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Speaker 1: Hey or hey, do you think science is good at

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Speaker 1: predicting the future.

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Speaker 4: It doesn't do very well with predicting the weather? Does

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Speaker 4: it well?

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Speaker 1: Here in California? Just says it's sunny every day?

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Speaker 4: Yeah, I guess that some states are easier to predict

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Speaker 4: the weather.

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Speaker 1: All right, but let me try my hand at it.

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Speaker 1: I have a prediction of the future.

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Speaker 4: Ooh, you're a psychic now and a physicist.

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Speaker 1: Well, I predict that you cannot go a whole episode

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Speaker 1: without talking about bananas.

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Speaker 4: What it's bananas?

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Speaker 1: Boom, there you go. I'm totally psychic.

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Speaker 4: You know the difference between a psychic and a physicist

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Speaker 4: their salary where you put to h that's the only difference. Hey,

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Speaker 4: im Jorge, I'm my cartoonist and the creator of PhD Comics.

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Speaker 1: Hi. I'm Daniel. I'm a particle physicist, not a particle psychic,

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Speaker 1: and I'm a co author of our book We Have

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Speaker 1: No Idea, A Guide to the Unknown Universe.

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Speaker 4: Welcome to our podcast, Daniel and Jorge Explain the Universe,

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Speaker 4: a production of iHeartRadio.

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Speaker 1: In which we talk about all the amazing, the fascinating,

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Speaker 1: the hot, the nasty, the wet, the bright, the dirty,

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Speaker 1: the soft, the quiet, the loud, the hot, the cold,

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Speaker 1: all the stuff in the universe that's fascinating and beautiful

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Speaker 1: and amazing and explain it to you in a way

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Speaker 1: that we hope you actually understand and hopefully even enjoy.

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Speaker 4: Yeah, And sometimes on the podcast, we like to talk

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Speaker 4: about predicting the future. What's going to happen, what's going

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Speaker 4: to happen to the universe, what's going to happen to

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Speaker 4: the Earth, what's going to happen to the Solar system,

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Speaker 4: what's going to happen to this podcast?

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Speaker 1: Where is this podcast going anyway? Thank Yeah, we like

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Speaker 1: to talk about what science does and doesn't know about

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Speaker 1: the future, what we can predict about what's going to happen,

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Speaker 1: and where we are totally clueless.

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Speaker 4: Yeah. Well, as as we were saying earlier, there's a

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Speaker 4: there's a fine line between being a psychic and being

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Speaker 4: a physicist.

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Speaker 1: I would say it's a bright but fine line.

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Speaker 4: It's bright, Okay, it's an impenetrable barrier, quantum barrier.

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Speaker 1: It's difficult to tunnel between being a physicist and being

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Speaker 1: a psychic. But in some sense we do have the

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Speaker 1: same job. Physics also wants to predict the future.

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Speaker 4: Yeah, that's kind of I mean, that's kind of why

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Speaker 4: in a way, physics was invented, right, Like, we want

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Speaker 4: to know where this catapult payload is going to land.

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Speaker 4: We want to know how far my car is going

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Speaker 4: to go.

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Speaker 1: Were you there when physics was invented. I didn't get

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Speaker 1: invited to that meeting.

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Speaker 4: Oh yeah, you didn't. I think Einstein was there and

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Speaker 4: Newton was there.

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Speaker 1: And damn I knew I should have checked my email

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Speaker 1: that day.

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Speaker 4: He missed the calendar invite.

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Speaker 1: That's right, And all those examples you mentioned are totally valuable,

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Speaker 1: and our examples of why science has an impact on

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Speaker 1: everyday life. You know, is my catapult is going to

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Speaker 1: get over those castle walls and this kind of stuff.

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Speaker 1: But also physics just wants to understand what's going to

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Speaker 1: happen to us, what's our fate? How can we plan

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Speaker 1: to live ahead? And can we understand the mysteries of

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Speaker 1: everything around us so that we can know when lightning

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Speaker 1: is going to strike or when disease is going to

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Speaker 1: wipe out our cattle.

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Speaker 4: Right, in a way, that's kind of the standard for physics, right,

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Speaker 4: It's like we say we understand something if in a

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Speaker 4: way we can sort of predict what's going to happen.

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Speaker 1: You want to verify that your theory of physics describes

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Speaker 1: the real universe out there and not just some idea

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Speaker 1: in your mind. You have to make a testable prediction. Einstein,

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Speaker 1: for example, predicted how light was going to be bent

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Speaker 1: during an eclipse, and people measured it and he was right.

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Speaker 1: And that's really when people started to believe his theory

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Speaker 1: or relativity, because if your theory can't predict the future,

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Speaker 1: then what use is it. Right, that's what science does.

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Speaker 1: It predicts the outcome of future experiments.

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Speaker 4: Yeah, and you know, I think people are comfortable with

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Speaker 4: this idea that physics is able to some degree predict

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Speaker 4: the future. Like if you told somebody, hey, physics predicts

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Speaker 4: that the Earth is going to be around for a

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Speaker 4: very long time, or that the universe will never end,

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Speaker 4: that those are comforting.

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Speaker 1: Or if you eat that candy you will get fat.

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Speaker 4: Yeah. I think that's more of a physical education.

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Speaker 1: There's physics there. You're converting candy energy into squishy stomach energy.

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Speaker 4: And yeah, So we're comfortable, I think with some physics

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Speaker 4: predicting some things about the future, But I think we're

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Speaker 4: a little bit uncomfortable about physics describing other things about

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Speaker 4: the future, right.

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Speaker 1: Yeah. We like to use physics and science to explore

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Speaker 1: the universe around us and outside us, But then sometimes

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Speaker 1: we turn that science on ourselves and we seek to

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Speaker 1: gain insight into how we work and then that makes

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Speaker 1: you wonder.

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Speaker 4: Yeah, it makes you wonder if you are predictable in

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Speaker 4: a way, you know, like could physics potentially, one day,

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Speaker 4: you know, simulate the human mind or simulate your mind

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Speaker 4: and predict what you're going to think and do.

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Speaker 1: It's a fascinating question because so many things that humans

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Speaker 1: have puzzled over for so many years, how eclipses happen,

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Speaker 1: where lightning strikes, all this stuff, how reproduction works. All

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Speaker 1: of these things have in the end been explained through science.

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Speaker 1: It turns out they are mechanistic. We can understand the

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Speaker 1: microscopically how it works and predict what's going to happen

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Speaker 1: in the future. And so then it's a natural question

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Speaker 1: to wonder how far you can extend that strategy. Can

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Speaker 1: you turn that around and extend it into your own

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Speaker 1: inner life?

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Speaker 4: Right? Yeah, because you know, like, if physics can predict what,

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Speaker 4: you know, a can of gas particles is going to do,

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Speaker 4: why can't it predict what a brain full of neurons

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Speaker 4: is going to do?

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Speaker 1: What's the difference in the end between a can of

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Speaker 1: gas and a brain of neurons.

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Speaker 4: Really depends on the person probably, but.

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Speaker 1: Depends on what that person ate recently.

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Speaker 4: Yeah, so it's a big question with I think some

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Speaker 4: really deep philosophical implications right about who we are and

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Speaker 4: whether or not we're predictable, or whether or not we

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Speaker 4: have this thing called free will.

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Speaker 1: Yeah, that's right. Like many the topics we touch on

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Speaker 1: in this podcast, there are deep philosophical implications, and so

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Speaker 1: I think today we should walk carefully and focus on

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Speaker 1: the science and then think about what the philosophical implications

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Speaker 1: are of what we do and do not know.

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Speaker 4: Yeah, and so today on the podcast, we'll be tackling

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Speaker 4: the question can't signs predict what you're going to do?

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Speaker 4: And I predict that there will be a lot of

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Speaker 4: predictions in this episode, but maybe not a lot of answers.

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Speaker 1: Well that's sort of our specialty, right, opening questions and

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Speaker 1: not really answering them.

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Speaker 4: Well, I think what you mean is getting people excited

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Speaker 4: about the questions, right, And.

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Speaker 1: That's right, sparking people's fundamental curiosity. I think it's I

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Speaker 1: think it's wonderful to talk about things that we don't

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Speaker 1: understand very well because hopefully it's a preview for what's

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Speaker 1: going to happen in the future. It's like a fantasy

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Speaker 1: for future science, maybe in five hundred years or in

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Speaker 1: a thousand years, science will have figured out the exact

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Speaker 1: workings of the human brain and can tell you exactly

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Speaker 1: what you're going to do tomorrow. That would be amazing,

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Speaker 1: would totally change the way life operates, what it's like

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Speaker 1: to be a human being.

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Speaker 4: Right, Yeah, I mean, we wrote a whole book about

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Speaker 4: all the things we don't know, Daniel, I'm sure we

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Speaker 4: can pull off.

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Speaker 1: A podcast also, I predict we will.

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Speaker 5: So.

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Speaker 4: Yeah. So this is an interesting question, and it kind

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Speaker 4: of goes back to a long time ago, when you know,

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Speaker 4: once signs started seeing that the universe was what they

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Speaker 4: call deterministic, meaning like like just a giant machine that

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Speaker 4: follows the laws of physics like a clock, then I

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Speaker 4: think that's when people started to think, like, hey, maybe

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Speaker 4: maybe our humanity is also predictable like a clock.

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Speaker 1: Yeah. I think that's sort of shocking. I think when

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Speaker 1: people first had that idea, it might have been a

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Speaker 1: terrifying to imagine that this experience they're having might actually

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Speaker 1: just be explainable, that things could be determined from the past.

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Speaker 4: Yeah. Well, I think if you told my eleven year

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Speaker 4: old self that I was a robot i'd be like cool. Nowadays, though,

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Speaker 4: you know, it's more of an uncomfortable statement.

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Speaker 1: You're a robot and we gave the remote control to

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Speaker 1: your sister.

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Speaker 4: Sorry, yeah, no.

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Speaker 1: I think also it's fun to think even more deeply

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Speaker 1: into sort of the origins of human thought on this question.

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Speaker 1: Is the sort of the hubris that the universe is

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Speaker 1: explainable at any level? I think a thousand years ago

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Speaker 1: or five thousand years ago, people might have been comfortable

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Speaker 1: with the idea that the universe doesn't follow laws, It

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Speaker 1: just sort of is, and maybe there are these omniscient

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Speaker 1: sentient beings out there that are in control of stuff

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Speaker 1: and they can do whatever they like. So the idea

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Speaker 1: that the universe that we could write down rules, we

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Speaker 1: could discover rules that the universe follows and use those

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Speaker 1: to predict what's going to happen. That's an incredible step

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Speaker 1: forward in human intellectual history. But also it's not something

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Speaker 1: we can necessarily explain, like why would the universe be deterministic?

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Speaker 1: Why would the universe even follow laws? Is it true

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Speaker 1: even today, given our amazing success in science, is it

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Speaker 1: true that everything we can discover, every natural phenomenon we discover,

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Speaker 1: will eventually be explained by science or can be explained

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Speaker 1: by science. Those are open questions in philosophy.

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Speaker 4: Yeah, and so I guess the question then is, you know,

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Speaker 4: how far can we push science? Like if science can

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Speaker 4: predict the future to some degree, like you know, it

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Speaker 4: can predict that the airplane is not going to fall

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Speaker 4: from the sky, or it's going to predict that, you know,

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Speaker 4: if I shoot this laser, this is what's going to happen.

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Speaker 4: You know, how far can we push the science and

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Speaker 4: maybe even predict what your brain is going to do?

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Speaker 1: Fascina any questions? And I think you'll see from some

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Speaker 1: of these reactions that there's a wide variety of opinions

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Speaker 1: out there. So as usual, I walked around campus that

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Speaker 1: you see Irvine, and I asked folks this question.

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Speaker 4: Can science predict what you're going to think or do?

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Speaker 4: So think about it for a second. Do you think

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Speaker 4: you're just a big squeishy biological robot machine or do

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Speaker 4: you think you have some sort of free will or

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Speaker 4: some sort of free spirit that nobody can predict what

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Speaker 4: you're going to think or do. Here's what people had

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Speaker 4: to say.

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Speaker 1: Not precisely to an extent. There's just so many variables.

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Speaker 6: No, No, because we're humans are unpredictable. I'm a psychology major.

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Speaker 6: So yeah, I don't believe that we can. We can try,

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Speaker 6: but it's not likely.

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Speaker 1: Yeah, I think so. Do you think so?

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Speaker 6: Yeah?

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Speaker 1: So then is there free will in that case? Like

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Speaker 1: are you making decisions?

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Speaker 7: Yeah, because it's still the person itself, that's still it's

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Speaker 7: just the other person that's predicting correctly, So.

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Speaker 4: It's not their choice theoretically. I mean, if we did

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Speaker 4: have those capabilities, then that might be possible.

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Speaker 8: We just need to advance sciences hard enough to be

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Speaker 8: able to figure that out.

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Speaker 4: No, don't. I don't think so.

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Speaker 1: So do you think the brain is like not described

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Speaker 1: by physical laws or does something else happening there?

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Speaker 5: No?

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Speaker 9: I think strong determinism I think is a very uh optimistic.

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Speaker 7: Yeah you do?

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Speaker 4: Yeah?

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Speaker 10: Why is that?

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Speaker 6: I don't know.

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Speaker 4: I just feel like it could And if.

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Speaker 1: It does, does that mean that you have free will

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Speaker 1: or don't have free will?

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Speaker 4: Or if you can predict what people are gonna think?

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Speaker 11: Then probably not?

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Speaker 10: Yes?

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Speaker 1: Yes?

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Speaker 10: Why is that?

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Speaker 7: I don't know.

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Speaker 5: I think I saw like a video where they're like

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Speaker 5: trying to map out like a brain or something, and

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Speaker 5: they were talking about like how to recreate it, and

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Speaker 5: that everything we think is just like a series of decisions,

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Speaker 5: and that you can put it in like binary or

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Speaker 5: something so like zero for yes or one for no,

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Speaker 5: and it all leads to what choices you make.

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Speaker 10: I think that if there were a method for it,

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Speaker 10: it wouldn't be super exact, just because there were so

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Speaker 10: many factors that play into it.

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Speaker 4: I don't think it's possible to be able to predict

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Speaker 4: every single.

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Speaker 6: One of them.

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Speaker 1: Yes, yes, Why is that?

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Speaker 6: I don't know?

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Speaker 4: Uh?

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Speaker 1: No, No, So you don't think the brain follows physical laws.

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Speaker 9: I think it does, but I think they're chaotic, chaotic.

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Speaker 1: Or random given enough information. Are you just a complicated

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Speaker 1: mechanical watch.

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Speaker 9: No, I think I may or may not subscribe to

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Speaker 9: the view that there's some quantum mechanics at play. So

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Speaker 9: maybe maybe you're not.

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Speaker 1: Speaking quantum mechanics gives us an opening for free will.

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Speaker 9: No, I don't know if that's true. I'm a pessimist.

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Speaker 7: I feel like eventually, yes, but I feel like we're

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Speaker 7: not there yet.

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Speaker 1: So if so, does that mean that your brain is

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Speaker 1: deterministic but you're just just a product of what's happened

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Speaker 1: to in the past and the stimulus it's getting.

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Speaker 10: Obviously, there's like some varying factors.

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Speaker 6: But I think there are some things that are like patronistic,

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Speaker 6: like if you're going to wake up and like drink

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Speaker 6: a coffee, or if you're going to wake up and

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Speaker 6: like go for a walk with Those things are patterns.

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Speaker 10: That like are habitual, but then there's things that aren't.

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Speaker 11: I feel like you could predict it to an extent,

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Speaker 11: but not like every single action by action, like there

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Speaker 11: might be like faults in the prediction.

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Speaker 1: But yeah, it's hard to do because it's impossible.

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Speaker 11: Just I feel like that's probably impossible because your things can.

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Speaker 8: Change, you know, a very complicated robot.

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Speaker 1: Does that mean that there is no free will?

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Speaker 9: I am currently thinking that there's no free will.

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Speaker 7: Yes, yeah, So maybe that's where it becomes a bit

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Speaker 7: complicated to answer this question, because I do believe that

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Speaker 7: the laws of physics govern everything in the universe, so

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Speaker 7: that would include us.

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Speaker 4: All right. I feel like these answers were all very

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Speaker 4: yes and no. You know, some people were like no,

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Speaker 4: some people were like yes. Nobody said like maybe I

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Speaker 4: don't know, you know, and like people had very strong opinions.

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Speaker 1: They certainly did, and I should have taken some data

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Speaker 1: to see if these were like all science majors who

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Speaker 1: had confidence as science would eventually figure it out or not.

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Speaker 1: But the last two were maybe the most fun because

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Speaker 1: they turned out to be a husband and wife and

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Speaker 1: he said, yes, he's a complicated robot and she says, no,

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Speaker 1: we're not robots. And then after I left, I heard

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Speaker 1: them arguing about it in a good natured way. In

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Speaker 1: a good natured way.

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Speaker 4: Oh oh, I see one of them thought that the

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Speaker 4: other one was predictable and the other one did not

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Speaker 4: like to be predicted.

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Speaker 8: Yeah.

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Speaker 1: I thought that was fascinating. Maybe sparked some dinner table conversation,

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Speaker 1: But you're right, And in contrast to some other questions,

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Speaker 1: this is definitely a topic everybody felt comfortable giving an

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Speaker 1: answer to. Sometimes I'll ask people a question and they'll

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Speaker 1: be like, what, I never heard of that before, or

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Speaker 1: I don't know, But here everybody had something thoughtful to say.

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Speaker 4: Yeah, because I think it touches something very deep within us,

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Speaker 4: you know, just this idea that there's something more to

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Speaker 4: me than just like a big biological clock or a

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Speaker 4: big biological you know, clump of cells doing what there

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Speaker 4: would do without thinking about it.

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Speaker 1: Yeah, I think most people feel like they are steering,

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Speaker 1: even if your body is a big biological robot. They

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Speaker 1: feel like they're in charge. They're making choices. They decide

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Speaker 1: to eat that cookie, or they decide to step on

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Speaker 1: that crack. They feel like they're making these decisions. And

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Speaker 1: so it doesn't sort of jibe with that experience to

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Speaker 1: imagine that those decisions are just the product of the

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Speaker 1: situation you were in an instant before. It's hard to

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Speaker 1: imagine how you could have such a visceral experience of

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Speaker 1: free will if you don't actually have it.

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Speaker 4: All right, so let's dig into the question. Here is

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Speaker 4: the question here, which is could science predict what you're

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Speaker 4: going to do? And so let's maybe paint a picture

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Speaker 4: to our listeners about what that might look like, you know, like,

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Speaker 4: how could science, physics, or you know, a combination of

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Speaker 4: biology and computer science possibly predict what a human brain

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Speaker 4: might think or do.

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Speaker 1: Yeah. So, the typical strategy for understanding something is to

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Speaker 1: think about it in terms of its microscopic bits, like

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Speaker 1: what's going on inside of it? Can we understand those

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Speaker 1: bits and from that build up some sort of understanding,

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Speaker 1: you know, like, if you wanted to say understand how

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Speaker 1: a watch worked, you would take it apart and you

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Speaker 1: would say, oh, there's a gear here, and there's a

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Speaker 1: lever gear, and this lever touches that gear which turns

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Speaker 1: this thing, and that's how this thing works. And if

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Speaker 1: you can make a model of all the things inside

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Speaker 1: of it, and each one of those things is following

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Speaker 1: the laws of physics, then together the whole thing has

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Speaker 1: to follow the laws of physics. Right. If something is

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Speaker 1: made up of pieces which are predictable, then putting them

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Speaker 1: together it should also be predictable.

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Speaker 4: Right you mean, like, so science, what might be Maybe

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Speaker 4: break down your brain and maybe build like a computer

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Speaker 4: model of each one of your neurons, and that somehow,

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Speaker 4: you know, acts exactly the same way that it's put together,

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Speaker 4: the exact same way that your brain is. And so

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Speaker 4: maybe like if you capture your brain in a computer,

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Speaker 4: could that maybe predict what you're thinking, what you'll think

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Speaker 4: and do.

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Speaker 1: Yeah, And it doesn't have to be a computer model

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Speaker 1: in the end, it just has to follow the mathematical

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Speaker 1: laws of physics, and you can express those as a

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Speaker 1: computer model. You could also build a mechanical model, right,

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Speaker 1: you can imagine building basically a physical copy of your brain.

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Speaker 1: So that's I think that detail is not critical the concept.

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Speaker 1: The critical concept is understanding what's going on inside the

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Speaker 1: little bits and then putting those together to basically replicate

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Speaker 1: your brain. But replicating is not enough, Like if I

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Speaker 1: had a perfect copy of your brain, Like if I

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Speaker 1: created another Jorge, that wouldn't necessarily help me understand what

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Speaker 1: you're going to do. In order to in order to

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Speaker 1: predict what you're going to do, I need to be

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Speaker 1: able to run experiments. And so yeah, having a simulation

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Speaker 1: of your brain, I could like say, well, what would

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Speaker 1: Jorge do if I offered him a cookie? Would he

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Speaker 1: say yes or no? So if I had a perfect

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Speaker 1: simulation of your brain, I could run those experiments.

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Speaker 4: Right, Yeah, And I think it goes to the idea that,

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Speaker 4: you know, we're really complicated as human beings, as thinking beings.

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Speaker 4: But you know, if you break down our brain, it's

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Speaker 4: made out of you know, lobes and chunks of brain tissue,

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Speaker 4: and those are made out of neurons connected to each other,

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Speaker 4: and neurons are made out of molecules, and so all

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Speaker 4: of these things down to the molecule level, kind of

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Speaker 4: follow the laws of physics, you know. It is sort

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Speaker 4: of at the end, just a big and very very complicated,

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Speaker 4: but still a big clock.

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Speaker 1: Yes, And that's a really deeply powerful implication of this

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Speaker 1: discovery that we made a long, long time ago that

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Speaker 1: everything is made out of the same bits. Right, I'm

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Speaker 1: made out of atoms. You're made out of atoms. This

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Speaker 1: chair I'm sitting on is made out of atoms. Things

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Speaker 1: are not made out of their own kind of stuff,

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Speaker 1: which means that in the end, they all follow the

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Speaker 1: same rules. The rules that hold the chair together are

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Speaker 1: the same rules that hold your cat together and your

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Speaker 1: hamster together and you together. So if we can figure

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Speaker 1: out what the rules are that describe how molecules and

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Speaker 1: atoms work, that, in principle, and that's an important distinction principle,

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Speaker 1: we should be able to extrapolate up and understand how

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Speaker 1: you work.

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Speaker 4: Right. It's kind of like saying, like, you know, we

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Speaker 4: are made out of inanimate bids, you know, think we're

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Speaker 4: made out of things that are just plain things which

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Speaker 4: you can maybe predict what they're going to do. And

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Speaker 4: so does that mean that we are also inanimate in

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Speaker 4: a way and.

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Speaker 1: Predictable imagine you came across a robot, for example, and

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Speaker 1: it was doing weird stuff. You want to understand it.

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Speaker 1: How do you understand it?

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Speaker 6: What?

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Speaker 1: You would take it apart and say, oh, it's made

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Speaker 1: out of these pieces, and can I understand each of

445
00:21:31,000 --> 00:21:33,480
Speaker 1: those pieces, and if so, you could put that together

446
00:21:33,560 --> 00:21:36,000
Speaker 1: to an understanding of the whole robot. And so the

447
00:21:36,040 --> 00:21:37,920
Speaker 1: idea is apply that to a person.

448
00:21:38,000 --> 00:21:39,600
Speaker 4: Yeah, and you could do things like, you know, if I,

449
00:21:40,280 --> 00:21:42,679
Speaker 4: you know, if I poke this little bit here in

450
00:21:42,680 --> 00:21:44,960
Speaker 4: this engine, you know, I predict that the end the

451
00:21:45,080 --> 00:21:48,120
Speaker 4: car is going to move forward or backwards exactly.

452
00:21:48,440 --> 00:21:51,919
Speaker 1: And all of this happening, of course, assumes a few things.

453
00:21:52,200 --> 00:21:55,000
Speaker 1: It assumes that you can get a picture of what's

454
00:21:55,080 --> 00:21:58,160
Speaker 1: what's happening inside your brain. The idea you presented requires

455
00:21:58,240 --> 00:22:00,199
Speaker 1: that we know each and ir on. We know the

456
00:22:00,240 --> 00:22:03,560
Speaker 1: situation that neuron is in, right, It's like for the watch,

457
00:22:03,600 --> 00:22:06,000
Speaker 1: it's like that we know where the levers are and

458
00:22:06,000 --> 00:22:10,159
Speaker 1: where the gears are, and that we can extrapolate up

459
00:22:10,200 --> 00:22:12,399
Speaker 1: from those levers and gears to the operation of a

460
00:22:12,440 --> 00:22:15,240
Speaker 1: whole brain. That's not trivial. And then you know, there's

461
00:22:15,280 --> 00:22:18,480
Speaker 1: questions about like is it actually deterministic or is there

462
00:22:18,520 --> 00:22:21,480
Speaker 1: like some funky quantum magic going on, or is there

463
00:22:21,520 --> 00:22:24,400
Speaker 1: something in there that like science can describe. So it's

464
00:22:24,400 --> 00:22:26,520
Speaker 1: not a trivial thing to say just just because your

465
00:22:26,560 --> 00:22:29,119
Speaker 1: brain is made of atoms that we could therefore predict

466
00:22:29,119 --> 00:22:29,600
Speaker 1: what you're.

467
00:22:29,440 --> 00:22:29,800
Speaker 6: Going to do.

468
00:22:30,040 --> 00:22:34,640
Speaker 4: Right, Yeah, I think that's at the core question here,

469
00:22:35,000 --> 00:22:37,440
Speaker 4: and so let's get into it. And just to save

470
00:22:37,480 --> 00:22:39,679
Speaker 4: you some time, Daniel, I will always take the cookie.

471
00:22:39,880 --> 00:22:42,560
Speaker 4: You don't need a fancy computer simulation to predict that.

472
00:22:43,480 --> 00:22:44,600
Speaker 4: I will take the cookie.

473
00:22:45,119 --> 00:22:46,720
Speaker 1: All right, I'm going to cross that off my Deep

474
00:22:46,800 --> 00:22:47,960
Speaker 1: Questions of the Universe list.

475
00:22:48,359 --> 00:22:50,280
Speaker 4: Yeah, so let's get into it a little bit more

476
00:22:50,760 --> 00:22:53,040
Speaker 4: deeply and figure out how you might even do this,

477
00:22:53,160 --> 00:22:55,439
Speaker 4: or whether we can or if there is some kind

478
00:22:55,480 --> 00:22:58,239
Speaker 4: of quantum magic that might give us a little bit

479
00:22:58,240 --> 00:23:00,840
Speaker 4: of a loophole to get free will. But first, let's

480
00:23:00,880 --> 00:23:01,719
Speaker 4: take a quick break.

481
00:23:05,800 --> 00:23:08,720
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Speaker 4: All right, we're talking about whether science can predict what

530
00:25:47,160 --> 00:25:50,080
Speaker 4: you're going to think and do, and we talked about, yeah,

531
00:25:50,080 --> 00:25:52,240
Speaker 4: that maybe your brain is made out of little bits

532
00:25:52,240 --> 00:25:55,080
Speaker 4: and pieces which which are sort of little biological machines,

533
00:25:55,119 --> 00:25:57,399
Speaker 4: and so does that mean that your whole brain is

534
00:25:57,520 --> 00:26:01,160
Speaker 4: just a machine or like a robot, which anyone could

535
00:26:01,280 --> 00:26:03,800
Speaker 4: predict what they're going to do. So let's get into it.

536
00:26:03,920 --> 00:26:06,600
Speaker 4: What are some of the things Daniel, that physicists know

537
00:26:07,000 --> 00:26:11,320
Speaker 4: or think that might make that picture really difficult or

538
00:26:11,440 --> 00:26:14,320
Speaker 4: maybe impossible to predict what you're going to do.

539
00:26:14,600 --> 00:26:17,000
Speaker 1: The first thing that comes to my mind is just

540
00:26:17,560 --> 00:26:20,600
Speaker 1: knowing the current status of your brain. Like if I

541
00:26:20,640 --> 00:26:24,560
Speaker 1: wanted to model the flight of a of a baseball,

542
00:26:24,800 --> 00:26:26,920
Speaker 1: if I wanted to know exactly where a baseball went,

543
00:26:27,440 --> 00:26:30,440
Speaker 1: I would need to know its current position and its

544
00:26:30,440 --> 00:26:31,119
Speaker 1: current direction.

545
00:26:31,359 --> 00:26:33,120
Speaker 4: If you wanted to know if you wanted to predict

546
00:26:33,119 --> 00:26:35,680
Speaker 4: where what's going to land, you would sort of need

547
00:26:35,720 --> 00:26:38,000
Speaker 4: to know, you know where it is and where it's

548
00:26:38,040 --> 00:26:39,040
Speaker 4: going at any given time.

549
00:26:39,320 --> 00:26:42,320
Speaker 1: Yeah, and any moment, just one moment in time, would

550
00:26:42,359 --> 00:26:44,399
Speaker 1: determine where it's going to be. So if I knew

551
00:26:44,560 --> 00:26:47,160
Speaker 1: where that baseball was now and where it was which

552
00:26:47,200 --> 00:26:49,440
Speaker 1: direction it was going in, I could just apply the

553
00:26:49,520 --> 00:26:53,000
Speaker 1: law of physics and propagate that information forward. I would say, oh,

554
00:26:53,000 --> 00:26:55,960
Speaker 1: it's going to move in a certain path under gravity,

555
00:26:56,160 --> 00:26:57,880
Speaker 1: and I can tell you exactly where it's going to land.

556
00:26:57,920 --> 00:27:01,240
Speaker 1: That's physics predicting the future and for one little tiny

557
00:27:01,240 --> 00:27:04,639
Speaker 1: particle or baseball or whatever. But in order to do

558
00:27:04,680 --> 00:27:06,439
Speaker 1: that for your brain, I would need to know the

559
00:27:06,560 --> 00:27:10,760
Speaker 1: current situation of your brain, Like, what is the situation

560
00:27:10,840 --> 00:27:13,800
Speaker 1: of every neuron? Is it about to fire a little pulse?

561
00:27:13,920 --> 00:27:16,000
Speaker 1: Is it not about to fire a pulse? How strong

562
00:27:16,119 --> 00:27:19,040
Speaker 1: is this connection between it and the next neuron? And

563
00:27:19,080 --> 00:27:21,880
Speaker 1: you know there's billions and billions of neurons in your brain,

564
00:27:21,920 --> 00:27:24,360
Speaker 1: so we're talking about an enormous amount of information you'd

565
00:27:24,400 --> 00:27:24,880
Speaker 1: need to know.

566
00:27:25,080 --> 00:27:26,479
Speaker 4: I think what you mean is that even if I

567
00:27:26,520 --> 00:27:29,840
Speaker 4: had a giant computer simulation of your brain, I would

568
00:27:29,880 --> 00:27:31,800
Speaker 4: need to give each of the neurons kind of a

569
00:27:31,840 --> 00:27:34,479
Speaker 4: starting value, right, or like I would need to know

570
00:27:35,280 --> 00:27:38,480
Speaker 4: where each neuron was in order to predict what the

571
00:27:38,520 --> 00:27:39,560
Speaker 4: whole brain was going to do.

572
00:27:39,960 --> 00:27:42,800
Speaker 1: Precisely, if I had a computer program that could simulate

573
00:27:42,840 --> 00:27:45,320
Speaker 1: an arbitrary brain, I would somehow need to configure it

574
00:27:45,359 --> 00:27:48,040
Speaker 1: to your brain, and that would mean knowing where all

575
00:27:48,080 --> 00:27:50,080
Speaker 1: the neurons were and how they were connected to each

576
00:27:50,119 --> 00:27:52,800
Speaker 1: other and all that kind of stuff. Just like if

577
00:27:52,840 --> 00:27:54,800
Speaker 1: you want to calculate where the baseball is going to go,

578
00:27:55,000 --> 00:27:56,960
Speaker 1: you need to know the initial conditions. You need to

579
00:27:56,960 --> 00:27:58,960
Speaker 1: know where it is now and which direction it is

580
00:27:59,000 --> 00:28:02,320
Speaker 1: going in. Like, even if I had that computer program,

581
00:28:02,400 --> 00:28:05,320
Speaker 1: and even if it was possible to use that computer

582
00:28:05,359 --> 00:28:07,720
Speaker 1: program to predict the future, how do I get that

583
00:28:07,800 --> 00:28:10,680
Speaker 1: information from your brain? Do I have to like scan

584
00:28:10,880 --> 00:28:13,000
Speaker 1: your brain somehow? Do I have to take your head

585
00:28:13,000 --> 00:28:16,560
Speaker 1: off and slice it into hyperfine little bits? Like how

586
00:28:16,560 --> 00:28:18,040
Speaker 1: do you even physically do that?

587
00:28:18,359 --> 00:28:21,600
Speaker 4: Right? Because each neuron might be in a different state,

588
00:28:21,680 --> 00:28:24,560
Speaker 4: you know, like one who might be more excited than others,

589
00:28:24,640 --> 00:28:26,639
Speaker 4: or you might be you know, you might be in

590
00:28:26,680 --> 00:28:29,720
Speaker 4: a bad mood, and so there's a general know, dopamine

591
00:28:29,800 --> 00:28:33,560
Speaker 4: levels that are you know, suppressing some of your neurons, right, now.

592
00:28:33,560 --> 00:28:35,440
Speaker 4: You need to know that in order to predict what

593
00:28:35,480 --> 00:28:36,240
Speaker 4: you're going to think and do.

594
00:28:36,440 --> 00:28:37,840
Speaker 1: Yeah, and you need to know it all at the

595
00:28:37,880 --> 00:28:40,080
Speaker 1: same moment, right I don't want to know what the

596
00:28:40,160 --> 00:28:41,800
Speaker 1: left half of your brain is doing right now and

597
00:28:41,840 --> 00:28:43,800
Speaker 1: the right half of your brain is doing two seconds ago.

598
00:28:44,040 --> 00:28:47,720
Speaker 1: I need a complete picture of your brain at one moment,

599
00:28:47,840 --> 00:28:50,760
Speaker 1: so that I know how what direction everything is moving in.

600
00:28:51,480 --> 00:28:53,080
Speaker 1: I'm just thinking of your brain as like a lot

601
00:28:53,160 --> 00:28:55,840
Speaker 1: of tiny little baseballs, and so I need to know

602
00:28:55,840 --> 00:28:58,080
Speaker 1: where all those baseballs are at the same time. So

603
00:28:58,160 --> 00:29:01,480
Speaker 1: even slicing your brain like a ham sandwich or whatever

604
00:29:01,720 --> 00:29:05,080
Speaker 1: wouldn't work. I need to somehow instantly scan everything in

605
00:29:05,120 --> 00:29:05,600
Speaker 1: your brain.

606
00:29:07,080 --> 00:29:11,120
Speaker 4: And that just seems almost impossible, right.

607
00:29:11,880 --> 00:29:13,880
Speaker 1: It seems almost impossible. I mean I read a lot

608
00:29:13,960 --> 00:29:16,960
Speaker 1: of science fiction, and there's some good stuff where the

609
00:29:17,000 --> 00:29:20,080
Speaker 1: people are uploading brains into the cloud, and sometimes they

610
00:29:20,120 --> 00:29:22,720
Speaker 1: talk about how scanning the brain is a destructive process,

611
00:29:22,840 --> 00:29:25,040
Speaker 1: but they never talk about how fast it has to be. Like,

612
00:29:25,080 --> 00:29:26,800
Speaker 1: if you want to scan the brain, it's got to

613
00:29:26,840 --> 00:29:29,240
Speaker 1: be a snapshot. You got to know where everything is

614
00:29:29,360 --> 00:29:33,200
Speaker 1: in an instant or it's all averaged like over the

615
00:29:33,280 --> 00:29:35,880
Speaker 1: last five minutes or ten minutes or twenty minutes or

616
00:29:35,920 --> 00:29:38,600
Speaker 1: however long it takes to scan the brain, and that's

617
00:29:38,640 --> 00:29:41,400
Speaker 1: going to be a disaster. So this is a technological issue,

618
00:29:41,440 --> 00:29:44,120
Speaker 1: not philosophical. But I don't know if that's even possible.

619
00:29:44,320 --> 00:29:46,680
Speaker 4: Oh I see, Okay, so that's kind of the first

620
00:29:47,440 --> 00:29:50,960
Speaker 4: barrier that might prevent you from predicting somebody's brain is

621
00:29:51,560 --> 00:29:55,120
Speaker 4: getting a snapshot of it. And it sounds impossible, but

622
00:29:55,160 --> 00:29:57,560
Speaker 4: I think technically you can't rule it out, right, Like,

623
00:29:57,640 --> 00:30:02,200
Speaker 4: it's not technically or theore ratically impossible that maybe one

624
00:30:02,280 --> 00:30:04,520
Speaker 4: day people will come up with a brain scan that

625
00:30:04,600 --> 00:30:07,320
Speaker 4: can somehow capture all of your brain in one instant.

626
00:30:07,400 --> 00:30:09,640
Speaker 1: That's right, unless you need to know it's sort of

627
00:30:09,640 --> 00:30:12,080
Speaker 1: at the level of quantum mechanical objects, like if you

628
00:30:12,120 --> 00:30:15,280
Speaker 1: need to know the exact location of every electron, then

629
00:30:15,320 --> 00:30:18,920
Speaker 1: it's theoretically impossible because you can't measure the perfect quantum

630
00:30:18,960 --> 00:30:21,520
Speaker 1: state of the entire brain all at the same time.

631
00:30:22,280 --> 00:30:24,600
Speaker 1: I think that's theoretically impossible, But as long as it's

632
00:30:24,600 --> 00:30:27,680
Speaker 1: you know, not necessarily sensitive to quantum effects in theory,

633
00:30:27,760 --> 00:30:31,760
Speaker 1: it's possible, but technologically way out of our grasp today today. Yeah,

634
00:30:31,840 --> 00:30:34,360
Speaker 1: let's assume that that's possible, and then think about what

635
00:30:34,400 --> 00:30:35,560
Speaker 1: the other problems would be.

636
00:30:35,720 --> 00:30:39,480
Speaker 4: Yeah, yeah, because that's that's not the only problem with

637
00:30:39,560 --> 00:30:40,280
Speaker 4: scanning your brain.

638
00:30:41,120 --> 00:30:43,960
Speaker 1: There are no first we have to achieve this almost

639
00:30:44,040 --> 00:30:47,800
Speaker 1: impossible sounding technological feat then we've got the real problem.

640
00:30:48,000 --> 00:30:50,200
Speaker 4: Yeah. Well, you know that's what they said about the iPhone.

641
00:30:50,280 --> 00:30:52,880
Speaker 4: And look where we are now, is that.

642
00:30:52,800 --> 00:30:55,760
Speaker 1: What they said about the iPhone was that this was

643
00:30:55,760 --> 00:30:57,600
Speaker 1: that at the physics meeting you had with Einstein and

644
00:30:57,640 --> 00:30:58,600
Speaker 1: Newton and all those guys.

645
00:30:58,680 --> 00:31:01,280
Speaker 4: Well it was science fiction fifty years ago, right, like

646
00:31:01,360 --> 00:31:04,080
Speaker 4: this device that fit in your pocket and you can

647
00:31:04,360 --> 00:31:07,760
Speaker 4: talk video with people and play addictive video games on

648
00:31:07,960 --> 00:31:10,360
Speaker 4: for hours. I mean, that was like a Star trek.

649
00:31:10,480 --> 00:31:13,960
Speaker 1: You know, that's true, And it'd be folly to say

650
00:31:14,040 --> 00:31:16,360
Speaker 1: this kind of technology will never be developed or would

651
00:31:16,360 --> 00:31:19,360
Speaker 1: take years or centuries or whatever, because those kind of

652
00:31:19,360 --> 00:31:22,640
Speaker 1: predictions are always people always look silly five years later

653
00:31:22,680 --> 00:31:25,320
Speaker 1: after making those predictions. So we should avoid that. And

654
00:31:25,400 --> 00:31:28,400
Speaker 1: maybe technology will progress rapidly and they'll invent the brain

655
00:31:28,440 --> 00:31:30,920
Speaker 1: scan next week and they'll have a brain scan app

656
00:31:31,080 --> 00:31:32,200
Speaker 1: that you can put on your phone.

657
00:31:32,520 --> 00:31:35,560
Speaker 4: Maybe, But then there are other things that would make

658
00:31:35,600 --> 00:31:38,320
Speaker 4: this really difficult or that kind of make your brain

659
00:31:39,000 --> 00:31:40,960
Speaker 4: almost pretty much unpredictable.

660
00:31:41,000 --> 00:31:43,360
Speaker 1: Right, Yeah, there are a lot of practical issues in

661
00:31:43,440 --> 00:31:46,400
Speaker 1: actually accomplishing predicting what your brain is going to do.

662
00:31:46,680 --> 00:31:48,760
Speaker 1: Say we had a snapshot of your brain, we knew

663
00:31:48,800 --> 00:31:51,200
Speaker 1: the current status of every neuron. We could load that

664
00:31:51,240 --> 00:31:53,840
Speaker 1: into the computer, and we wanted to run that forward.

665
00:31:53,880 --> 00:31:56,680
Speaker 1: We wanted to say, all right, well, what's this person

666
00:31:56,760 --> 00:32:00,160
Speaker 1: going to do in five minutes. That's not always easy.

667
00:32:00,440 --> 00:32:03,720
Speaker 1: Some systems, even if they're made of atoms which follow

668
00:32:03,760 --> 00:32:06,120
Speaker 1: physical laws, and even if you knew where all those

669
00:32:06,160 --> 00:32:08,680
Speaker 1: atoms were, are hard to describe.

670
00:32:09,040 --> 00:32:13,040
Speaker 4: Yeah, because they're you were telling me earlier, they're not

671
00:32:13,960 --> 00:32:17,479
Speaker 4: easy systems to kind of simulate, or they're not easy

672
00:32:17,520 --> 00:32:22,040
Speaker 4: systems to kind of predict what they're going to do.

673
00:32:22,200 --> 00:32:25,000
Speaker 1: Yeah, and this is again a technological problem, but it's

674
00:32:25,040 --> 00:32:27,720
Speaker 1: a real problem. Like why can't we predict the weather

675
00:32:28,240 --> 00:32:32,480
Speaker 1: In theory, weather follows rules that we understand water droplets

676
00:32:32,520 --> 00:32:35,400
Speaker 1: and air resistance and wind and all that stuff. That's

677
00:32:35,440 --> 00:32:36,600
Speaker 1: not complicated physics.

678
00:32:36,600 --> 00:32:37,360
Speaker 4: They're just atoms.

679
00:32:37,440 --> 00:32:39,680
Speaker 1: Yeah, they're just atoms. And we don't even need to

680
00:32:39,680 --> 00:32:41,360
Speaker 1: worry about the atoms. We can think about the water

681
00:32:41,440 --> 00:32:44,640
Speaker 1: droplets and the temperature and stuff. So why is it

682
00:32:44,680 --> 00:32:46,800
Speaker 1: so hard to predict when it's going to rain or

683
00:32:46,840 --> 00:32:48,840
Speaker 1: when a hurricane is going to come. If it's following

684
00:32:48,880 --> 00:32:51,600
Speaker 1: the laws of physics, and we have amazing satellites that

685
00:32:51,640 --> 00:32:54,360
Speaker 1: are like gathering all this data about what the temperature

686
00:32:54,400 --> 00:32:57,960
Speaker 1: is everywhere, it's a very similar problem. The reason that

687
00:32:58,120 --> 00:33:00,280
Speaker 1: weather is hard to predict is not because because it

688
00:33:00,280 --> 00:33:02,880
Speaker 1: doesn't follow the laws of physics. It's because it's very,

689
00:33:03,040 --> 00:33:07,320
Speaker 1: very sensitive to the exact tiny details of the situation.

690
00:33:07,680 --> 00:33:10,560
Speaker 1: A small change in temperature over here leads to a

691
00:33:10,600 --> 00:33:13,600
Speaker 1: big change over there. That's something we call chaos.

692
00:33:13,800 --> 00:33:16,320
Speaker 4: Right. It's this idea that kind of goes back to

693
00:33:16,360 --> 00:33:20,640
Speaker 4: that whole butterfly idea, right, Like they say that if

694
00:33:20,640 --> 00:33:23,440
Speaker 4: a butterfly flaps its wing in one part of the world,

695
00:33:23,560 --> 00:33:26,840
Speaker 4: it can actually affect the weather in another part of

696
00:33:26,840 --> 00:33:30,200
Speaker 4: the world, just because it's so sensitive to even small

697
00:33:30,240 --> 00:33:32,560
Speaker 4: things like a butterfly flapping its wing.

698
00:33:32,800 --> 00:33:35,720
Speaker 1: That's right, and not every system is chaotics. Some things

699
00:33:35,720 --> 00:33:38,120
Speaker 1: are not right. Some things. For example, when you throw

700
00:33:38,160 --> 00:33:40,560
Speaker 1: a ball, you throw a ball, if you change the

701
00:33:40,680 --> 00:33:43,680
Speaker 1: angle you're throwing it at by a tiny bit, then

702
00:33:43,720 --> 00:33:46,560
Speaker 1: the outcome is changed by a tiny bit, right, you

703
00:33:46,600 --> 00:33:49,240
Speaker 1: throw it a tiny bit harder, it lands, it goes

704
00:33:49,280 --> 00:33:52,320
Speaker 1: a tiny bit further. But other things, if you change

705
00:33:52,320 --> 00:33:53,760
Speaker 1: how you do them by a tiny bit, you get

706
00:33:53,760 --> 00:33:56,760
Speaker 1: a very different outcome, Like rolling a dye. If you

707
00:33:56,840 --> 00:33:59,160
Speaker 1: roll a dice slightly differently, you spin your hand a

708
00:33:59,200 --> 00:34:01,840
Speaker 1: tiny bit differently, you get a four instead of a two,

709
00:34:01,960 --> 00:34:04,080
Speaker 1: or a five instead of a one. So the outcome

710
00:34:04,160 --> 00:34:07,640
Speaker 1: depends very very sensitively and exactly how you did it.

711
00:34:07,640 --> 00:34:11,280
Speaker 1: It's not random, it's following the laws of physics. In theory,

712
00:34:11,440 --> 00:34:15,160
Speaker 1: it's predictable, but in practice it's very difficult because the

713
00:34:15,200 --> 00:34:19,160
Speaker 1: outcome depends very sensitively on how you flap those butterfly wings.

714
00:34:18,920 --> 00:34:21,120
Speaker 4: Yeah, or how you throw the die. Like to predict

715
00:34:21,400 --> 00:34:24,080
Speaker 4: a die roll, you would need to really kind of

716
00:34:24,120 --> 00:34:27,560
Speaker 4: like pay attention to the exact angle that the each

717
00:34:27,920 --> 00:34:31,560
Speaker 4: die is at when it leaves your hand, and you

718
00:34:31,640 --> 00:34:34,759
Speaker 4: have to predict also or simulate how like when the

719
00:34:34,840 --> 00:34:37,400
Speaker 4: corners hit the ground, how that's going to affect the

720
00:34:37,400 --> 00:34:40,319
Speaker 4: spin of the die. And so it's a really it's

721
00:34:40,360 --> 00:34:44,240
Speaker 4: just a much more complicated system to simulate and predict

722
00:34:44,280 --> 00:34:45,720
Speaker 4: than like just throwing a baseball.

723
00:34:45,840 --> 00:34:47,880
Speaker 1: So then the question is is your brain like a

724
00:34:47,920 --> 00:34:51,000
Speaker 1: big bag of dice where each one very difficult to

725
00:34:51,000 --> 00:34:53,439
Speaker 1: predict and bouncing off the other one it's a very

726
00:34:53,440 --> 00:34:56,560
Speaker 1: complex thing. Or is it made out of things which

727
00:34:56,560 --> 00:35:00,440
Speaker 1: are easy to predict? And if you knew pretty much

728
00:35:00,560 --> 00:35:02,880
Speaker 1: the current situation that you could predict how it's going

729
00:35:02,920 --> 00:35:05,040
Speaker 1: to come about? You know, is your brain a hurricane

730
00:35:05,440 --> 00:35:07,080
Speaker 1: or is it just a baseball right?

731
00:35:07,560 --> 00:35:09,560
Speaker 4: Or you know, if I have butterflies in my stomach

732
00:35:09,600 --> 00:35:12,960
Speaker 4: and those they flap, how is it going to affect

733
00:35:12,960 --> 00:35:15,400
Speaker 4: my decision to eat a cookier or not exactly.

734
00:35:15,440 --> 00:35:17,800
Speaker 1: So the point is that even if you knew exactly

735
00:35:17,800 --> 00:35:20,440
Speaker 1: the current situation of the brain, could you have a

736
00:35:20,520 --> 00:35:23,800
Speaker 1: powerful enough computer to predict what's going to happen going forward?

737
00:35:23,960 --> 00:35:26,720
Speaker 1: And in the end, that's really the limitation. For example,

738
00:35:26,800 --> 00:35:29,640
Speaker 1: for hurricanes, if we knew the location of every drop

739
00:35:29,640 --> 00:35:32,280
Speaker 1: of water on Earth and its current position and velocity,

740
00:35:32,440 --> 00:35:35,399
Speaker 1: and we had a super duper powerful computer, then yeah,

741
00:35:35,440 --> 00:35:39,480
Speaker 1: we could probably predict the weather very accurately. But we

742
00:35:39,560 --> 00:35:41,520
Speaker 1: don't have those computers right.

743
00:35:41,640 --> 00:35:43,880
Speaker 4: Well, I feel like we're getting better, though, you know,

744
00:35:43,960 --> 00:35:46,680
Speaker 4: like sometimes I'm really impressed by how far ahead we

745
00:35:46,719 --> 00:35:49,759
Speaker 4: can predict the weather and how somewhat accurately we can.

746
00:35:49,960 --> 00:35:52,680
Speaker 1: This is just a hurdle of technology. Something like eighty

747
00:35:52,719 --> 00:35:55,920
Speaker 1: percent of the fastest computers in the world, like the supercomputers,

748
00:35:56,000 --> 00:35:58,840
Speaker 1: are all devoted to this problem, predicting the future of

749
00:35:58,840 --> 00:36:01,760
Speaker 1: the weather, understanding the atmosphere and all of its chaos,

750
00:36:02,080 --> 00:36:05,160
Speaker 1: and so it's really just like throwing more computational power

751
00:36:05,200 --> 00:36:05,840
Speaker 1: at the problem.

752
00:36:05,960 --> 00:36:09,560
Speaker 4: Well, and that's because weather is a chaotic system.

753
00:36:09,280 --> 00:36:11,880
Speaker 1: Right, Definitely, weather, it's very chaotic. It's very difficult to

754
00:36:11,920 --> 00:36:14,840
Speaker 1: predict the outcome, even if you know the current conditions.

755
00:36:15,000 --> 00:36:17,640
Speaker 4: But do we know if the brain is chaotic or

756
00:36:18,200 --> 00:36:19,200
Speaker 4: humans or chaotic?

757
00:36:19,360 --> 00:36:21,319
Speaker 1: Humans seem chaotic to me, I mean some of them

758
00:36:21,360 --> 00:36:23,520
Speaker 1: I've known for decades, and I still don't understand why

759
00:36:23,520 --> 00:36:26,360
Speaker 1: the neto decisions they do and not just your children,

760
00:36:27,600 --> 00:36:30,560
Speaker 1: not just my children. No, we don't know for sure,

761
00:36:30,640 --> 00:36:32,920
Speaker 1: but it seems to me very likely. I mean, it's

762
00:36:32,920 --> 00:36:37,200
Speaker 1: a hyper connected, very sensitive set of neurons. It seems

763
00:36:37,239 --> 00:36:39,760
Speaker 1: to me very unlikely that it wouldn't be a chaotic,

764
00:36:39,800 --> 00:36:42,239
Speaker 1: But we don't know. It might be that there are

765
00:36:42,600 --> 00:36:45,759
Speaker 1: sort of emergent phenomenon that you're not really sensitive to.

766
00:36:45,760 --> 00:36:48,640
Speaker 1: All those little details, and that you could build a

767
00:36:48,719 --> 00:36:52,319
Speaker 1: model that predicts roughly where things are going. If you

768
00:36:52,320 --> 00:36:54,800
Speaker 1: don't care about the details, you can tell whether somebody's

769
00:36:54,840 --> 00:36:56,719
Speaker 1: going to have a cookie and who they're going to

770
00:36:56,760 --> 00:36:59,160
Speaker 1: vote for the next election. It's possible that you could

771
00:36:59,160 --> 00:37:02,240
Speaker 1: build those models. That requires a sort of another layer

772
00:37:02,280 --> 00:37:06,040
Speaker 1: of insight. Right. Imagine you only knew the baseball in

773
00:37:06,120 --> 00:37:08,440
Speaker 1: terms of little particles inside of it, and you're like, oh,

774
00:37:08,480 --> 00:37:10,440
Speaker 1: there's no way I could predict the way this baseball

775
00:37:10,480 --> 00:37:12,680
Speaker 1: is going to move. It's ten to the twenty three particles.

776
00:37:12,680 --> 00:37:15,520
Speaker 1: It's impossible. But if you understand, if you took a

777
00:37:15,560 --> 00:37:18,600
Speaker 1: step back and saw the flight of it, you could say, oh, actually,

778
00:37:18,640 --> 00:37:21,200
Speaker 1: I can describe this and ignore all the particles. I

779
00:37:21,239 --> 00:37:24,000
Speaker 1: can ignore all those details they're not relevant, and I

780
00:37:24,000 --> 00:37:26,560
Speaker 1: can just describe this in terms of simple motion. So

781
00:37:26,640 --> 00:37:30,560
Speaker 1: it's possible that there's an emergent theory of psychology mathematical

782
00:37:30,600 --> 00:37:33,239
Speaker 1: psychology that could describe the motion of the brain.

783
00:37:33,360 --> 00:37:36,040
Speaker 4: We just don't know, or I wonder if it might

784
00:37:36,160 --> 00:37:38,640
Speaker 4: vary with people. You know, some people might be more

785
00:37:38,680 --> 00:37:40,120
Speaker 4: predictable than others.

786
00:37:40,480 --> 00:37:42,960
Speaker 1: Yeah, I certainly know some people who seem pretty chaotic.

787
00:37:44,440 --> 00:37:47,040
Speaker 4: All right, Well, those are two pretty big hurdles, But

788
00:37:47,080 --> 00:37:51,799
Speaker 4: then there's another hurdle coming up and or possibly a

789
00:37:51,800 --> 00:37:55,120
Speaker 4: loophole that might still let us have some free will

790
00:37:55,160 --> 00:37:59,719
Speaker 4: in this deterministic machine like universe. So let's get into

791
00:37:59,760 --> 00:38:01,480
Speaker 4: that first. Let's take a quick break.

792
00:38:06,040 --> 00:38:07,839
Speaker 1: When you pop a piece of cheese into your mouth,

793
00:38:07,960 --> 00:38:11,080
Speaker 1: or enjoy a rich spoonful of Greek yogurt, you're probably

794
00:38:11,160 --> 00:38:15,200
Speaker 1: not thinking about the environmental impact of each and every bite.

795
00:38:15,239 --> 00:38:17,840
Speaker 1: But the people in the dairy industry are. US Dairy

796
00:38:17,880 --> 00:38:22,160
Speaker 1: has set themselves some ambitious sustainability goals, including being greenhouse

797
00:38:22,200 --> 00:38:24,799
Speaker 1: gas neutral by twenty to fifty. That's why they're working

798
00:38:24,800 --> 00:38:27,160
Speaker 1: hard every day to find new ways to reduce waste,

799
00:38:27,239 --> 00:38:31,439
Speaker 1: conserve natural resources, and drive down greenhouse gas emissions. Take water,

800
00:38:31,480 --> 00:38:35,080
Speaker 1: for example, most dairy farms reuse water up to four times.

801
00:38:35,120 --> 00:38:38,479
Speaker 1: The same water cools the milk, cleans equipment, washes the barn,

802
00:38:38,600 --> 00:38:42,320
Speaker 1: and irrigates the crops. How is US dairy tackling greenhouse gases?

803
00:38:42,360 --> 00:38:45,319
Speaker 1: Many farms use anaerobic digestors that turn the methane from

804
00:38:45,360 --> 00:38:48,759
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805
00:38:48,800 --> 00:38:51,040
Speaker 1: electric cars. So the next time you grab a slice

806
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Speaker 1: of pizza or lick an ice cream cone, know that

807
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Speaker 1: dairy farmers and processors around the country are using the

808
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810
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00:39:05,000 --> 00:39:08,640
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Speaker 4: All right, Daniel, The last topic here is about randomness

841
00:40:48,040 --> 00:40:53,880
Speaker 4: and whether randomness at the quantum level can maybe effect

842
00:40:53,920 --> 00:40:56,520
Speaker 4: what we see as free will, or whether it can

843
00:40:56,520 --> 00:40:59,000
Speaker 4: affect whether or not we can predict what people will

844
00:40:59,000 --> 00:40:59,480
Speaker 4: think or do.

845
00:41:00,000 --> 00:41:02,000
Speaker 1: The premise here, the assumption we're making is that the

846
00:41:02,040 --> 00:41:05,640
Speaker 1: brain should be predictable if it's various bits are predictable,

847
00:41:05,760 --> 00:41:07,680
Speaker 1: if it's made out of predictable bits, then you should

848
00:41:07,680 --> 00:41:10,360
Speaker 1: be able to put those predictable bits together into a

849
00:41:10,360 --> 00:41:14,320
Speaker 1: predictable brain. But listeners of the podcast are probably wondering,

850
00:41:14,719 --> 00:41:17,600
Speaker 1: are those bits predictable? The brain is made of atoms,

851
00:41:17,600 --> 00:41:19,759
Speaker 1: and atoms are made of protons and electrons and all

852
00:41:19,840 --> 00:41:22,760
Speaker 1: this stuff, and we know that those things are quantum mechanical.

853
00:41:22,760 --> 00:41:25,680
Speaker 1: And we talked on the podcast recently about the crazy

854
00:41:26,000 --> 00:41:30,200
Speaker 1: probabilistic nature of quantum mechanics, that things are not determined

855
00:41:30,280 --> 00:41:33,279
Speaker 1: until they're measures, that there is really true randomness at

856
00:41:33,280 --> 00:41:36,160
Speaker 1: the quantum scale. So you might be wondering, how can

857
00:41:36,200 --> 00:41:40,160
Speaker 1: you build determinism on top of this fuzzy quantum randomness.

858
00:41:40,280 --> 00:41:42,520
Speaker 4: Yeah, because we talked about some of the challenges, right,

859
00:41:42,560 --> 00:41:45,759
Speaker 4: We talked about scanning your brain and about chaos, but

860
00:41:45,800 --> 00:41:49,319
Speaker 4: those are technical problems. You're saying that maybe just in

861
00:41:49,360 --> 00:41:52,480
Speaker 4: the bits themselves of the brain, there is some inherent

862
00:41:53,000 --> 00:41:57,120
Speaker 4: randomness that might make it impossible to predict absolutely.

863
00:41:57,239 --> 00:42:00,200
Speaker 1: We know that there's inherent randomness. Everything, as we say,

864
00:42:00,400 --> 00:42:03,520
Speaker 1: made of atoms, and those things are governed by fundamentally

865
00:42:03,600 --> 00:42:08,080
Speaker 1: quantum mechanical properties. What we don't know is if that matters, right,

866
00:42:08,120 --> 00:42:11,600
Speaker 1: It certainly doesn't matter for predicting the motion of a

867
00:42:11,640 --> 00:42:15,200
Speaker 1: mechanical watch. People can build mechanical watches that are super

868
00:42:15,239 --> 00:42:17,719
Speaker 1: accurate for years at a time. We can predict the

869
00:42:17,719 --> 00:42:20,880
Speaker 1: flight of a baseball without even knowing that quantum mechanics

870
00:42:20,960 --> 00:42:23,879
Speaker 1: was a thing. I remember, quantum mechanics affects things only

871
00:42:23,880 --> 00:42:28,840
Speaker 1: on super duper tiny scales. The uncertainty principle delta x

872
00:42:28,920 --> 00:42:33,320
Speaker 1: delta P the relationship between the uncertainty and motion and position.

873
00:42:33,840 --> 00:42:37,440
Speaker 1: The uncertainty there is is related to this Plank's constant hbar,

874
00:42:37,520 --> 00:42:39,719
Speaker 1: which is like the fundamental unit of the universe. But

875
00:42:39,800 --> 00:42:42,279
Speaker 1: this is a super tiny number. It's ten to the

876
00:42:42,320 --> 00:42:46,680
Speaker 1: minus thirty four jewels. And so it might be that

877
00:42:46,800 --> 00:42:49,520
Speaker 1: on top of the quantum randomness we do have a

878
00:42:49,600 --> 00:42:52,840
Speaker 1: layer of physics which is deterministic. Or it could be

879
00:42:52,920 --> 00:42:55,360
Speaker 1: that you know that the quantum randomness sort of seeps

880
00:42:55,480 --> 00:42:57,719
Speaker 1: up from below and affects the working of the brain.

881
00:42:57,880 --> 00:43:02,160
Speaker 4: Okay, so you're saying that there is a fundamental randomness

882
00:43:02,840 --> 00:43:06,560
Speaker 4: in the universe and in my brain cells, like, at

883
00:43:06,960 --> 00:43:11,360
Speaker 4: to the smallest level, I can't possibly predict my brain.

884
00:43:11,560 --> 00:43:14,879
Speaker 4: But maybe if I go up a few levels, then

885
00:43:14,960 --> 00:43:17,200
Speaker 4: the brain starts to be more predictable.

886
00:43:17,280 --> 00:43:19,120
Speaker 1: We see this in physics. We see at the very

887
00:43:19,160 --> 00:43:22,040
Speaker 1: smallest levels you cannot predict what's going to happen. You

888
00:43:22,080 --> 00:43:24,920
Speaker 1: shoot the same photon into the same experiment twice, you

889
00:43:24,960 --> 00:43:28,799
Speaker 1: get two different outcomes. There's really true randomness there, and

890
00:43:28,840 --> 00:43:33,759
Speaker 1: that breaks determinism. So photons not deterministic, electrons not deterministic.

891
00:43:34,000 --> 00:43:38,880
Speaker 1: Protons not deterministic. But somehow, when you put these things together,

892
00:43:39,120 --> 00:43:42,759
Speaker 1: you put enough of them together, all those random fluctuations

893
00:43:42,840 --> 00:43:46,000
Speaker 1: average out that basically cancel each other. It's a deep

894
00:43:46,120 --> 00:43:48,600
Speaker 1: theorem in physics. It's called the air infest theorem. That

895
00:43:48,719 --> 00:43:51,040
Speaker 1: you have enough of these things and it doesn't matter

896
00:43:51,080 --> 00:43:53,120
Speaker 1: anymore as long as you're measuring things on the sort

897
00:43:53,120 --> 00:43:57,080
Speaker 1: of classical scale and sizes that we care about centimeters, millimeters.

898
00:43:57,080 --> 00:44:00,160
Speaker 1: Even then the quantum mechanic effects average out, which which

899
00:44:00,200 --> 00:44:03,400
Speaker 1: is why we didn't notice quantum mechanics for thousands of years.

900
00:44:03,480 --> 00:44:06,600
Speaker 4: It's hidden inside the summation of all of these quantum

901
00:44:06,680 --> 00:44:07,560
Speaker 4: little events.

902
00:44:07,840 --> 00:44:10,239
Speaker 1: Yeah, which is why it was so hard to accept. Right,

903
00:44:10,239 --> 00:44:13,400
Speaker 1: we had just accepted the mind boggling consequences of a

904
00:44:13,440 --> 00:44:16,719
Speaker 1: deterministic universe, like, Wow, the universe seems to follow rules

905
00:44:16,760 --> 00:44:19,040
Speaker 1: and we can predict it. And then we discovered, oh,

906
00:44:19,120 --> 00:44:22,120
Speaker 1: actually no, at its deepest level, it seems weirdly random.

907
00:44:22,120 --> 00:44:24,960
Speaker 1: And that was totally in contradiction with everything we thought

908
00:44:25,000 --> 00:44:28,120
Speaker 1: we understood, which is why quantum mechanics were so counterintuitive

909
00:44:28,160 --> 00:44:31,480
Speaker 1: and so fuzzy. But still, it didn't mean that all

910
00:44:31,480 --> 00:44:34,040
Speaker 1: those things we'd learned were wrong. It just meant that

911
00:44:34,080 --> 00:44:36,919
Speaker 1: they applied a sort of the larger scales that those

912
00:44:37,280 --> 00:44:40,400
Speaker 1: same rules can't be applied to electrons and protons, but

913
00:44:40,440 --> 00:44:43,719
Speaker 1: they can still be applied to baseballs and watches. So

914
00:44:43,840 --> 00:44:46,960
Speaker 1: then the question is is your brain a baseball and

915
00:44:47,040 --> 00:44:48,719
Speaker 1: watch or is it an electron.

916
00:44:48,880 --> 00:44:51,799
Speaker 4: Yeah, it's kind of like if I said, hey, Danie,

917
00:44:51,800 --> 00:44:53,719
Speaker 4: I'm going to flip this coin, and if it's had,

918
00:44:53,760 --> 00:44:55,879
Speaker 4: I'm going to take your cookie, and if it's tails,

919
00:44:55,920 --> 00:44:58,440
Speaker 4: I'm not going to take your cookie. Then then it's

920
00:44:58,480 --> 00:45:00,880
Speaker 4: kind of like you might say, it's unpredictable, what am

921
00:45:00,880 --> 00:45:02,560
Speaker 4: I going to do? Right, because you don't know.

922
00:45:02,520 --> 00:45:05,440
Speaker 1: Except that a coin is deterministic. A coin is a

923
00:45:05,440 --> 00:45:07,880
Speaker 1: classical object. If I knew exactly how you were going

924
00:45:07,960 --> 00:45:10,319
Speaker 1: to flip it, I could predict exactly how that coin

925
00:45:10,400 --> 00:45:12,160
Speaker 1: was going to land, and I would know that you

926
00:45:12,160 --> 00:45:13,640
Speaker 1: were going to take the cookie no matter how the

927
00:45:13,640 --> 00:45:16,040
Speaker 1: coin flipped, actually, because I know you as a person.

928
00:45:16,719 --> 00:45:19,800
Speaker 1: But yeah, exactly the coin flip is not written. But

929
00:45:19,800 --> 00:45:22,680
Speaker 1: if you, for example, had a radioactive particle and you said,

930
00:45:22,840 --> 00:45:25,680
Speaker 1: I'm going to wait one minute. If the particle decays

931
00:45:25,920 --> 00:45:27,719
Speaker 1: within this minute, I'm eating the cookie, and if it

932
00:45:27,760 --> 00:45:30,719
Speaker 1: doesn't decay within this minute, I'm not eating the cookie,

933
00:45:30,920 --> 00:45:33,200
Speaker 1: then I can't predict whether or not you're going to

934
00:45:33,239 --> 00:45:35,799
Speaker 1: eat the cookie. That's because you've linked a macroscopic action

935
00:45:36,080 --> 00:45:39,680
Speaker 1: eating the cookie to a quantum mechanical thing. That's rare, right,

936
00:45:39,680 --> 00:45:43,000
Speaker 1: that's a whole Schrodinger's box argument. It's very difficult to

937
00:45:43,080 --> 00:45:45,920
Speaker 1: find quantum mechanic effects that you can see on the

938
00:45:45,960 --> 00:45:47,120
Speaker 1: macroscopic scale.

939
00:45:47,719 --> 00:45:51,360
Speaker 4: Oh, I see, Like, if I made my cookie eating

940
00:45:51,680 --> 00:45:58,000
Speaker 4: dependent on whether this particle decays or not, then it's unpredictable.

941
00:45:58,480 --> 00:46:01,759
Speaker 4: But if I make my eating cookie depending on whether

942
00:46:01,840 --> 00:46:05,360
Speaker 4: the particle will decay over a minute, then we know

943
00:46:05,440 --> 00:46:08,560
Speaker 4: a lot more. Right, it's less unpredictable because you know

944
00:46:08,800 --> 00:46:11,600
Speaker 4: that on average, most of these particles. Let's say like

945
00:46:11,680 --> 00:46:14,360
Speaker 4: ninety percent of these particles decay within a minute.

946
00:46:14,480 --> 00:46:17,200
Speaker 1: Yeah, that's true. And if you want to make statements

947
00:46:17,200 --> 00:46:20,560
Speaker 1: about averages, then you're a rock solid footing even quant mechanically,

948
00:46:20,640 --> 00:46:23,239
Speaker 1: because quant mechanics doesn't mean things don't follow laws. It

949
00:46:23,320 --> 00:46:26,239
Speaker 1: just means those laws are probabilistic. So you can say,

950
00:46:26,280 --> 00:46:28,200
Speaker 1: on average, this is going to happen. On average, that's

951
00:46:28,200 --> 00:46:31,399
Speaker 1: going to happen for one particular particle. You can't make

952
00:46:31,440 --> 00:46:34,440
Speaker 1: any predictions on a quantum mechanical scale, But you know

953
00:46:34,719 --> 00:46:38,160
Speaker 1: what quantum mechanical effects do you observe as a person, Like,

954
00:46:38,600 --> 00:46:42,600
Speaker 1: for something to affect your life that's really random, it

955
00:46:42,680 --> 00:46:45,279
Speaker 1: has to have an impact like that somebody's measuring this

956
00:46:45,360 --> 00:46:48,240
Speaker 1: quantum mechanical thing and making a decision based on it.

957
00:46:48,239 --> 00:46:51,920
Speaker 1: It's really pretty rare for quantum mechanical randomness to affect

958
00:46:52,080 --> 00:46:56,000
Speaker 1: the macroscopic world. People do really complicated experiments to try

959
00:46:56,000 --> 00:46:58,200
Speaker 1: to set this kind of thing up, Like Bose Einstein

960
00:46:58,280 --> 00:47:02,080
Speaker 1: condensate is like a macroscopic quantum state that you can

961
00:47:02,160 --> 00:47:06,000
Speaker 1: see that actually behaves quantum mechanically. It's really weird and amazing.

962
00:47:06,080 --> 00:47:09,759
Speaker 1: People win the Nobel Prize for that. So for the

963
00:47:09,800 --> 00:47:13,200
Speaker 1: brain to be dependent on quantum mechanical randomness, you'd have

964
00:47:13,239 --> 00:47:16,400
Speaker 1: to show somehow that like the motion of these electrons,

965
00:47:16,480 --> 00:47:20,600
Speaker 1: this weird quantum mechanical effect was triggering neurons, and neurons

966
00:47:20,600 --> 00:47:22,840
Speaker 1: were somehow sensitive to these things.

967
00:47:23,239 --> 00:47:27,520
Speaker 4: Yeah, so it's fundamentally random. But the question, it seems

968
00:47:27,560 --> 00:47:29,799
Speaker 4: like the core and the key question here is whether

969
00:47:29,880 --> 00:47:33,879
Speaker 4: that randomness at the quantum level really affects whether I'm

970
00:47:33,880 --> 00:47:35,880
Speaker 4: going to eat my cookie or not, or whether that

971
00:47:35,960 --> 00:47:39,200
Speaker 4: all gets drowned out by the bazillion electrons that I

972
00:47:39,239 --> 00:47:39,920
Speaker 4: have in my brain.

973
00:47:40,120 --> 00:47:42,839
Speaker 1: And there are some folks who've made that argument, and

974
00:47:42,880 --> 00:47:45,560
Speaker 1: I think that that argument is largely in response to

975
00:47:45,960 --> 00:47:48,759
Speaker 1: fear of determinism. They don't want to think that the

976
00:47:49,280 --> 00:47:51,640
Speaker 1: brain is just a mechanical watch that can be predicted.

977
00:47:51,880 --> 00:47:55,320
Speaker 1: So they're sort of striving for some way to leave

978
00:47:55,400 --> 00:47:57,520
Speaker 1: a window open for free will. And they say, oh, well,

979
00:47:57,560 --> 00:48:00,360
Speaker 1: if we can connect it to quantum randomness, and you know,

980
00:48:00,400 --> 00:48:02,719
Speaker 1: you can't be predicted and therefore there might be room

981
00:48:02,760 --> 00:48:05,680
Speaker 1: for free will. And famous people like Roger Penrose make

982
00:48:05,719 --> 00:48:08,880
Speaker 1: this argument, but it's just a it's more like an

983
00:48:08,920 --> 00:48:11,960
Speaker 1: outline of an argument there's no evidence that the brain

984
00:48:12,040 --> 00:48:14,880
Speaker 1: is dependent on quantum mechanics. It's just like, can we

985
00:48:14,960 --> 00:48:18,040
Speaker 1: find some path maybe go down that leads us to

986
00:48:18,080 --> 00:48:21,480
Speaker 1: a non deterministic brain. There's no real evidence or argument there.

987
00:48:21,480 --> 00:48:22,680
Speaker 1: It's just like a suggestion.

988
00:48:23,320 --> 00:48:26,399
Speaker 4: Well, I think the thing is that it's a it's

989
00:48:26,440 --> 00:48:28,960
Speaker 4: a yes or no decision, right, whether I eat the cookie,

990
00:48:29,360 --> 00:48:31,799
Speaker 4: it's either yes or no. So I'm kind of like

991
00:48:31,880 --> 00:48:36,120
Speaker 4: sitting on the edge of a really thin knife, right,

992
00:48:36,400 --> 00:48:38,839
Speaker 4: and you know who knows? Are you can you really

993
00:48:38,920 --> 00:48:42,880
Speaker 4: rule out that, you know, how one particular electron behaved

994
00:48:43,600 --> 00:48:45,839
Speaker 4: was resultant in pushing me one way or the other?

995
00:48:46,120 --> 00:48:50,280
Speaker 4: Or are you saying that pretty much that's unlikely.

996
00:48:50,719 --> 00:48:52,359
Speaker 1: No, I'm not saying that we can rule it out

997
00:48:52,360 --> 00:48:54,279
Speaker 1: at all. You're totally right, and it could be that

998
00:48:54,320 --> 00:48:57,160
Speaker 1: there are quantum mechanic effects that determine whether or not

999
00:48:57,200 --> 00:48:59,840
Speaker 1: you make that decision. It's possible, but I'm saying we

1000
00:48:59,880 --> 00:49:02,480
Speaker 1: have no evidence for that. Nobody has shown that we

1001
00:49:02,480 --> 00:49:04,759
Speaker 1: don't even understand the mechanism of it. That doesn't mean

1002
00:49:04,800 --> 00:49:07,000
Speaker 1: it's not happening. It just means that it's more of

1003
00:49:07,040 --> 00:49:10,560
Speaker 1: a suggestion, an open door than an actual idea.

1004
00:49:10,880 --> 00:49:13,600
Speaker 4: All right, Well, I feel pretty good. I feel like

1005
00:49:13,640 --> 00:49:16,080
Speaker 4: we am We're right where we predicted we would be.

1006
00:49:17,120 --> 00:49:20,839
Speaker 4: I think you deserve a cookie or a banana at

1007
00:49:20,880 --> 00:49:22,719
Speaker 4: the very list. Oh no, wait, do you said I

1008
00:49:22,719 --> 00:49:23,760
Speaker 4: wouldn't say, there.

1009
00:49:23,520 --> 00:49:26,239
Speaker 1: You go, there you go. But I think that there's

1010
00:49:26,280 --> 00:49:28,640
Speaker 1: some fascinating questions there, Like, even if you knew the

1011
00:49:28,719 --> 00:49:31,200
Speaker 1: answer to this question, even if you showed that the

1012
00:49:31,239 --> 00:49:34,880
Speaker 1: brain was deterministic or wasn't that the brain was quantum

1013
00:49:34,880 --> 00:49:39,160
Speaker 1: mechanically random, what would that mean for this experience of

1014
00:49:39,280 --> 00:49:39,880
Speaker 1: free will?

1015
00:49:40,000 --> 00:49:41,840
Speaker 4: Let me see if I can reac out what we learned.

1016
00:49:41,840 --> 00:49:45,200
Speaker 4: We learned that it's predicting what people are going to

1017
00:49:45,280 --> 00:49:49,200
Speaker 4: do is super technically hard with the scanning all of

1018
00:49:49,239 --> 00:49:53,319
Speaker 4: the neurons in your brain and chaos maybe playing a

1019
00:49:53,400 --> 00:49:55,759
Speaker 4: larch part and making it unpredictable. But let's say like

1020
00:49:55,800 --> 00:49:58,759
Speaker 4: we invented technology to take care of that. There's still

1021
00:49:58,800 --> 00:50:02,920
Speaker 4: sort of the question of whether randomness trickles up to

1022
00:50:02,960 --> 00:50:07,319
Speaker 4: influence decisions, you know, randomness at the quantum level, whether

1023
00:50:07,400 --> 00:50:10,360
Speaker 4: that trickles up to influence decisions. And it sounds like

1024
00:50:10,400 --> 00:50:13,160
Speaker 4: we don't know. It sounds like we can't say either way.

1025
00:50:13,280 --> 00:50:17,200
Speaker 1: That's right, quantum mechanics might make it theoretically impossible to

1026
00:50:17,320 --> 00:50:19,920
Speaker 1: predict the brain. We don't know, and if it doesn't,

1027
00:50:20,160 --> 00:50:22,200
Speaker 1: if it doesn't make it impossible, and we know, it's

1028
00:50:22,239 --> 00:50:25,600
Speaker 1: still super duper hard because you need to know the

1029
00:50:25,640 --> 00:50:28,080
Speaker 1: exact state of the brain and you need to overcome

1030
00:50:28,120 --> 00:50:33,000
Speaker 1: the potential overwhelming chaos of your billions and billions of neurons.

1031
00:50:33,600 --> 00:50:37,240
Speaker 1: So even if it's not impossible, it's definitely very, very tricky.

1032
00:50:37,040 --> 00:50:38,960
Speaker 4: Right, And then we could probably have a whole podcast

1033
00:50:39,000 --> 00:50:42,120
Speaker 4: just on the implications on how we feel about free

1034
00:50:42,120 --> 00:50:45,520
Speaker 4: will and whether it would still exist even if it

1035
00:50:45,640 --> 00:50:47,600
Speaker 4: was governed by quantum randomness.

1036
00:50:47,719 --> 00:50:50,759
Speaker 1: That's right, And this is an area of philosophy, And

1037
00:50:50,920 --> 00:50:53,520
Speaker 1: neither you nor I have formal training in philosophy.

1038
00:50:53,520 --> 00:50:55,120
Speaker 4: What do you mean? I have a doctorate in philosophy

1039
00:50:55,960 --> 00:50:56,680
Speaker 4: in engineering.

1040
00:50:59,320 --> 00:51:01,720
Speaker 1: So then let me ask Jorge, if I could prove

1041
00:51:01,800 --> 00:51:04,359
Speaker 1: that you were deterministic, does that mean you don't have

1042
00:51:04,400 --> 00:51:07,359
Speaker 1: free will? Does being a big mechanical robot mean you're

1043
00:51:07,360 --> 00:51:10,080
Speaker 1: not making choices? Or is that just describe the choices

1044
00:51:10,120 --> 00:51:11,360
Speaker 1: you are you are making?

1045
00:51:11,560 --> 00:51:14,120
Speaker 4: Mmm? I think that would be bananas.

1046
00:51:15,000 --> 00:51:15,080
Speaker 5: No.

1047
00:51:15,239 --> 00:51:17,279
Speaker 4: I think for me it doesn't matter. I feel like

1048
00:51:17,320 --> 00:51:19,680
Speaker 4: it doesn't matter. I'm not someone who sweats free will

1049
00:51:19,800 --> 00:51:22,400
Speaker 4: too much to be honest, like, I feel like I

1050
00:51:22,480 --> 00:51:25,840
Speaker 4: might be a robot. That's fine, And you know, currently

1051
00:51:26,120 --> 00:51:28,120
Speaker 4: it's almost like who would want to predict what I'm

1052
00:51:28,120 --> 00:51:29,359
Speaker 4: going to do? Do you know what I mean?

1053
00:51:29,840 --> 00:51:32,640
Speaker 1: Like it's such like why is this problem even interesting?

1054
00:51:33,800 --> 00:51:35,960
Speaker 4: Who would take the time to care about what I'm

1055
00:51:36,120 --> 00:51:36,880
Speaker 4: going to think and do?

1056
00:51:38,040 --> 00:51:38,160
Speaker 8: So?

1057
00:51:38,200 --> 00:51:41,000
Speaker 4: As long as nobody else can think or would want

1058
00:51:41,040 --> 00:51:43,480
Speaker 4: to know what I think and do? Then to me like, okay,

1059
00:51:43,520 --> 00:51:44,880
Speaker 4: I could be a robot or I could not be

1060
00:51:44,880 --> 00:51:45,360
Speaker 4: a robot.

1061
00:51:45,440 --> 00:51:47,920
Speaker 1: Well, I think there's an implication of moral responsibility. We've

1062
00:51:47,920 --> 00:51:50,279
Speaker 1: been talking about you eating a cookie. What if that

1063
00:51:50,400 --> 00:51:54,160
Speaker 1: was somebody else's cookie, right, and you ate that person's cookie,

1064
00:51:54,239 --> 00:51:56,720
Speaker 1: then could you say, Hey, I didn't make that choice.

1065
00:51:56,719 --> 00:51:59,040
Speaker 1: I have no free will, and therefore I can't be

1066
00:51:59,080 --> 00:52:03,520
Speaker 1: morally culpable. If everybody's deterministic and people aren't making choices,

1067
00:52:03,560 --> 00:52:06,120
Speaker 1: then our sort of whole theory of morality kind of

1068
00:52:06,120 --> 00:52:09,680
Speaker 1: falls apart, and we can't really punish anybody for anything,

1069
00:52:09,920 --> 00:52:10,319
Speaker 1: all right.

1070
00:52:10,239 --> 00:52:12,920
Speaker 4: We'll say that. For our other podcast, Daniel and Jorge

1071
00:52:13,040 --> 00:52:14,480
Speaker 4: explain the moral.

1072
00:52:14,280 --> 00:52:18,120
Speaker 1: Universe Daniel and Jorge blather on about philosophy. They don't

1073
00:52:18,160 --> 00:52:19,240
Speaker 1: really know what they're talking about.

1074
00:52:20,440 --> 00:52:22,640
Speaker 4: I would listen to that, and I predict that a

1075
00:52:22,719 --> 00:52:24,480
Speaker 4: lot of people will probably not.

1076
00:52:24,640 --> 00:52:26,480
Speaker 1: No, but I think these are like many of the

1077
00:52:26,480 --> 00:52:28,000
Speaker 1: things that we talk about on the show, there are

1078
00:52:28,120 --> 00:52:30,719
Speaker 1: deep implications for just what it means to be human being.

1079
00:52:31,000 --> 00:52:34,200
Speaker 1: Often we talk about how the universe was created and

1080
00:52:34,239 --> 00:52:36,040
Speaker 1: how it came to be in its future, and that

1081
00:52:36,080 --> 00:52:40,080
Speaker 1: has deep importance for people. What people think about their

1082
00:52:40,120 --> 00:52:42,400
Speaker 1: place and the cosmos and how they should live their lives,

1083
00:52:42,400 --> 00:52:44,880
Speaker 1: and this is similar. It's a question, you know, is

1084
00:52:44,920 --> 00:52:47,239
Speaker 1: this the threshold that science will ever cross? And if so,

1085
00:52:47,400 --> 00:52:49,680
Speaker 1: what does it mean? And so I love the fact

1086
00:52:49,680 --> 00:52:52,640
Speaker 1: that science is so relevant sometimes that it really touches

1087
00:52:52,719 --> 00:52:54,319
Speaker 1: the deep core of how you're going to live your

1088
00:52:54,320 --> 00:52:55,560
Speaker 1: life and what it means to be human.

1089
00:52:55,760 --> 00:52:58,359
Speaker 4: Yeah, it's amazing to think that physics can have such

1090
00:52:58,400 --> 00:53:01,280
Speaker 4: an impact in who we are, as in our souls,

1091
00:53:01,360 --> 00:53:04,239
Speaker 4: in our conception of who we are.

1092
00:53:04,360 --> 00:53:06,960
Speaker 1: Well, physics is deep in my soul and now maybe

1093
00:53:06,960 --> 00:53:09,680
Speaker 1: you're discovering it's actually deep in your soul as well.

1094
00:53:11,520 --> 00:53:15,560
Speaker 4: We got you, We infected your soul to your audience physics.

1095
00:53:15,680 --> 00:53:17,520
Speaker 4: All right, well, thanks for joining us. We hope you

1096
00:53:17,719 --> 00:53:20,120
Speaker 4: enjoyed that, and we hope that meant all of your

1097
00:53:20,440 --> 00:53:23,040
Speaker 4: expectations about what this podcast was going to be.

1098
00:53:23,200 --> 00:53:25,319
Speaker 1: That's right. Thanks for tuning in. And for those of

1099
00:53:25,320 --> 00:53:28,960
Speaker 1: you who wonder whether science can actually explain the universe,

1100
00:53:29,000 --> 00:53:31,799
Speaker 1: remember that we don't know. Science has worked pretty well

1101
00:53:31,840 --> 00:53:33,520
Speaker 1: so far and been able to explain a lot of

1102
00:53:33,560 --> 00:53:35,680
Speaker 1: what we've seen, but there might be some day in

1103
00:53:35,680 --> 00:53:38,200
Speaker 1: the future where we find some phenomenon that can't be

1104
00:53:38,280 --> 00:53:41,320
Speaker 1: explained by science, or by our current visioning of science

1105
00:53:41,400 --> 00:53:44,359
Speaker 1: or our mathematical rules. So we are continuing on this

1106
00:53:44,440 --> 00:53:47,200
Speaker 1: journey of trying to explore and explain the universe we

1107
00:53:47,239 --> 00:53:48,040
Speaker 1: find around us.

1108
00:53:48,239 --> 00:53:50,120
Speaker 4: Thanks for joining us, see you next time.

1109
00:53:57,760 --> 00:53:59,960
Speaker 1: Before we still have a question after listening to all

1110
00:54:00,040 --> 00:54:03,200
Speaker 1: all these explanations, please drop us a line. We'd love

1111
00:54:03,280 --> 00:54:05,800
Speaker 1: to hear from you. You can find us at Facebook, Twitter,

1112
00:54:05,880 --> 00:54:09,560
Speaker 1: and Instagram at Daniel and Jorge That's one word, or

1113
00:54:09,680 --> 00:54:14,400
Speaker 1: email us at Feedback at Danielandhorge dot com. Thanks for listening,

1114
00:54:14,400 --> 00:54:17,120
Speaker 1: and remember that Daniel and Jorge Explain the Universe is

1115
00:54:17,160 --> 00:54:21,759
Speaker 1: a production of iHeartRadio. For more podcasts from iHeartRadio visit

1116
00:54:21,800 --> 00:54:25,880
Speaker 1: the iHeartRadio app, Apple Podcasts, or wherever you listen to

1117
00:54:25,920 --> 00:54:37,839
Speaker 1: your favorite shows. When you pop a piece of cheese

1118
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Speaker 1: into your mouth, you're probably not thinking about the environmental impact.

1119
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Speaker 1: But the people in the dairy industry are. That's why

1120
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1121
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1122
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Speaker 1: House US dairy tackling greenhouse gases. Many farms use anaerobic

1123
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1125
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