640 --> 00:50:42,560
Speaker 1: is doing your research on TV and YouTube?

979
00:50:43,960 --> 00:50:45,200
Speaker 2: That's just part of living man.

980
00:50:47,480 --> 00:50:49,719
Speaker 1: Now, you said Kathy used to do that. What does

981
00:50:49,760 --> 00:50:50,320
Speaker 1: she do now?

982
00:50:50,560 --> 00:50:52,480
Speaker 2: Yeah? Now Kathy has a bunch of jobs. She's teaching

983
00:50:52,520 --> 00:50:54,080
Speaker 2: at Berkeley and at Stanford, and she has her own

984
00:50:54,120 --> 00:50:57,880
Speaker 2: consulting company helping people find physicists to work in their teams.

985
00:50:58,200 --> 00:51:01,120
Speaker 1: All right, Well, here is Daniel's interview doctor Kathy Kopeck

986
00:51:01,680 --> 00:51:03,920
Speaker 1: on how to think like a physicist and how to

987
00:51:03,920 --> 00:51:05,800
Speaker 1: get a job as as a physicist, or how to

988
00:51:05,840 --> 00:51:07,520
Speaker 1: pretend you're not a physicist to get a job. Is

989
00:51:07,560 --> 00:51:08,120
Speaker 1: that that?

990
00:51:08,600 --> 00:51:11,719
Speaker 2: Yeah, yeah, to get a non physics job if you

991
00:51:11,760 --> 00:51:15,520
Speaker 2: are a physicist, there you go. All right. So then

992
00:51:15,560 --> 00:51:18,319
Speaker 2: it's my great pleasure to introduce to the podcast my

993
00:51:18,440 --> 00:51:22,040
Speaker 2: friend and colleague, doctor Kathy Copik. Kathy, thanks very much

994
00:51:22,080 --> 00:51:22,880
Speaker 2: for joining us today.

995
00:51:23,440 --> 00:51:25,160
Speaker 8: Oh, thanks so much. I'm really excited.

996
00:51:25,640 --> 00:51:27,640
Speaker 2: Tell us a little bit about who you are, what

997
00:51:27,760 --> 00:51:30,800
Speaker 2: your background is. You have a special and unusual journey.

998
00:51:31,480 --> 00:51:35,480
Speaker 8: Oh yeah, thanks sure. So I was a physicist and

999
00:51:35,480 --> 00:51:37,120
Speaker 8: am a physicist. I don't know if we talked in

1000
00:51:37,160 --> 00:51:40,600
Speaker 8: the past or present tense, but I worked in experimental

1001
00:51:40,640 --> 00:51:45,560
Speaker 8: particle physicists for a long time, first actually in California

1002
00:51:45,600 --> 00:51:49,000
Speaker 8: and but Bar, then outside Chicago on the CDs experiment

1003
00:51:49,040 --> 00:51:52,880
Speaker 8: at Formulab. Then I was at CERN for a long time,

1004
00:51:53,040 --> 00:51:57,640
Speaker 8: as were you, working on the Atlas experiment with Columbia

1005
00:51:57,680 --> 00:51:59,600
Speaker 8: and then with Berkeley. So I just I was in

1006
00:51:59,640 --> 00:52:04,160
Speaker 8: physics for a long time, studying the smallest things, and

1007
00:52:04,200 --> 00:52:07,080
Speaker 8: then I worked in the last ten years a lot

1008
00:52:07,120 --> 00:52:11,680
Speaker 8: on helping teams outside of academia think about how they

1009
00:52:12,200 --> 00:52:14,200
Speaker 8: use data in lots of ways, and how they hire

1010
00:52:14,280 --> 00:52:17,080
Speaker 8: their teams. I worked for about seven years at the

1011
00:52:17,120 --> 00:52:20,279
Speaker 8: Insight Data Science Fellows Program, working with a lot of

1012
00:52:20,280 --> 00:52:24,120
Speaker 8: scientists making a transition from working in science to working

1013
00:52:24,520 --> 00:52:29,000
Speaker 8: in tech in business, and worked with literally thousands of

1014
00:52:29,000 --> 00:52:32,800
Speaker 8: people making career transitions to literally hundreds of companies. And

1015
00:52:32,840 --> 00:52:35,680
Speaker 8: now I work as a consultant field Work partners with

1016
00:52:35,719 --> 00:52:38,239
Speaker 8: a friend and we help teams do the same kind

1017
00:52:38,280 --> 00:52:39,800
Speaker 8: of things as consultants.

1018
00:52:40,120 --> 00:52:42,480
Speaker 2: So this may seem like an obvious question, but why

1019
00:52:42,520 --> 00:52:45,320
Speaker 2: are people making a transition. You're getting a PhD in

1020
00:52:45,360 --> 00:52:48,120
Speaker 2: particle physics, You're studying the secrets of the universe. Why

1021
00:52:48,120 --> 00:52:51,480
Speaker 2: are people then going to work for healthcare companies or whatever?

1022
00:52:52,080 --> 00:52:55,920
Speaker 8: Sure, yeah, I say two main reasons. One is genuine interest.

1023
00:52:56,000 --> 00:52:58,600
Speaker 8: You know, people are excited about and curious about lots

1024
00:52:58,600 --> 00:53:00,359
Speaker 8: of things. It's one of the things that drive them

1025
00:53:00,400 --> 00:53:03,359
Speaker 8: to be scientists in the first place. And I talk

1026
00:53:03,480 --> 00:53:07,759
Speaker 8: to lots of people who are interviewing with our program

1027
00:53:06,840 --> 00:53:11,040
Speaker 8: to make that transition, and people were like, you know,

1028
00:53:11,080 --> 00:53:13,080
Speaker 8: I've done this thing for a long time and I

1029
00:53:13,120 --> 00:53:15,439
Speaker 8: really like doing it, and now I'm interested in doing

1030
00:53:15,440 --> 00:53:18,560
Speaker 8: something else. And so I think there is definitely genuine

1031
00:53:18,600 --> 00:53:22,319
Speaker 8: interest and curiosity about what it's like. And then I

1032
00:53:22,320 --> 00:53:24,400
Speaker 8: think on the other side, you know, the job market

1033
00:53:24,400 --> 00:53:27,799
Speaker 8: for academics is very hard getting that next position, that

1034
00:53:27,920 --> 00:53:31,960
Speaker 8: next position. Both it's very challenging. There's fewer and fewer

1035
00:53:32,000 --> 00:53:35,920
Speaker 8: positions at every level, and so naturally people have to

1036
00:53:35,960 --> 00:53:39,919
Speaker 8: exit academia. And also there's often fewer choice, like less

1037
00:53:40,000 --> 00:53:41,960
Speaker 8: choice of each level, so you know where you're going

1038
00:53:42,000 --> 00:53:43,640
Speaker 8: to live, what you're going to work on, who you're

1039
00:53:43,680 --> 00:53:46,279
Speaker 8: going to work with. Getting those positions is pretty tough,

1040
00:53:46,480 --> 00:53:48,440
Speaker 8: and so not just in physics, but in all fields

1041
00:53:48,480 --> 00:53:54,400
Speaker 8: across academia. People transition out after their undergrad after their PhD,

1042
00:53:54,680 --> 00:53:57,319
Speaker 8: after post docs, and sometimes at the faculty level as well.

1043
00:53:58,160 --> 00:54:00,840
Speaker 2: So we're always telling our students, hey, come to a

1044
00:54:00,840 --> 00:54:03,360
Speaker 2: PhD in physics because you're going to learn important skills

1045
00:54:03,400 --> 00:54:05,680
Speaker 2: about thinking and you're going to train yourself to be

1046
00:54:06,120 --> 00:54:09,680
Speaker 2: a smart person. And those skills are broadly applicable. And

1047
00:54:09,760 --> 00:54:11,759
Speaker 2: I've never worked outside of academia, so I don't know

1048
00:54:11,760 --> 00:54:14,080
Speaker 2: if I've been lying to people. Tell me, have I

1049
00:54:14,120 --> 00:54:17,399
Speaker 2: been lying to people? What skills do physics PhDs learn

1050
00:54:17,680 --> 00:54:20,160
Speaker 2: that are actually useful outside of particle physics?

1051
00:54:20,600 --> 00:54:23,920
Speaker 8: Sure? Sure, I do not think you are lying to people.

1052
00:54:23,960 --> 00:54:27,160
Speaker 8: I do think those skills are genuinely useful, and you

1053
00:54:27,200 --> 00:54:29,000
Speaker 8: can tell when you see where people go on to

1054
00:54:29,080 --> 00:54:31,680
Speaker 8: work after they've been in physics a lot of times

1055
00:54:31,680 --> 00:54:35,800
Speaker 8: in physics, and also that's in other places. The skills

1056
00:54:35,800 --> 00:54:40,120
Speaker 8: that people learn. I think there's three main things. The

1057
00:54:40,160 --> 00:54:42,800
Speaker 8: first one is just trying to figure out how to

1058
00:54:42,840 --> 00:54:47,200
Speaker 8: break a problem into smaller problems and questions, thinking about like, Okay,

1059
00:54:47,239 --> 00:54:50,080
Speaker 8: there's this big question we have, like what's the smallest

1060
00:54:50,120 --> 00:54:52,160
Speaker 8: thing in the universe, the thing that both you and

1061
00:54:52,200 --> 00:54:54,680
Speaker 8: I worked on and so have the big question? But

1062
00:54:54,719 --> 00:54:56,759
Speaker 8: then okay, how do I break that down into things

1063
00:54:56,760 --> 00:54:59,319
Speaker 8: that can be measured or things that we can write

1064
00:54:59,480 --> 00:55:03,720
Speaker 8: a theoretic model for. So breaking big questions into small

1065
00:55:03,800 --> 00:55:06,840
Speaker 8: questions it's a really important skill if you want to

1066
00:55:06,840 --> 00:55:09,359
Speaker 8: ask questions about the universe, but also if you want

1067
00:55:09,360 --> 00:55:13,319
Speaker 8: to ask questions about a business, or you know, how

1068
00:55:13,480 --> 00:55:15,680
Speaker 8: how many beds in a hospital are likely to be

1069
00:55:15,719 --> 00:55:18,400
Speaker 8: available on a given day given the procedures and things

1070
00:55:18,400 --> 00:55:21,200
Speaker 8: that are coming up, and how uncertain is it that

1071
00:55:21,360 --> 00:55:24,000
Speaker 8: people will get discharged on a certain day. If you're

1072
00:55:24,000 --> 00:55:27,120
Speaker 8: trying to build a model of anything, not just in science,

1073
00:55:27,160 --> 00:55:29,600
Speaker 8: but also in the real world, breaking a big problem

1074
00:55:29,600 --> 00:55:32,040
Speaker 8: into small questions is a big, big skill.

1075
00:55:32,320 --> 00:55:34,120
Speaker 2: Let me drill into that a little bit. I understand

1076
00:55:34,200 --> 00:55:35,920
Speaker 2: it's important to know, like how to get started on

1077
00:55:35,960 --> 00:55:38,200
Speaker 2: a problem. You're working for a company and they give

1078
00:55:38,200 --> 00:55:40,120
Speaker 2: you this project. They're like, build us this widget that

1079
00:55:40,160 --> 00:55:42,160
Speaker 2: does that thing, and you need to know what to

1080
00:55:42,200 --> 00:55:44,720
Speaker 2: do on day one so that after day ninety you're there.

1081
00:55:45,480 --> 00:55:48,640
Speaker 2: Why is that something that physicists in particular are good at,

1082
00:55:48,680 --> 00:55:51,200
Speaker 2: Like how does study in the nature of the universe

1083
00:55:51,400 --> 00:55:54,600
Speaker 2: make you good at learning how to break down problems?

1084
00:55:55,120 --> 00:55:57,880
Speaker 8: Yeah, a lot of things that physicists are good at

1085
00:55:57,920 --> 00:56:01,359
Speaker 8: are think scientists in general are good at asking question

1086
00:56:01,440 --> 00:56:05,400
Speaker 8: breaking it into problems, But physics in particular, I think

1087
00:56:06,360 --> 00:56:09,520
Speaker 8: both people who are drawn to physics and physics education

1088
00:56:09,680 --> 00:56:13,160
Speaker 8: reinforce the same thing, which is not just being a

1089
00:56:13,200 --> 00:56:17,600
Speaker 8: little bit curious, but being like really curious. You know,

1090
00:56:17,960 --> 00:56:21,520
Speaker 8: they're not just stopping at some level that's like a

1091
00:56:21,560 --> 00:56:25,359
Speaker 8: service level or where there's maybe approximations or things you're

1092
00:56:25,480 --> 00:56:29,000
Speaker 8: like really continuing to either you personally because it's how

1093
00:56:29,040 --> 00:56:32,440
Speaker 8: you think about the world, or in your education, working

1094
00:56:32,480 --> 00:56:36,400
Speaker 8: with your teachers and mentors are like really really really

1095
00:56:36,480 --> 00:56:40,240
Speaker 8: drilling down to these questions, to the really basic pieces

1096
00:56:40,280 --> 00:56:43,640
Speaker 8: of it. And I think that is unique to physics.

1097
00:56:43,680 --> 00:56:46,640
Speaker 8: It's you know, the people who study physics have chosen

1098
00:56:46,719 --> 00:56:50,120
Speaker 8: to kind of like continue down that path of questions

1099
00:56:50,160 --> 00:56:53,680
Speaker 8: to where you know, there's things are not even alive anymore.

1100
00:56:55,120 --> 00:56:59,239
Speaker 8: You're studying one atom, or studying how galaxies form, or

1101
00:56:59,280 --> 00:57:05,440
Speaker 8: some like very complicated basic question about the universe. So

1102
00:57:06,200 --> 00:57:08,440
Speaker 8: and I think it's true everybody takes a question and

1103
00:57:08,480 --> 00:57:11,080
Speaker 8: breaks it into smaller questions in science, but in physics

1104
00:57:11,960 --> 00:57:15,000
Speaker 8: really really trying to get to the most basic things

1105
00:57:15,040 --> 00:57:16,040
Speaker 8: about how the world works.

1106
00:57:16,120 --> 00:57:18,600
Speaker 2: Right, all right, So I interrupted you. You were telling us

1107
00:57:18,920 --> 00:57:21,520
Speaker 2: the good things that physicists learned, and number one is

1108
00:57:21,560 --> 00:57:23,600
Speaker 2: breaking things into pieces, and number two.

1109
00:57:23,600 --> 00:57:26,920
Speaker 8: Was breaking things into pieces. Number two I think especially

1110
00:57:26,920 --> 00:57:32,080
Speaker 8: in experimental physics working with very large general purpose data

1111
00:57:32,120 --> 00:57:35,240
Speaker 8: sets and a lot of parts of science. You know,

1112
00:57:35,320 --> 00:57:38,560
Speaker 8: every experimental science people have data sets. Sometimes they're very large,

1113
00:57:39,160 --> 00:57:42,920
Speaker 8: but a lot of scientists create those data sets themselves

1114
00:57:43,360 --> 00:57:45,400
Speaker 8: in a smaller group, so they have you know, they're

1115
00:57:45,400 --> 00:57:48,800
Speaker 8: trying to study one thing about how a certain bacteria

1116
00:57:49,080 --> 00:57:51,840
Speaker 8: does something, or you know, you're in their own lab

1117
00:57:51,920 --> 00:57:54,280
Speaker 8: and they kind of know, oh, maybe the data from

1118
00:57:54,480 --> 00:57:56,960
Speaker 8: July is no good because the temperature was off or something.

1119
00:57:57,040 --> 00:58:01,560
Speaker 8: You know, they know the data often because they created it.

1120
00:58:01,600 --> 00:58:05,240
Speaker 8: In physics, especially in experimental particle physics, where we both worked,

1121
00:58:05,280 --> 00:58:08,000
Speaker 8: but also in astrophysics and lots of other areas of physics,

1122
00:58:08,160 --> 00:58:12,160
Speaker 8: people have these very collaborative general purpose data sets that

1123
00:58:12,200 --> 00:58:15,160
Speaker 8: are meant not just to answer one question, but you

1124
00:58:15,200 --> 00:58:18,640
Speaker 8: can ask so many questions from them. And they're messy.

1125
00:58:18,880 --> 00:58:22,640
Speaker 8: They're built, these detectors that are built, and we have problems,

1126
00:58:22,680 --> 00:58:25,840
Speaker 8: some parts not working. Maybe that's showing up in some

1127
00:58:26,440 --> 00:58:29,480
Speaker 8: initial variables, also in some calculated variables down the road.

1128
00:58:29,520 --> 00:58:32,120
Speaker 8: You have to make corrections. Working with that kind of

1129
00:58:32,240 --> 00:58:36,800
Speaker 8: general purpose data is a real skill because that real

1130
00:58:36,840 --> 00:58:39,560
Speaker 8: world data that you might study if you're working at

1131
00:58:39,600 --> 00:58:44,080
Speaker 8: a business or nonprofit or asking some questions about non

1132
00:58:44,160 --> 00:58:47,280
Speaker 8: academic data, very similar to So that's a skill I

1133
00:58:47,320 --> 00:58:50,640
Speaker 8: think people learn in physics. And then a third one

1134
00:58:50,640 --> 00:58:55,080
Speaker 8: I would say is this collaboration working in. Not all

1135
00:58:55,080 --> 00:58:57,200
Speaker 8: collaborations are as big as the ones that we worked on.

1136
00:58:58,120 --> 00:59:01,959
Speaker 8: Most are not, but but working in everybody who's working

1137
00:59:02,000 --> 00:59:04,960
Speaker 8: in physics and in science is really trying to figure

1138
00:59:04,960 --> 00:59:07,920
Speaker 8: out what's already been done. Who has domain knowledge that

1139
00:59:08,000 --> 00:59:10,200
Speaker 8: might help me figure out the piece of it that

1140
00:59:10,240 --> 00:59:13,120
Speaker 8: I'm working on. How do I share what I'm working

1141
00:59:13,160 --> 00:59:15,560
Speaker 8: on in a way that can make sense to build

1142
00:59:15,600 --> 00:59:18,640
Speaker 8: some collaboration. How do I share my results back? How

1143
00:59:18,680 --> 00:59:21,440
Speaker 8: do I write about and speak about what I learned

1144
00:59:21,760 --> 00:59:24,800
Speaker 8: in a way that's going to help advance the research

1145
00:59:24,840 --> 00:59:27,560
Speaker 8: on this question. So all of those I think are

1146
00:59:27,600 --> 00:59:28,360
Speaker 8: really important.

1147
00:59:28,520 --> 00:59:31,480
Speaker 2: So in understanding what it's like to think like a physicist,

1148
00:59:31,880 --> 00:59:35,000
Speaker 2: I think one thing that's helpful is understanding where physicists

1149
00:59:35,040 --> 00:59:37,280
Speaker 2: find their skills useful. So you told us the kind

1150
00:59:37,280 --> 00:59:39,960
Speaker 2: of skills we learn, But where do people who have

1151
00:59:40,080 --> 00:59:44,120
Speaker 2: been trained in particle physics end up making impacts in

1152
00:59:44,160 --> 00:59:47,360
Speaker 2: the world outside of particle physics. Where are these skills helpful?

1153
00:59:47,960 --> 00:59:48,200
Speaker 1: Yeah?

1154
00:59:48,720 --> 00:59:52,480
Speaker 8: I think really everywhere. And I'm not just like trying

1155
00:59:52,520 --> 00:59:56,120
Speaker 8: to make it seem just everywhere. But in all the

1156
00:59:56,240 --> 00:59:58,680
Speaker 8: kinds of tech companies that you can think of that

1157
00:59:58,760 --> 01:00:02,520
Speaker 8: are working today, people are doing interesting work. Also, small places,

1158
01:00:02,880 --> 01:00:07,040
Speaker 8: nonprofits I mentioned initially. I mentioned this, like people working

1159
01:00:07,040 --> 01:00:09,840
Speaker 8: at a hospital to try to figure out how to

1160
01:00:10,560 --> 01:00:13,200
Speaker 8: build a system that helps predict when patients are going

1161
01:00:13,240 --> 01:00:16,520
Speaker 8: to be coming in or not. People are working in pharmaceuticals,

1162
01:00:16,560 --> 01:00:19,680
Speaker 8: just really in every area I think people are working.

1163
01:00:19,720 --> 01:00:22,840
Speaker 8: I mean I yeah, there's there's so many ex article

1164
01:00:22,920 --> 01:00:27,000
Speaker 8: business to know, so many of us that people go

1165
01:00:27,120 --> 01:00:29,520
Speaker 8: in and people are driven and curious to work on

1166
01:00:29,600 --> 01:00:33,480
Speaker 8: so many things that Yeah, just lots of places.

1167
01:00:33,840 --> 01:00:36,560
Speaker 2: And you know, physics is very good broad training, but

1168
01:00:36,600 --> 01:00:39,280
Speaker 2: we're not learning everything when people go out into the

1169
01:00:39,280 --> 01:00:42,160
Speaker 2: world and try to work on actual practical problems with

1170
01:00:42,360 --> 01:00:45,160
Speaker 2: real deliverables and stuff. What are some sort of blind spots.

1171
01:00:45,160 --> 01:00:47,840
Speaker 2: What are some things that physicists don't learn that are

1172
01:00:47,920 --> 01:00:49,200
Speaker 2: useful in the rest of the world.

1173
01:00:50,000 --> 01:00:54,320
Speaker 8: Yeah, I think that all of those advantages, those superpowers

1174
01:00:54,360 --> 01:00:57,040
Speaker 8: that I talked about have some kind of reverse kryptonite,

1175
01:00:57,040 --> 01:01:00,560
Speaker 8: which is like being very curious and very detail oriented

1176
01:01:00,600 --> 01:01:02,760
Speaker 8: and driven to like get to the very bottom of

1177
01:01:02,800 --> 01:01:05,920
Speaker 8: the question is a good instinct in physics, it's important.

1178
01:01:06,120 --> 01:01:10,600
Speaker 8: But sometimes in the business world you don't have the

1179
01:01:10,720 --> 01:01:14,000
Speaker 8: time or resources to like get really to the very

1180
01:01:14,040 --> 01:01:16,080
Speaker 8: bottom of something, and you have to kind of step

1181
01:01:16,120 --> 01:01:19,320
Speaker 8: back and make an approximation or maybe we're only going

1182
01:01:19,400 --> 01:01:20,840
Speaker 8: to run this thing for a week and we're going

1183
01:01:20,880 --> 01:01:22,240
Speaker 8: to get as far as we're going to get, but

1184
01:01:22,640 --> 01:01:24,040
Speaker 8: at the end, what we're trying to do is like

1185
01:01:24,200 --> 01:01:26,959
Speaker 8: recommend the next song for someone, or recommend the next

1186
01:01:27,400 --> 01:01:30,560
Speaker 8: for someone to watch. And so actually it's okay if

1187
01:01:30,640 --> 01:01:33,840
Speaker 8: like we don't understand everything about this, and so sometimes

1188
01:01:33,880 --> 01:01:35,680
Speaker 8: taking that step back and being like, you know, this

1189
01:01:35,800 --> 01:01:39,040
Speaker 8: isn't a six month project or a six year project.

1190
01:01:39,080 --> 01:01:42,280
Speaker 8: This is like a six week project, and we're gonna

1191
01:01:42,360 --> 01:01:44,760
Speaker 8: build something and we're gonna ship it and it's going

1192
01:01:44,840 --> 01:01:46,360
Speaker 8: to be good enough for that need, you know. And

1193
01:01:46,400 --> 01:01:48,640
Speaker 8: there are areas where that's true. And then there are areas,

1194
01:01:48,880 --> 01:01:52,840
Speaker 8: you know, where like in health and healthcare, where you

1195
01:01:52,880 --> 01:01:54,640
Speaker 8: don't want to make errors. And so I think people

1196
01:01:54,720 --> 01:01:59,120
Speaker 8: kind of through their personality might choose areas where it's

1197
01:01:59,120 --> 01:02:02,439
Speaker 8: okay to you know, recommend the next song for someone

1198
01:02:02,480 --> 01:02:04,840
Speaker 8: they might not enjoy as much. Where it's not okay

1199
01:02:04,880 --> 01:02:08,080
Speaker 8: to recommend, you know, a medication to someone that's not

1200
01:02:08,120 --> 01:02:10,440
Speaker 8: the right fit for them, right if it's you know,

1201
01:02:10,560 --> 01:02:13,200
Speaker 8: and there's still usually in a healthcare setting, there would be

1202
01:02:13,240 --> 01:02:15,600
Speaker 8: a doctor that would be the prescriber. But if you

1203
01:02:15,600 --> 01:02:19,120
Speaker 8: have a tool that's very biased or making wrong predictions

1204
01:02:19,560 --> 01:02:23,480
Speaker 8: for something that's really important like healthcare. You know, there's

1205
01:02:23,560 --> 01:02:24,640
Speaker 8: less room for error.

1206
01:02:25,400 --> 01:02:28,040
Speaker 2: So you've helped a lot of people figure out how

1207
01:02:28,040 --> 01:02:31,080
Speaker 2: to go from particle physics to someplace in the real

1208
01:02:31,120 --> 01:02:33,120
Speaker 2: world where they can make a contribution. How do you

1209
01:02:33,160 --> 01:02:35,400
Speaker 2: do that? How do you like get to know somebody

1210
01:02:35,400 --> 01:02:38,000
Speaker 2: and figure out, like what are their strengths and weaknesses

1211
01:02:38,040 --> 01:02:40,000
Speaker 2: and how does it fit. I mean, you're basically like

1212
01:02:40,040 --> 01:02:43,960
Speaker 2: the yinta of particle physics and jobs. But tell us

1213
01:02:44,000 --> 01:02:44,760
Speaker 2: about your process.

1214
01:02:44,880 --> 01:02:47,280
Speaker 8: Sure, sure, everybody is very different. That's one thing that

1215
01:02:47,320 --> 01:02:50,080
Speaker 8: I enjoy about it. So you know, some people need

1216
01:02:50,120 --> 01:02:52,680
Speaker 8: to grow or change in one area, where other folks

1217
01:02:52,680 --> 01:02:55,160
Speaker 8: that's very different for them. I think the first thing

1218
01:02:55,480 --> 01:02:58,200
Speaker 8: that I try to ask is what motivates people, what

1219
01:02:58,200 --> 01:03:00,840
Speaker 8: they're excited by. You know, some people are very excited

1220
01:03:00,880 --> 01:03:04,000
Speaker 8: by the impact in the real world and the people

1221
01:03:04,040 --> 01:03:05,919
Speaker 8: that might use or be helped by the thing they're

1222
01:03:05,960 --> 01:03:08,840
Speaker 8: working on. Other folks are very excited about the technical

1223
01:03:08,880 --> 01:03:12,240
Speaker 8: tools themselves, like getting to use the most advanced tools

1224
01:03:12,280 --> 01:03:15,760
Speaker 8: and models and getting to work on something technically very exciting.

1225
01:03:16,200 --> 01:03:19,920
Speaker 8: Other people are have worked very deeply and you know,

1226
01:03:20,040 --> 01:03:22,959
Speaker 8: worked ten years on one thing and are actually looking

1227
01:03:23,080 --> 01:03:25,400
Speaker 8: to do something more broad like they're like, oh, I

1228
01:03:25,400 --> 01:03:28,080
Speaker 8: want to learn about a lot of things. Some people

1229
01:03:28,120 --> 01:03:30,080
Speaker 8: love to interact with a lot of people. Some people

1230
01:03:30,120 --> 01:03:32,000
Speaker 8: want to be a little bit more like I kind

1231
01:03:32,000 --> 01:03:33,600
Speaker 8: of want to be given the thing and do my

1232
01:03:33,640 --> 01:03:37,080
Speaker 8: own thing. And so I think there's very different work

1233
01:03:37,440 --> 01:03:41,320
Speaker 8: for people depending on what they like and what they're

1234
01:03:41,360 --> 01:03:43,520
Speaker 8: interested in. And so once you know a little bit

1235
01:03:43,520 --> 01:03:45,920
Speaker 8: more about that, like what are the constraints around the

1236
01:03:46,000 --> 01:03:48,560
Speaker 8: kind of jobs that people are looking for, then I

1237
01:03:48,560 --> 01:03:52,800
Speaker 8: think it's easy to recommend specific like okay, well, and

1238
01:03:52,840 --> 01:03:55,320
Speaker 8: based on geography too, like there's just different kinds of

1239
01:03:55,400 --> 01:03:58,240
Speaker 8: jobs in different places in North America in the world,

1240
01:03:58,640 --> 01:04:02,240
Speaker 8: and so okay, well, for you, it sounds like you're

1241
01:04:02,280 --> 01:04:04,800
Speaker 8: excited about this and you're living here and these are

1242
01:04:04,840 --> 01:04:10,360
Speaker 8: your experiences. Helping people describe what they've done and what

1243
01:04:10,400 --> 01:04:14,440
Speaker 8: they want to do next. People usually don't need to

1244
01:04:14,440 --> 01:04:17,560
Speaker 8: build new skills. They have a lot of skills. It's

1245
01:04:17,600 --> 01:04:21,080
Speaker 8: just they need to have some kind of exploration of

1246
01:04:21,120 --> 01:04:25,919
Speaker 8: the space of available things, what they want, what they have,

1247
01:04:26,360 --> 01:04:30,280
Speaker 8: how they can describe what they've done and maybe demonstrate

1248
01:04:30,280 --> 01:04:34,240
Speaker 8: it in a different way by talking about it differently.

1249
01:04:35,440 --> 01:04:37,120
Speaker 8: Those are the main things I think I would do.

1250
01:04:37,600 --> 01:04:39,840
Speaker 2: So, I've seen a lot of physicists end up like

1251
01:04:39,960 --> 01:04:43,640
Speaker 2: on Wall Street or in data science. These seem to

1252
01:04:43,720 --> 01:04:47,400
Speaker 2: be places like where that community has an appetite for it.

1253
01:04:47,440 --> 01:04:50,360
Speaker 2: They're like, oh, yeah, we like hiring physicists or whatever. Yeah,

1254
01:04:50,680 --> 01:04:53,160
Speaker 2: but tell us some other places where physicists might end

1255
01:04:53,240 --> 01:04:56,800
Speaker 2: up some you know, maybe unusual or bizarre places physics

1256
01:04:56,800 --> 01:04:57,880
Speaker 2: PhDs end up working in.

1257
01:04:58,120 --> 01:05:02,080
Speaker 8: Yeah, that's a good question. I do you think people

1258
01:05:02,240 --> 01:05:05,960
Speaker 8: end up in a lot of places that basically anywhere

1259
01:05:06,040 --> 01:05:11,320
Speaker 8: where people are like building some models to help a

1260
01:05:11,400 --> 01:05:14,920
Speaker 8: system run better. So it could be you know, things education,

1261
01:05:15,200 --> 01:05:18,360
Speaker 8: educational software. People are trying to build ways to help

1262
01:05:18,480 --> 01:05:21,400
Speaker 8: kids learn to read and learn to do math. There's

1263
01:05:21,680 --> 01:05:25,000
Speaker 8: all kinds of games that people work on. Anything that

1264
01:05:25,040 --> 01:05:29,640
Speaker 8: you buy or sell clothes or you know, any sort

1265
01:05:29,680 --> 01:05:32,160
Speaker 8: of products, any sort of recommendations for things that you're

1266
01:05:32,320 --> 01:05:35,040
Speaker 8: that people are working on. Anything in the healthcare industry.

1267
01:05:35,080 --> 01:05:38,840
Speaker 8: I talked about that a lot already. Anything in the

1268
01:05:39,000 --> 01:05:41,520
Speaker 8: kind of broad tech you see, there's a ton of

1269
01:05:42,760 --> 01:05:46,160
Speaker 8: work right now in AI, certainly large language models. A

1270
01:05:46,200 --> 01:05:48,520
Speaker 8: lot of people from physics are working on those tools

1271
01:05:48,600 --> 01:05:52,280
Speaker 8: at all the places you can imagine. There's really a

1272
01:05:52,320 --> 01:05:54,480
Speaker 8: lot of a lot of places. I can't think of

1273
01:05:54,600 --> 01:05:59,080
Speaker 8: one like fun especially funny, Like, oh, here's one thing

1274
01:05:59,120 --> 01:06:02,360
Speaker 8: you can think of. But in every area media, fashion,

1275
01:06:03,040 --> 01:06:05,440
Speaker 8: people are working in all sorts of areas.

1276
01:06:05,200 --> 01:06:08,520
Speaker 2: People working on like optimizing you know, underwear sizes and

1277
01:06:08,520 --> 01:06:09,080
Speaker 2: stuff like this.

1278
01:06:09,280 --> 01:06:13,280
Speaker 8: For sure, for sure, that's particle physics at work. That's right,

1279
01:06:14,160 --> 01:06:16,440
Speaker 8: it's funny and it's a joke. But it's also true

1280
01:06:16,440 --> 01:06:19,400
Speaker 8: that like I don't know, for me, finding clothes that

1281
01:06:19,440 --> 01:06:21,080
Speaker 8: fit is actually really nice.

1282
01:06:21,200 --> 01:06:23,400
Speaker 2: Yes, it's an important, unsolved fart. You can make a

1283
01:06:23,440 --> 01:06:25,160
Speaker 2: real impact in people's daily lives.

1284
01:06:25,480 --> 01:06:27,320
Speaker 8: I mean, it's like a little bit silly, But it's

1285
01:06:27,360 --> 01:06:29,480
Speaker 8: also true that there's a lot of I think there's

1286
01:06:29,480 --> 01:06:32,200
Speaker 8: a lot of systems where people have just done the

1287
01:06:32,240 --> 01:06:34,800
Speaker 8: same thing forever and having a fresh take on it

1288
01:06:34,800 --> 01:06:35,520
Speaker 8: can be helpful.

1289
01:06:35,720 --> 01:06:37,960
Speaker 2: Yeah, everybody's got like their favorite pair of jeans or

1290
01:06:38,000 --> 01:06:39,720
Speaker 2: their favorite pair of underwear, and there's a reason they

1291
01:06:39,760 --> 01:06:43,919
Speaker 2: fit right, it feels good. So there's this lore going

1292
01:06:43,960 --> 01:06:46,240
Speaker 2: around that I hear a lot that one of the

1293
01:06:46,280 --> 01:06:50,040
Speaker 2: reasons behind the two thousand and eight financial collapse was

1294
01:06:50,560 --> 01:06:53,280
Speaker 2: that Wall Street went a little bit crazy with its modeling,

1295
01:06:53,520 --> 01:06:55,960
Speaker 2: and that there were these crazy quants and that most

1296
01:06:56,000 --> 01:06:59,040
Speaker 2: of them were ex physicists who didn't really understand the

1297
01:06:59,040 --> 01:07:01,920
Speaker 2: system and just like wrote a bunch of code that

1298
01:07:01,960 --> 01:07:06,120
Speaker 2: went crazy and destroyed people's lives. So would they have

1299
01:07:06,200 --> 01:07:09,000
Speaker 2: to say that did just call the cause the financial

1300
01:07:09,080 --> 01:07:10,120
Speaker 2: collapse or not?

1301
01:07:11,440 --> 01:07:17,960
Speaker 8: Probably not alone. I'll say that the I do you

1302
01:07:18,040 --> 01:07:23,000
Speaker 8: think there's a superpower kryptonite that physicists are very interested in,

1303
01:07:23,240 --> 01:07:25,880
Speaker 8: you know, going down to the root causes the basic

1304
01:07:27,480 --> 01:07:29,160
Speaker 8: how do you take this problem break it into the

1305
01:07:29,160 --> 01:07:32,800
Speaker 8: basic parts? And I think that the cryptonite version of

1306
01:07:32,840 --> 01:07:34,800
Speaker 8: that is like thinking that you can do that in

1307
01:07:34,840 --> 01:07:39,320
Speaker 8: any field, for any topic without necessarily consulting and learning

1308
01:07:39,320 --> 01:07:42,760
Speaker 8: about the domaining knowledge of the practitioners or people that

1309
01:07:42,800 --> 01:07:46,439
Speaker 8: have worked in that area. There's a famous data science person,

1310
01:07:46,520 --> 01:07:49,520
Speaker 8: Drew Conway used to say, physicists where like kind of

1311
01:07:49,520 --> 01:07:51,640
Speaker 8: like wil to beasts that would like run into an

1312
01:07:51,680 --> 01:07:54,960
Speaker 8: area that seems interesting, like biophysics, right, It's like, oh,

1313
01:07:55,200 --> 01:07:57,360
Speaker 8: there's something interesting there. All the physics here comes a

1314
01:07:57,360 --> 01:07:59,440
Speaker 8: lot of old you know, ex physicists who are like,

1315
01:07:59,760 --> 01:08:02,040
Speaker 8: we'll solve all the problems. And so when I would

1316
01:08:02,080 --> 01:08:04,240
Speaker 8: give talks to physics. I would say, don't be a

1317
01:08:04,280 --> 01:08:08,600
Speaker 8: will to beast, like, don't run into their area to

1318
01:08:08,640 --> 01:08:10,840
Speaker 8: a new area. So these maybe these two thousand and

1319
01:08:10,840 --> 01:08:13,080
Speaker 8: eight physicists are kind of just like I know, I'll

1320
01:08:13,080 --> 01:08:15,080
Speaker 8: break down this problem into these parts and look what

1321
01:08:15,120 --> 01:08:17,640
Speaker 8: I'm doing, isn't it cool? But if there is a

1322
01:08:17,640 --> 01:08:22,599
Speaker 8: little bit more domain knowledge or thought around, how could

1323
01:08:22,600 --> 01:08:26,439
Speaker 8: this go wrong? How might this affect people who? Why

1324
01:08:26,520 --> 01:08:31,160
Speaker 8: might we not do this? They could have avoided some

1325
01:08:31,200 --> 01:08:31,879
Speaker 8: bad outcomes?

1326
01:08:32,000 --> 01:08:34,400
Speaker 2: All right, So maybe we're not totally guilty, just partially.

1327
01:08:35,280 --> 01:08:35,479
Speaker 8: Yeah.

1328
01:08:36,040 --> 01:08:38,920
Speaker 2: So a lot of our audience are folks who like

1329
01:08:38,960 --> 01:08:41,439
Speaker 2: physics and like thinking about physics and have been listening

1330
01:08:41,479 --> 01:08:44,080
Speaker 2: to the pod and learning to think like a physicist

1331
01:08:44,120 --> 01:08:47,080
Speaker 2: and applying you know, that mental model to questions about

1332
01:08:47,120 --> 01:08:49,840
Speaker 2: the universe. But what would be your advice for somebody

1333
01:08:49,840 --> 01:08:52,760
Speaker 2: out there who wants to take advantage of this way

1334
01:08:52,800 --> 01:08:55,519
Speaker 2: of thinking, somebody who's not necessarily trained as a physicist

1335
01:08:55,560 --> 01:08:58,479
Speaker 2: but wants to learn to think like a physicist. What

1336
01:08:58,479 --> 01:09:00,960
Speaker 2: would be your advice for learn need to think that way?

1337
01:09:01,560 --> 01:09:04,240
Speaker 8: Yeah? I think there's this. I'm sure you talk about

1338
01:09:04,320 --> 01:09:08,599
Speaker 8: the Drake's equation, which is used for thinking about where

1339
01:09:08,680 --> 01:09:10,559
Speaker 8: extraterrestrial life might be in the Milky Way?

1340
01:09:10,640 --> 01:09:10,760
Speaker 2: Right?

1341
01:09:10,840 --> 01:09:13,080
Speaker 8: Is that right? Probably know much more of that. So

1342
01:09:13,560 --> 01:09:15,960
Speaker 8: that's the thing where you kind of are taking these pieces.

1343
01:09:16,320 --> 01:09:19,160
Speaker 8: Anybody can look up the Drake equation or Drake's equation

1344
01:09:19,520 --> 01:09:22,719
Speaker 8: and taking these pieces and trying to put it together

1345
01:09:22,840 --> 01:09:24,759
Speaker 8: to get one answer. And I went to a business

1346
01:09:24,800 --> 01:09:27,800
Speaker 8: class where people were talking about using the same sort

1347
01:09:27,840 --> 01:09:31,160
Speaker 8: of thing to model businesses or other processes where it's

1348
01:09:31,240 --> 01:09:35,040
Speaker 8: just trying to think about anybody can think about what

1349
01:09:35,160 --> 01:09:40,839
Speaker 8: are the parts that come together to create some answer

1350
01:09:40,960 --> 01:09:43,680
Speaker 8: or some prediction. And so just take thinking about that.

1351
01:09:43,920 --> 01:09:47,280
Speaker 8: Breaking something up into things that you can measure individually

1352
01:09:47,360 --> 01:09:50,080
Speaker 8: or you can think about individually, can really help solve

1353
01:09:50,080 --> 01:09:53,120
Speaker 8: a problem, whether it's a science problem, business problem, any

1354
01:09:53,200 --> 01:09:53,879
Speaker 8: kind of problems.

1355
01:09:53,920 --> 01:09:55,800
Speaker 2: All right, So then last question, a bit of a

1356
01:09:55,800 --> 01:09:59,200
Speaker 2: personal one. What do you miss most about actively working

1357
01:09:59,280 --> 01:10:01,880
Speaker 2: in physics? Say about being a physicist, because I think

1358
01:10:01,920 --> 01:10:04,719
Speaker 2: you're always a physicist once you're trying, like once a Jedi,

1359
01:10:04,800 --> 01:10:07,200
Speaker 2: always in Jedi. But what do you miss most about

1360
01:10:07,200 --> 01:10:10,760
Speaker 2: like working on particle physics other than working with me?

1361
01:10:10,880 --> 01:10:17,160
Speaker 8: Obviously I was gonna say, I mean, you're joking, but

1362
01:10:17,680 --> 01:10:23,320
Speaker 8: I think I really really did. There's a very special, fun,

1363
01:10:23,760 --> 01:10:27,040
Speaker 8: exciting environment of being at the lab in these big

1364
01:10:27,080 --> 01:10:30,640
Speaker 8: experiments at both that you know, at Slack in California,

1365
01:10:30,760 --> 01:10:35,160
Speaker 8: Fermi Lab, Brooke Caven Cern that these labs just it's

1366
01:10:35,200 --> 01:10:38,080
Speaker 8: really literally people from all over the world and having

1367
01:10:38,160 --> 01:10:42,519
Speaker 8: lunch together and the big cafeteria. Cerns called our one

1368
01:10:42,920 --> 01:10:46,439
Speaker 8: restaurant one a very creative name. I don't know if

1369
01:10:46,439 --> 01:10:48,360
Speaker 8: it still is. It's not named after someone now, is

1370
01:10:48,400 --> 01:10:51,679
Speaker 8: it still our one? Yeah? So our one. So if

1371
01:10:51,680 --> 01:10:55,200
Speaker 8: you're there for lunch or for coffee or the end

1372
01:10:55,200 --> 01:10:57,679
Speaker 8: of the day, it's just really fun to run into

1373
01:10:57,760 --> 01:11:00,400
Speaker 8: so many people then you've worked with over your whole career,

1374
01:11:00,640 --> 01:11:02,800
Speaker 8: people who are getting into the field, people who are

1375
01:11:03,320 --> 01:11:05,719
Speaker 8: very senior. You never know who's going to be there.

1376
01:11:06,920 --> 01:11:10,479
Speaker 8: Just having some food, drinking coffee and getting to talk

1377
01:11:10,479 --> 01:11:13,040
Speaker 8: to people about what they're working on and also what

1378
01:11:13,040 --> 01:11:16,360
Speaker 8: they're doing and how they are. It's very very fun

1379
01:11:16,920 --> 01:11:21,000
Speaker 8: memories of hanging out there with all sorts of people,

1380
01:11:21,360 --> 01:11:24,080
Speaker 8: and yeah, no, it was a great time. So I

1381
01:11:24,120 --> 01:11:28,400
Speaker 8: would say just missing being with all the people that

1382
01:11:29,160 --> 01:11:31,679
Speaker 8: we used to work with and getting to meet new people.

1383
01:11:31,920 --> 01:11:34,840
Speaker 8: That's a really truly international environment too, really fun.

1384
01:11:35,040 --> 01:11:37,799
Speaker 2: It is fun to hear conversations in so many different languages.

1385
01:11:38,880 --> 01:11:41,880
Speaker 2: I like running into the same really old Nobel Prize

1386
01:11:41,880 --> 01:11:44,200
Speaker 2: winners over and over again, introducing myself every single time

1387
01:11:44,240 --> 01:11:46,040
Speaker 2: because they don't remember me because they're like one hundred

1388
01:11:46,040 --> 01:11:49,200
Speaker 2: and fifty years old. And I also remember one of

1389
01:11:49,200 --> 01:11:51,840
Speaker 2: the first times I was at our one and you

1390
01:11:51,920 --> 01:11:54,559
Speaker 2: had some special trick for making an iced coffee. When

1391
01:11:54,600 --> 01:11:56,479
Speaker 2: you showed it to meet and Katrina and for the

1392
01:11:56,479 --> 01:11:58,000
Speaker 2: rest of the summer we were like, oh, let's get

1393
01:11:58,040 --> 01:12:01,200
Speaker 2: a Kathy. We called it a kathy, you know, a coppuccino.

1394
01:12:01,360 --> 01:12:06,600
Speaker 8: That's yeah, yeah, I've made up created a TGI Fridays.

1395
01:12:06,840 --> 01:12:09,559
Speaker 8: I don't know if you're looking for sponsorships Stan TGI

1396
01:12:09,720 --> 01:12:13,759
Speaker 8: Fridays the restaurant when I was a server there, created

1397
01:12:13,800 --> 01:12:15,479
Speaker 8: the Copacina Cocino delicious.

1398
01:12:15,520 --> 01:12:16,840
Speaker 2: Thank you got us through that summer.

1399
01:12:17,240 --> 01:12:19,519
Speaker 8: Yeah, they don't have They didn't have cappuccino machines. That's

1400
01:12:19,520 --> 01:12:23,400
Speaker 8: sort They had espresso machines, but no, no cappuccino machines,

1401
01:12:23,400 --> 01:12:25,200
Speaker 8: so you got to figure it out all right.

1402
01:12:25,280 --> 01:12:27,680
Speaker 2: Well, thanks very much for sharing with us how to

1403
01:12:27,760 --> 01:12:29,600
Speaker 2: think like a physicist, and how to drink coffee like

1404
01:12:29,640 --> 01:12:31,760
Speaker 2: a physicist. Really appreciate it.

1405
01:12:32,280 --> 01:12:34,679
Speaker 8: We'll put the put the recipe people.

1406
01:12:35,960 --> 01:12:40,200
Speaker 2: In the show notes for Copucina show notes. All right,

1407
01:12:40,280 --> 01:12:40,840
Speaker 2: thanks Kathy.

1408
01:12:41,320 --> 01:12:44,160
Speaker 1: All Right, interesting talk there, Daniel. It seems like she's

1409
01:12:44,360 --> 01:12:45,759
Speaker 1: basically saying we all have skills.

1410
01:12:48,120 --> 01:12:50,160
Speaker 2: Everybody's skills are different at least. Yeah. I think she

1411
01:12:50,240 --> 01:12:53,400
Speaker 2: probably aligns with you to think that, like scientists are

1412
01:12:53,439 --> 01:12:56,080
Speaker 2: all curious thinkers and mental model builders, and not even

1413
01:12:56,120 --> 01:12:58,880
Speaker 2: all physicists are the same. We all think differently and

1414
01:12:59,000 --> 01:13:01,800
Speaker 2: enjoy different parts of the process. Hmm.

1415
01:13:03,000 --> 01:13:05,360
Speaker 1: And that can help you get a job, right, because

1416
01:13:05,560 --> 01:13:07,640
Speaker 1: these are all skills that we could all use in

1417
01:13:07,680 --> 01:13:08,120
Speaker 1: every field.

1418
01:13:08,160 --> 01:13:10,760
Speaker 2: Probably, yeah, exactly. And so in the end, thinking like

1419
01:13:10,800 --> 01:13:13,559
Speaker 2: a physicist is just like thinking like a scientist, being

1420
01:13:13,640 --> 01:13:16,920
Speaker 2: a curious person, trying to understand the world, being methodical

1421
01:13:17,120 --> 01:13:19,439
Speaker 2: about it, try not to fool yourself with what the

1422
01:13:19,560 --> 01:13:20,200
Speaker 2: data is telling you.

1423
01:13:20,960 --> 01:13:25,320
Speaker 1: Yeah, and just trying to maximize your functionality to the stakeholders.

1424
01:13:26,520 --> 01:13:27,080
Speaker 2: Exactly.

1425
01:13:27,479 --> 01:13:32,920
Speaker 1: Maximize shareholder revenue, maximize physicists employment.

1426
01:13:35,160 --> 01:13:37,439
Speaker 2: Try not to cause any more financial collapses. Please.

1427
01:13:37,640 --> 01:13:41,080
Speaker 1: All right, Well, an interesting discussion about thinking like a scientist,

1428
01:13:41,160 --> 01:13:44,680
Speaker 1: thinking like a physicist, what are the commonalities and how

1429
01:13:44,840 --> 01:13:48,120
Speaker 1: things might be a little bit unique. For people who

1430
01:13:48,160 --> 01:13:49,680
Speaker 1: pursue physics as a career.

1431
01:13:49,880 --> 01:13:52,000
Speaker 2: And for those of you out there not pursuing physics

1432
01:13:52,040 --> 01:13:54,960
Speaker 2: as a career, but who have discovered a love for physics,

1433
01:13:55,080 --> 01:13:58,200
Speaker 2: keep doing it, keep thinking like a physicist or a scientist,

1434
01:13:58,280 --> 01:14:00,560
Speaker 2: and keep being curious about the world and trying to

1435
01:14:00,640 --> 01:14:02,920
Speaker 2: make the whole thing click together in your mind.

1436
01:14:03,080 --> 01:14:05,680
Speaker 1: Yeah, but mostly just keep thinking, please.

1437
01:14:06,840 --> 01:14:08,120
Speaker 2: And keep listening to the pod.

1438
01:14:08,240 --> 01:14:11,280
Speaker 1: Thanks everybody, We hope you enjoyed that. Thanks for joining us.

1439
01:14:12,040 --> 01:14:12,800
Speaker 1: See you next time.

1440
01:14:17,720 --> 01:14:20,559
Speaker 2: For more science and curiosity, come find us on social

1441
01:14:20,640 --> 01:14:25,519
Speaker 2: media where we answer questions and post videos. We're on Twitter, Discorg, Insta,

1442
01:14:25,640 --> 01:14:29,360
Speaker 2: and now TikTok. Thanks for listening and remember that Daniel

1443
01:14:29,360 --> 01:14:32,800
Speaker 2: and Jorge Explain the Universe is a production of iHeartRadio.

1444
01:14:33,120 --> 01:14:38,200
Speaker 2: For more podcasts from iHeartRadio, visit the iHeartRadio app, Apple Podcasts,

1445
01:14:38,400 --> 01:14:40,719
Speaker 2: or wherever you listen to your favorite shows.