Latest / Daniel and Kelly’s Extraordinary Universe / Can science predict what you will do?

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Can science predict what you will do? · Daniel and Kelly’s Extraordinary Universe

• 0:09
  Speaker 1: Hey, jorgey, do you think science is good at predicting
• 0:12
  Speaker 1: the future. It doesn't do very well with predicting the weather,
• 0:16
  Speaker 1: does it well? Here in California just says sunny every day? Yeah?
• 0:19
  Speaker 1: I guess some states are easier to predict the better.
• 0:21
  Speaker 1: All right, but let me try my hand at it.
• 0:24
  Speaker 1: I have a prediction of the future. You're a psychic,
• 0:28
  Speaker 1: note and a physicist. Well, I predict that you cannot
• 0:33
  Speaker 1: go a whole episode without talking about bananas. What that's bananas? Boom,
• 0:39
  Speaker 1: there you go. I'm totally psychic. You know the difference
• 0:42
  Speaker 1: between a psychic and a physicist their salary where you
• 0:47
  Speaker 1: put the h That's the only difference. Hey, I'm RhE
• 1:08
  Speaker 1: and my cartoonist and the creator of PhD comments. Hi.
• 1:12
  Speaker 1: I'm Daniel. I'm a particle physicist, not a particle psychic,
• 1:16
  Speaker 1: and I'm a co author of our book We Have
• 1:18
  Speaker 1: No Idea, A Guide to the Unknown Universe. Welcome to
• 1:22
  Speaker 1: our podcast, Daniel and Jorge Explain the Universe, a production
• 1:25
  Speaker 1: of I Heart Radio, in which we talk about all
• 1:28
  Speaker 1: the amazing, the fascinating, the hot, the nasty, the wet,
• 1:31
  Speaker 1: the bright, the dirty, the soft, the quiet, the loud,
• 1:33
  Speaker 1: the hot, the cold, all the stuff in the universe
• 1:36
  Speaker 1: that's fascinating and beautiful and amazing, and explain it to
• 1:39
  Speaker 1: you in a way that we hope you actually understand
• 1:42
  Speaker 1: and hopefully even enjoy. Yeah, and sometimes on the podcast,
• 1:45
  Speaker 1: we like to talk about predicting the future. What's gonna happen,
• 1:50
  Speaker 1: what's gonna happen to the universe, what's going to happen
• 1:53
  Speaker 1: to the Earth, what's gonna happen to the solar system,
• 1:56
  Speaker 1: what's going to happen to this podcast? Where is this
• 1:59
  Speaker 1: podcast going anyway? Yeah, we like to talk about what
• 2:03
  Speaker 1: science does and doesn't know about the future, what we
• 2:06
  Speaker 1: can predict about what's going to happen, and where we
• 2:08
  Speaker 1: are totally clueless. Yeah, well, as I was we were
• 2:11
  Speaker 1: saying earlier, there's a there's a fine line between being
• 2:14
  Speaker 1: a psychic and being a physicist. I would say it's
• 2:18
  Speaker 1: a bright but fine line. It's right, Okay, it's an
• 2:21
  Speaker 1: impenetrable barrier, quantum barrier. It's difficult to tunnel between being
• 2:26
  Speaker 1: a physicist and being a psychic, but in some sense
• 2:29
  Speaker 1: we do have the same job. Physics also wants to
• 2:32
  Speaker 1: predict the future. Yeah, that's kind of. I mean, that's
• 2:34
  Speaker 1: kind of why, in a way, physics was invented, right,
• 2:38
  Speaker 1: Like we want to know where this catapult payload is
• 2:42
  Speaker 1: going to land. We want to know how far my
• 2:45
  Speaker 1: car is going to go? Were you there when physics
• 2:47
  Speaker 1: was invented? I didn't get invited to that meeting? Oh yeah,
• 2:49
  Speaker 1: and you didn't? Um? I think Einstein was there and
• 2:52
  Speaker 1: Newton was there, and damn I knew I should have
• 2:56
  Speaker 1: checked my email that day. He missed the calendar invite.
• 2:58
  Speaker 1: That's right, And all those exams, as you mentioned, are
• 3:00
  Speaker 1: totally valuable and our examples of why science has an
• 3:04
  Speaker 1: impact on everyday life. You know, is my catapult going
• 3:07
  Speaker 1: to get over those castle walls and this kind of stuff?
• 3:10
  Speaker 1: But also physics just wants to understand what's going to
• 3:13
  Speaker 1: happen to us, what's our fate? How can we plan
• 3:15
  Speaker 1: to live ahead? Um? And can we understand the mysteries
• 3:18
  Speaker 1: of everything around us so that we can know when
• 3:20
  Speaker 1: lightning is going to strike or when disease is going
• 3:22
  Speaker 1: to wipe out our cattle? Right? In a way, that's
• 3:25
  Speaker 1: kind of the the standard for physics, right. It's like
• 3:27
  Speaker 1: like we we we say we understand something in a
• 3:31
  Speaker 1: way we can sort of predict what's going to happen.
• 3:33
  Speaker 1: You want to verify that your theory of physics describes
• 3:36
  Speaker 1: the real universe out there and not just some idea
• 3:39
  Speaker 1: in your mind. You have to make a testable prediction. Einstein,
• 3:43
  Speaker 1: for example, predicted how light was going to be bent
• 3:45
  Speaker 1: during an eclipse, and people measured it and he was right.
• 3:48
  Speaker 1: And that's really when people started to believe his theory
• 3:51
  Speaker 1: of relativity. Because if your theory can't predict the future,
• 3:55
  Speaker 1: then what uses it, Right, That's what science does. It
• 3:58
  Speaker 1: predicts the outcome of future elements. Yeah, and you know,
• 4:01
  Speaker 1: I think people are comfortable with this idea that physics
• 4:05
  Speaker 1: is able to some degree predict the future. Like if
• 4:07
  Speaker 1: if you told somebody, hey, physics predict that the Earth
• 4:09
  Speaker 1: is going to be around for a very long time,
• 4:11
  Speaker 1: or that the universe will never end that those are comforting,
• 4:15
  Speaker 1: or if you eat that candy you will get bad. Yeah,
• 4:18
  Speaker 1: I think that's a that's more of a physical education. Um,
• 4:24
  Speaker 1: there's physics there. You're converting candy energy into squishy stomach energy. Yeah.
• 4:31
  Speaker 1: So we're comfortable I think with some physics predicting some
• 4:34
  Speaker 1: things about the future. But I think we're a little
• 4:37
  Speaker 1: bit uncomfortable about physics describing other things about the future, right. Yeah.
• 4:42
  Speaker 1: We like to use physics and science to explore the
• 4:44
  Speaker 1: universe around us and outside us. But then sometimes we
• 4:48
  Speaker 1: turn that science on ourselves and we seek to gain
• 4:51
  Speaker 1: insight into how we work. And then that makes you wonder. Yeah,
• 4:54
  Speaker 1: it makes you wonder if you are predictable in a
• 4:57
  Speaker 1: way you know, like could physics but actually one day,
• 5:02
  Speaker 1: you know, simulate the human mind, or simulate your mind
• 5:05
  Speaker 1: and predict what you're gonna think and do. It's a
• 5:08
  Speaker 1: fascinating question because so many things that humans have puzzled
• 5:11
  Speaker 1: over for so many years, how eclipse has happened, where
• 5:14
  Speaker 1: lightning strikes, all this stuff, how reproduction works. All of
• 5:17
  Speaker 1: these things have in the end been explained through science.
• 5:20
  Speaker 1: It turns out they are mechanistic. We can understand the
• 5:24
  Speaker 1: microscopically how it works and predict what's going to happen
• 5:27
  Speaker 1: in the future. And so then it's a natural question
• 5:29
  Speaker 1: to wonder how far you can extend that strategy. Can
• 5:32
  Speaker 1: you turn that around and extend it into your own
• 5:35
  Speaker 1: inner life? Right yeah, because you know, like if physics
• 5:38
  Speaker 1: can predict what, uh, you know a can of gas
• 5:41
  Speaker 1: particles is going to do. Why can't it predict what
• 5:45
  Speaker 1: a brain full of neurons is going to do? What's
• 5:47
  Speaker 1: the difference in the end between a can of gas
• 5:49
  Speaker 1: and a brain of neurons Really depends on the person probably,
• 5:52
  Speaker 1: but it depends on what that person eight recently. Yeah,
• 5:57
  Speaker 1: So it's a big question with I think some really
• 5:59
  Speaker 1: deep philosophical implications right about who we are and whether
• 6:04
  Speaker 1: or not we're predictable, or whether or not we have
• 6:07
  Speaker 1: this thing called free will. Yeah, that's right. Like many
• 6:10
  Speaker 1: of the topics we touch on in this podcast, there
• 6:13
  Speaker 1: are deep philosophical implications, and so I think today we
• 6:17
  Speaker 1: should walk carefully and focus on the science and then
• 6:19
  Speaker 1: think about what the philosophical implications are of what we
• 6:23
  Speaker 1: do and do not know. Yeah, and so today on
• 6:25
  Speaker 1: the podcast, we'll be tackling the question can't science predict
• 6:34
  Speaker 1: what you're going to do? And I predict that there
• 6:37
  Speaker 1: will be a lot of predictions in this episode, but
• 6:40
  Speaker 1: maybe not a lot of answers. Well that's sort of
• 6:44
  Speaker 1: our specialty, right, opening questions and not really answering that.
• 6:50
  Speaker 1: I think what you mean is getting people excited about
• 6:52
  Speaker 1: the questions, right, and that's right, sparking people's fundamental curiosity.
• 6:58
  Speaker 1: I think it's I think it's wonderful to talk about
• 7:00
  Speaker 1: things that we don't understand very well, because hopefully it's
• 7:03
  Speaker 1: a preview for what's going to happen in the future.
• 7:05
  Speaker 1: It's like a fantasy for future science. Maybe in five
• 7:08
  Speaker 1: years or in a thousand years, Signs will have figured
• 7:11
  Speaker 1: out the exact workings of the human brain. It can
• 7:13
  Speaker 1: tell you exactly what you're gonna do tomorrow. That would
• 7:16
  Speaker 1: be amazing. We totally change the way life operates, the
• 7:19
  Speaker 1: way what it's like to be a human being, right, Yeah,
• 7:21
  Speaker 1: I mean, we wrote a whole book about all the
• 7:23
  Speaker 1: things we don't know, Daniel, I'm sure we can pull
• 7:26
  Speaker 1: off a podcast also, I predict we will. So. Yeah.
• 7:31
  Speaker 1: So this is an interesting question, and it's kind of
• 7:34
  Speaker 1: goes back to a long time ago, when you know,
• 7:37
  Speaker 1: once Signs started seeing that the universe was what they
• 7:41
  Speaker 1: called deterministic, meaning like like just the giant machine that
• 7:45
  Speaker 1: follows the laws of physics like a clock. Then I
• 7:49
  Speaker 1: think that's when people started to think, like, hey, maybe
• 7:52
  Speaker 1: maybe our humanity is also predictable like a clock. Yeah.
• 7:56
  Speaker 1: I think that's sort of shocking. I think when people
• 7:59
  Speaker 1: first had that idea, it might have been a terrifying
• 8:02
  Speaker 1: moment to imagine that this experience they're having might actually
• 8:06
  Speaker 1: just be explainable, that things could be determined from the past. Yeah. Well,
• 8:11
  Speaker 1: I think if you told my eleven year old self
• 8:13
  Speaker 1: that I was a robot, I'd be like cool. Nowadays, though,
• 8:17
  Speaker 1: you know, it's more of an uncomfortable statement. You're a
• 8:20
  Speaker 1: robot and we gave the remote control to your sister. Sorry. Yeah, no.
• 8:27
  Speaker 1: I think also it's fun to think even more deeply
• 8:31
  Speaker 1: into sort of the origins of human thought on this question.
• 8:34
  Speaker 1: Is the sort of the hubrist that the universe is
• 8:37
  Speaker 1: explainable at any level? You know, I think a thousand
• 8:40
  Speaker 1: years ago or five thousand years ago, people might have
• 8:41
  Speaker 1: been comfortable with the idea that the universe doesn't follow laws,
• 8:45
  Speaker 1: it just sort of is, and maybe there are these
• 8:47
  Speaker 1: omniscient sentient beings out there that are in control of
• 8:50
  Speaker 1: stuff and they can do whatever they like. So the
• 8:52
  Speaker 1: idea that the universe that we could write down rules,
• 8:55
  Speaker 1: we could discover rules that the universe follows and use
• 8:58
  Speaker 1: those to predict what's going to happen. That's an incredible
• 9:00
  Speaker 1: step forward in human intellectual history. But also it's not
• 9:03
  Speaker 1: something we can necessarily explain, like why would the universe
• 9:08
  Speaker 1: be deterministic? Why would the universe even follow laws? Is
• 9:11
  Speaker 1: it true even today, given our amazing success in science,
• 9:15
  Speaker 1: is it true that everything we can discover, Um, every
• 9:18
  Speaker 1: natural phenomena we discover will eventually be explained by science
• 9:21
  Speaker 1: or can be explained by science. Those are open questions
• 9:24
  Speaker 1: in philosophy. Yeah, and so I guess the question then is,
• 9:28
  Speaker 1: you know how far can we push science? Like if
• 9:30
  Speaker 1: science can predict the future to some degree, like you know,
• 9:33
  Speaker 1: I can predict that the airplane is not going to
• 9:35
  Speaker 1: fall from the sky, or it's going to predict that, Um,
• 9:38
  Speaker 1: you know, if I shoot this laser, this is what's
• 9:39
  Speaker 1: gonna happen. You know, how far can we push the
• 9:42
  Speaker 1: science and maybe even predict what your brain is going
• 9:46
  Speaker 1: to do? Fascinating questions. And I think you'll see from
• 9:49
  Speaker 1: some of these reactions that there's a wide variety of
• 9:52
  Speaker 1: opinions out there. So, as usual, I walked around campus
• 9:56
  Speaker 1: at you see Irvine, and I asked folks this question,
• 9:59
  Speaker 1: can science predict what you're going to think or do?
• 10:02
  Speaker 1: So think about it for a second. Do you think
• 10:04
  Speaker 1: you're just a big, squeehy biological robot machine or do
• 10:08
  Speaker 1: you think you have some sort of free will or
• 10:11
  Speaker 1: some sort of free spirit that nobody can predict what
• 10:14
  Speaker 1: you're going to think or do. Here's what people have
• 10:17
  Speaker 1: to say, not precisely to an extent. There's just so
• 10:22
  Speaker 1: many variables. No, no, not um, because we're humans are unpredictable.
• 10:29
  Speaker 1: I'm a psychology aager. So yeah, I don't believe that
• 10:32
  Speaker 1: we can. We can try, but it's not likely. Yeah,
• 10:39
  Speaker 1: I think so, you think so? Yeah, So then is
• 10:41
  Speaker 1: their free will in that case? Yeah, because it's still
• 10:46
  Speaker 1: the person that'slf, that's stims, just the other person that's
• 10:49
  Speaker 1: from the predicting correctly, So it's not their choice theoretically.
• 10:52
  Speaker 1: I mean, if we did have these capabilities, then that
• 10:55
  Speaker 1: might be possible. We just need to advance. Science is
• 10:59
  Speaker 1: far enough to able to figure that out. No, I don't.
• 11:03
  Speaker 1: I don't think so. So do you think the brain
• 11:05
  Speaker 1: is like not described by physical laws or does something
• 11:07
  Speaker 1: else happening there? No? I think, UM, strong determinism I
• 11:11
  Speaker 1: think is a very uh optimistic Yeah you do. Why
• 11:17
  Speaker 1: is that? Um? I don't know. I just feel like
• 11:20
  Speaker 1: it could, and if it does, does that mean that
• 11:23
  Speaker 1: you have free will or don't have free will? Or um?
• 11:26
  Speaker 1: If you can predict what people are going to think,
• 11:28
  Speaker 1: then probably not. Yes, yes, Why is that? I don't know.
• 11:33
  Speaker 1: I think I saw like a video where they're like
• 11:36
  Speaker 1: trying to map out like a brain or something, and
• 11:39
  Speaker 1: they were talking about like how to recreate it, and
• 11:42
  Speaker 1: that everything we think is just like a series of decisions,
• 11:46
  Speaker 1: and that you can put it in like binary or something,
• 11:48
  Speaker 1: so like zero for yes or one for no, and
• 11:52
  Speaker 1: it all leads to what choices you make. I think
• 11:55
  Speaker 1: that if there were a method for it, it wouldn't
• 11:58
  Speaker 1: be super exact, just because there were so many Does
• 12:00
  Speaker 1: that play into it. I don't think it's possible to
• 12:01
  Speaker 1: be able to predict every single one of them. Yes, yes, um,
• 12:07
  Speaker 1: oh no, no No. So you don't think the brain
• 12:10
  Speaker 1: follows physical laws. I think it does, but I think
• 12:13
  Speaker 1: they're chaotic, chaotic or random. I've given enough information. Are
• 12:19
  Speaker 1: you just a complicated mechanical watch? Uh? No. I think
• 12:25
  Speaker 1: I may or may not subscribe to the view that
• 12:29
  Speaker 1: there's some quantum mechanics at play. So maybe you're not
• 12:33
  Speaker 1: seating quantum mechanics because it's an opening for free will.
• 12:36
  Speaker 1: M M. Now, I don't know if that's true. I'm
• 12:41
  Speaker 1: I'm a pessimist. I feel like eventually, yes, but I
• 12:45
  Speaker 1: feel like we're not there yet. So if so, does
• 12:47
  Speaker 1: that mean that your brain is deterministic but you're just
• 12:50
  Speaker 1: just a product of what's happened in the past and
• 12:52
  Speaker 1: the stimulus it's getting. Obviously there's like some varying factories,
• 12:56
  Speaker 1: but I think there are some things that are like patternistic,
• 12:58
  Speaker 1: like if you're gonna wake up in like drink a coffee,
• 13:00
  Speaker 1: or if you're gonna wake up and like go for
• 13:02
  Speaker 1: a walk, Like those things are patterns that like are habitual.
• 13:06
  Speaker 1: But then there's things that aren't. I feel like you
• 13:08
  Speaker 1: could predict it to an extent, but not like every
• 13:12
  Speaker 1: single action by action, like there might be like faults
• 13:15
  Speaker 1: in the prediction. But yeah, it's hard to do because
• 13:19
  Speaker 1: it's impossible. Just I feel like that's probably impossible because
• 13:23
  Speaker 1: your things can change, you know, very complicated robot. Does
• 13:28
  Speaker 1: that mean that there's no free will? I currently thinking
• 13:34
  Speaker 1: that there's no free will. Yes, Yeah, So maybe that's
• 13:38
  Speaker 1: where it becomes a bit complicated to answer this question,
• 13:41
  Speaker 1: because I do believe that the laws of physics govern
• 13:45
  Speaker 1: everything in the universe, so that would include us, all right.
• 13:49
  Speaker 1: I feel like these answers were all very yes and no.
• 13:52
  Speaker 1: You know, some people were like no, some people are
• 13:55
  Speaker 1: like yes. Nobody said like maybe, I don't know, you know,
• 13:58
  Speaker 1: like people had very opinions. They certainly did, and I
• 14:02
  Speaker 1: should have taken some data to see if these were
• 14:04
  Speaker 1: like all science majors who had confidence as science would
• 14:07
  Speaker 1: eventually figure it out or not. But the last two
• 14:10
  Speaker 1: were maybe the most fun because they turned out to
• 14:12
  Speaker 1: be a husband and wife and he said, yes, he's
• 14:15
  Speaker 1: a complicated robot and she says, no, we're not robots.
• 14:18
  Speaker 1: And then after I left, I heard them arguing about
• 14:21
  Speaker 1: it in a good natured way. In a good natured way, Oh,
• 14:26
  Speaker 1: I see one of them. One of them thought that
• 14:28
  Speaker 1: the other one was predictable and the other one did
• 14:31
  Speaker 1: not like to be predicted. Yeah. I thought that was fascinating.
• 14:34
  Speaker 1: Maybe sparked some dinner table conversation. But you're right, and
• 14:37
  Speaker 1: and in contrast to some other questions, this is definitely
• 14:40
  Speaker 1: a topic everybody felt comfortable giving an answer to. Sometimes
• 14:44
  Speaker 1: I'll ask people a question and they'll be like, what,
• 14:46
  Speaker 1: I never heard of that before or I don't know,
• 14:48
  Speaker 1: but here everybody had something thoughtful to say. Yeah, because
• 14:51
  Speaker 1: I think it touches something very deep within us, you know,
• 14:54
  Speaker 1: just this idea that there's something more to me than
• 14:57
  Speaker 1: just like a big biological clock or a big biological
• 15:02
  Speaker 1: you know, clump of cells doing what there would do
• 15:05
  Speaker 1: without thinking about it. Yeah, I think most people feel
• 15:08
  Speaker 1: like they are steering, even if your body is a
• 15:11
  Speaker 1: big biological robot. They feel like they're in charge. They're
• 15:14
  Speaker 1: making choices. They decide to eat that cookie, or they
• 15:16
  Speaker 1: decide to step on that crack. They feel like they're
• 15:19
  Speaker 1: making these decisions and so so and so. It doesn't
• 15:22
  Speaker 1: sort of jibe with that experience to imagine that those
• 15:25
  Speaker 1: decisions are just the product of the situation you're in
• 15:28
  Speaker 1: an instant before. It's hard to imagine how you could
• 15:32
  Speaker 1: have such a visceral experience of free will if you
• 15:36
  Speaker 1: don't actually have it. All right, so let's stick into
• 15:38
  Speaker 1: the question. Here is the question here, which is a
• 15:41
  Speaker 1: cook science predict what you're going to do. And so
• 15:44
  Speaker 1: let's maybe paint the picture to our listeners about what
• 15:47
  Speaker 1: that might look like. You know, like, how could science,
• 15:50
  Speaker 1: physics or you know, a combination of biology and computer
• 15:53
  Speaker 1: science possibly predict what a human brain might think or do. YEA. So,
• 15:59
  Speaker 1: the typical raggy for understanding something is to think about
• 16:03
  Speaker 1: it in terms of its microscopic bits, like what's going
• 16:05
  Speaker 1: on inside of it? Can we understand those bits and
• 16:09
  Speaker 1: from that build up some sort of understanding, you know,
• 16:12
  Speaker 1: like if you wanted to say understand how a watch worked,
• 16:15
  Speaker 1: you would take it apart and you would say, oh,
• 16:17
  Speaker 1: there's a gear here, and there's a lever there, and
• 16:19
  Speaker 1: this lever touches that gear which turns this thing, and
• 16:22
  Speaker 1: that's how this thing works. And if you can make
• 16:24
  Speaker 1: a model of all the things inside of it, and
• 16:27
  Speaker 1: each one of those things is following the laws of physics,
• 16:30
  Speaker 1: then together the whole thing has to follow the laws
• 16:33
  Speaker 1: of physics. Right. If something is made up of pieces
• 16:36
  Speaker 1: which are predictable, then putting them together it should also
• 16:40
  Speaker 1: be predictable, right you mean like a so science would
• 16:43
  Speaker 1: might be maybe break down your brain and maybe build
• 16:47
  Speaker 1: like a computer model of each one of your neurons
• 16:51
  Speaker 1: and that somehow, you know, acts exactly the same way
• 16:55
  Speaker 1: that is put together the exact same way that your
• 16:58
  Speaker 1: brain is. And so maybe like if you capture your
• 17:01
  Speaker 1: brain in a computer, could that maybe predict what you're thinking,
• 17:06
  Speaker 1: what you'll think and do. Yeah, And it doesn't have
• 17:08
  Speaker 1: to be a computer model in the end, it just
• 17:10
  Speaker 1: has to follow the mathematical laws of physics, and you
• 17:13
  Speaker 1: can express those as a computer model. You could also
• 17:15
  Speaker 1: build a mechanical model. You can imagine building a basically
• 17:19
  Speaker 1: a physical copy of your brain. So that's I think, uh,
• 17:22
  Speaker 1: that detail is not critical the concept. The critical concept
• 17:25
  Speaker 1: is understanding what's going on inside the little bits and
• 17:28
  Speaker 1: then build putting those together to basically replicate your brain.
• 17:33
  Speaker 1: But replicating is not enough. Like if I had a
• 17:35
  Speaker 1: perfect copy of your brain, Like if I created another Hooge,
• 17:39
  Speaker 1: that wouldn't necessarily help me understand what you're going to do.
• 17:42
  Speaker 1: In order to in order to predict what you're gonna do,
• 17:45
  Speaker 1: I need to be able to run experiments. And so
• 17:47
  Speaker 1: you're having a simulation of your brain. I could like say, well,
• 17:50
  Speaker 1: what would Hooge do if I offered him a cookie?
• 17:52
  Speaker 1: Would he say yes or no? So if I had
• 17:54
  Speaker 1: a perfect simulation of your brain, I could run those experiments, right, Yeah,
• 18:00
  Speaker 1: I think it goes to the idea that you know
• 18:02
  Speaker 1: we're really complicated as human beings, as thinking beings. But
• 18:06
  Speaker 1: you know, if you break down our brain, it's made
• 18:09
  Speaker 1: out of you know, lobes and chunks of brain tissue,
• 18:12
  Speaker 1: and those are made out of neurons connected to each other,
• 18:16
  Speaker 1: and neurons are made out of molecules, and so all
• 18:19
  Speaker 1: of these things down to the molecule level kind of
• 18:21
  Speaker 1: follow the laws of physics. You know. It is sort
• 18:24
  Speaker 1: of at the end, just a big and very very complicated,
• 18:27
  Speaker 1: but still a big clock. Yes. And that's a really
• 18:31
  Speaker 1: deeply powerful implication of this discovery that we made a
• 18:35
  Speaker 1: long long time ago, that everything is made out of
• 18:37
  Speaker 1: the same bits. Right, I'm made out of atoms. You're
• 18:40
  Speaker 1: made out of atoms. This chair I'm sitting on is
• 18:42
  Speaker 1: made out of atoms. Things are not made out of
• 18:44
  Speaker 1: their own kind of stuff, which means that they in
• 18:46
  Speaker 1: the end, they all follow the same rules. The rules
• 18:49
  Speaker 1: that hold a chair together are the same rules that
• 18:51
  Speaker 1: hold your cat together, and your hamster together and you together.
• 18:54
  Speaker 1: So if we can figure out what the rules are
• 18:56
  Speaker 1: that describe how molecules and atoms work, that improve instaple.
• 19:00
  Speaker 1: And that's an important distinction and principle we should be
• 19:03
  Speaker 1: able to extrapolate it up and understand how you work. Right,
• 19:07
  Speaker 1: It's kind of like saying, like, you know, we are
• 19:09
  Speaker 1: made out of inanimate bids. You know, I think we're
• 19:12
  Speaker 1: made out of things that are just plain things which
• 19:15
  Speaker 1: you can maybe predict what they're going to do. And
• 19:18
  Speaker 1: so does that mean that we are also inanimate in
• 19:22
  Speaker 1: a way unpredictable. Imagine you came across a robot, for example,
• 19:26
  Speaker 1: and it was doing weird stuff and you want to
• 19:27
  Speaker 1: understand it. How would you understand it? What? You would
• 19:30
  Speaker 1: take it apart and say, oh, it's made out of
• 19:31
  Speaker 1: these pieces, and can I understand each of those pieces?
• 19:33
  Speaker 1: And if so, you could put that together to an
• 19:36
  Speaker 1: understanding of the whole robot. And so the idea is
• 19:39
  Speaker 1: apply that to a person. Yeah, and you could do
• 19:40
  Speaker 1: things like, you know, if I, you know, if I
• 19:43
  Speaker 1: poked this little bit here in this engine, you know,
• 19:46
  Speaker 1: I predicted the and the car is gonna move forward
• 19:49
  Speaker 1: or backwards exactly. And all of this happening, of course,
• 19:52
  Speaker 1: assumes a few things. It assumes that you can get
• 19:56
  Speaker 1: a picture of what's what's happening inside your brain. The
• 19:59
  Speaker 1: idea you presented for choirs that we know each neuron,
• 20:01
  Speaker 1: we know the situation that neuron is in, right, It's
• 20:04
  Speaker 1: like for the watch, it's like that we know where
• 20:06
  Speaker 1: the levers are and where the gears are, and that
• 20:10
  Speaker 1: we can extrapolate up from those levers and gears to
• 20:13
  Speaker 1: the operation of a whole brain. That's not trivial. And
• 20:16
  Speaker 1: then you know, there's questions about like is it actually deterministic,
• 20:20
  Speaker 1: or is there like some funky quantum magic going on,
• 20:22
  Speaker 1: or is there something in there that like science can describe.
• 20:26
  Speaker 1: So it's not a trivial thing to say just just
• 20:28
  Speaker 1: because your brain is made of atoms that we could
• 20:30
  Speaker 1: therefore predict what you're gonna do. Right, Yeah, I think
• 20:33
  Speaker 1: that's the at the core question here, And so let's
• 20:37
  Speaker 1: get into it and um, just to say, some time, Daniel,
• 20:40
  Speaker 1: I will always take the cookie. You don't need a
• 20:42
  Speaker 1: fancy computer simulation to predict that I will take the cookie.
• 20:47
  Speaker 1: All right, I'm gonna cross that off my deep questions
• 20:49
  Speaker 1: of the universal list. Yeah, so let's get into it
• 20:51
  Speaker 1: a little bit more deeply and figure out how you
• 20:54
  Speaker 1: might even do this, or whether we can or if
• 20:57
  Speaker 1: there is some kind of quantum magic that might give
• 20:59
  Speaker 1: a little bit of a loophole to get free will.
• 21:02
  Speaker 1: But first let's take a quick break. All right, we're
• 21:17
  Speaker 1: talking about whether science can predict what you're gonna think
• 21:19
  Speaker 1: and do, and we talked about, um, yeah, that maybe
• 21:22
  Speaker 1: your your brain is made out of little bits and
• 21:24
  Speaker 1: pieces which which are sort of little biological machines. And
• 21:27
  Speaker 1: so does that mean that your whole brain is just
• 21:29
  Speaker 1: a machine or like a robot, which anyone could predict
• 21:33
  Speaker 1: what they're going to do? So let's get into it.
• 21:35
  Speaker 1: What are some of the things Daniel, that physicists know
• 21:38
  Speaker 1: or think that might make that picture really difficult or
• 21:43
  Speaker 1: maybe impossible to predict what you're going to do. The
• 21:46
  Speaker 1: first thing that comes to my mind is just knowing
• 21:49
  Speaker 1: the current status of your brain. Like if I wanted
• 21:52
  Speaker 1: to model the flight of a of a baseball, I
• 21:56
  Speaker 1: wanted to know exactly where baseball went. I would need
• 21:59
  Speaker 1: to know it's current position and it's current direction. If
• 22:03
  Speaker 1: you wanted to know if you wanted to predict where
• 22:05
  Speaker 1: what's gonna land, you would sort of need to know
• 22:08
  Speaker 1: you know where it is and where it's going at
• 22:10
  Speaker 1: any given time. Yeah, and and at any moment, just
• 22:13
  Speaker 1: one moment in time would determine where it's going to be.
• 22:15
  Speaker 1: So if I knew where that baseball was now and
• 22:18
  Speaker 1: where it was in which direction it was going in,
• 22:20
  Speaker 1: I could just apply the law of physics and propagate
• 22:23
  Speaker 1: that information forward. I would say, Oh, it's going to
• 22:25
  Speaker 1: move in a certain path under the under gravity, and
• 22:28
  Speaker 1: I can tell you exactly where it's going to land.
• 22:29
  Speaker 1: That's physics predicting the future, and that works for one
• 22:32
  Speaker 1: little tiny particle or baseball or whatever. But in order
• 22:36
  Speaker 1: to do that for your brain, I would need to
• 22:38
  Speaker 1: know the current situation of your brain, Like, what is
• 22:42
  Speaker 1: the situation of every neuron? Is it about to fire
• 22:44
  Speaker 1: a little pulse? Is it not about to fire pulse?
• 22:47
  Speaker 1: How strong is this connection between it and the next neuron?
• 22:50
  Speaker 1: And you know there's billions and billions of neurons in
• 22:53
  Speaker 1: your brains. We're talking about an enormous amount of information
• 22:56
  Speaker 1: you'd need to know. I think what you mean is
• 22:57
  Speaker 1: that even if I had a giant computers similiation of
• 23:00
  Speaker 1: your brain, I would need to give each of the
• 23:02
  Speaker 1: neurons kind of a starting value, right, or like I
• 23:05
  Speaker 1: would need to know where each neuron was in order
• 23:09
  Speaker 1: to predict what the whole brain was going to do. Precisely.
• 23:12
  Speaker 1: If I had a computer program that could simulate an
• 23:14
  Speaker 1: arbitrary brain, I would somehow need to configure it to
• 23:17
  Speaker 1: your brain. And that would mean knowing where all the
• 23:20
  Speaker 1: neurons were and how they were connected to each other
• 23:22
  Speaker 1: and all that kind of stuff. Just like if you
• 23:24
  Speaker 1: want to calculate where the baseball is going to go,
• 23:26
  Speaker 1: you need to know the initial conditions. You need to
• 23:28
  Speaker 1: know where it is now and which direction is going in.
• 23:31
  Speaker 1: So like, even if I had that computer program, and
• 23:34
  Speaker 1: even if it was possible um to use that computer
• 23:37
  Speaker 1: program to predict the future, how do I get that
• 23:39
  Speaker 1: information from your brain? Do I have to like scan
• 23:42
  Speaker 1: your brain somehow? You have to take your head off
• 23:45
  Speaker 1: and slice it into hyperfine little bits? Like how do
• 23:48
  Speaker 1: you even physically do that? Right? Because each neuron might
• 23:52
  Speaker 1: be in a different state, you know, like one win
• 23:54
  Speaker 1: might be more excited than others, or you might be
• 23:57
  Speaker 1: you know, you might be in a bad mood. And
• 23:59
  Speaker 1: so there's a general I don't know dopamine levels that
• 24:02
  Speaker 1: are you know, suppressing some of your neurons right now.
• 24:05
  Speaker 1: You need to know that in order to predict what
• 24:07
  Speaker 1: you're going to think and do. Yeah, And you need
• 24:08
  Speaker 1: to know it all at the same moment. Right, I
• 24:10
  Speaker 1: don't want to know what the left half of your
• 24:12
  Speaker 1: brain is doing right now and the right half of
• 24:14
  Speaker 1: your brain is doing two seconds ago. I need a
• 24:16
  Speaker 1: complete picture of your brain at one moment, so that
• 24:20
  Speaker 1: I know how what direction everything is moving in. I'm
• 24:23
  Speaker 1: just thinking of your brain is like a lot of
• 24:25
  Speaker 1: tiny little baseballs, and so I need to know where
• 24:28
  Speaker 1: all those baseballs are at the same time. So even
• 24:30
  Speaker 1: slicing your brain, you know, like a ham sandwich or whatever,
• 24:33
  Speaker 1: wouldn't work. I need to somehow instantly scan everything in
• 24:37
  Speaker 1: your brain. And that just seems almost impossible, right, It
• 24:43
  Speaker 1: seems almost impossible. I mean I read a lot of
• 24:45
  Speaker 1: science fiction, and there's some good stuff where the people
• 24:49
  Speaker 1: are uploading brains into the cloud, and sometimes they talk
• 24:52
  Speaker 1: about how scanning the brain is a destructive process, but
• 24:54
  Speaker 1: they never talked about how fast it has to be. Like,
• 24:57
  Speaker 1: if you want to scan the brain, it's got to
• 24:58
  Speaker 1: be a snapshot. You gotta know where everything is in
• 25:01
  Speaker 1: an instant or it's all averaged like over the last
• 25:05
  Speaker 1: five minutes or ten minutes or twenty minutes, or however
• 25:08
  Speaker 1: long it takes to scan the brain. And that's going
• 25:10
  Speaker 1: to be a disaster. So this is a technological issue,
• 25:13
  Speaker 1: not philosophical. But I don't know if that's even possible.
• 25:16
  Speaker 1: Oh I see, Okay, so that's kind of the first
• 25:18
  Speaker 1: um barrier that might prevent you from predicting somebody's brain
• 25:22
  Speaker 1: is getting a snapshot of it. But uh, and it
• 25:26
  Speaker 1: sounds impossible, but I think technically you can't rule it
• 25:28
  Speaker 1: out right, Like, it's not technically or theoretically impossible that
• 25:33
  Speaker 1: maybe one day people will come up with a brain
• 25:36
  Speaker 1: scan that can somehow capture all of your brain in
• 25:38
  Speaker 1: one instant. That's right, unless you need to know it's
• 25:41
  Speaker 1: sort of at the level of quantum mechanical objects, like
• 25:43
  Speaker 1: you need to know the exact location of every electron.
• 25:47
  Speaker 1: Then it's theoretically impossible because you can't measure the perfect
• 25:50
  Speaker 1: quantum state of the entire brain all at the same time.
• 25:54
  Speaker 1: I think that's theoretically impossible. But as long as it's
• 25:56
  Speaker 1: you know, not necessarily sensitive to quantum effects in theory,
• 25:59
  Speaker 1: it's possible, but technologically way out of our grasp today today. Yeah,
• 26:03
  Speaker 1: let's assume that that's possible and then think about what
• 26:06
  Speaker 1: the other problems would be. Yeah, yeah, because that's that's
• 26:09
  Speaker 1: not that's not the only problem is scanning your brain.
• 26:13
  Speaker 1: There are first we have to achieve this almost impossible
• 26:16
  Speaker 1: sounding technological feat. Then we got the real problems. Yeah, well,
• 26:20
  Speaker 1: you know that's what they said about the iPhone. And
• 26:22
  Speaker 1: look where we are now? Is that? Is that what
• 26:24
  Speaker 1: they said about the iPhone? Was it that? This was
• 26:27
  Speaker 1: that at the physics meeting you had with Einstein and
• 26:29
  Speaker 1: you know all those guys, Well it was science pitching
• 26:31
  Speaker 1: like fifty years ago, right, like this device that fit
• 26:34
  Speaker 1: in your pocket and you can talk video with people
• 26:37
  Speaker 1: and play addictive video games on for hours. I mean
• 26:40
  Speaker 1: that was like a Star trek. You know that's true.
• 26:43
  Speaker 1: And it'd be folly to say this kind of technology
• 26:46
  Speaker 1: will never be developed or would take years or centuries
• 26:49
  Speaker 1: or whatever, because those kind of predictions are always people
• 26:52
  Speaker 1: always look silly five years later after making those predictions.
• 26:56
  Speaker 1: So we should avoid that. And maybe technology will progress
• 26:58
  Speaker 1: rapidly and they'll invent the brain scan next week, and
• 27:01
  Speaker 1: they'll have a brain scan app that you can put
• 27:03
  Speaker 1: on your phone. Maybe, But then there are other things
• 27:06
  Speaker 1: that will would make this really difficult or that kind
• 27:09
  Speaker 1: of make your brain almost pretty much unpredictable. Right, Yeah,
• 27:13
  Speaker 1: there are a lot of practical issues and actually accomplishing
• 27:16
  Speaker 1: predicting what your brain is going to do. Say, we
• 27:18
  Speaker 1: had a snapshot at your brain, we knew the current
• 27:21
  Speaker 1: status of every neuron. We could load that into the computer,
• 27:24
  Speaker 1: and we wanted to run that forward. We wanted to say,
• 27:26
  Speaker 1: all right, well, what's this person going to do in
• 27:29
  Speaker 1: five minutes. That's not always easy. Some systems, even if
• 27:33
  Speaker 1: they're made of atoms which follow physical laws, and even
• 27:36
  Speaker 1: if you knew where all those atoms were, are hard
• 27:39
  Speaker 1: to describe. Yeah, because there um you're telling me earlier.
• 27:44
  Speaker 1: They're not easy systems to kind of simulate, or they're
• 27:48
  Speaker 1: not easy systems to um to kind of predict what
• 27:53
  Speaker 1: they're going to do. Yeah, and this is again a
• 27:55
  Speaker 1: technological problem, but it's a real problem, Like why can't
• 27:58
  Speaker 1: we predict the weather? In theory, weather follows rules that
• 28:02
  Speaker 1: we understand water droplets and air resistance and wind and
• 28:06
  Speaker 1: all that stuff. That's not complicated physics. They're just atoms. Yeah,
• 28:09
  Speaker 1: they're just atoms, And we don't even need to worry
• 28:11
  Speaker 1: about the atoms. We can think about the water droplets
• 28:13
  Speaker 1: and the temperature and stuff. So why is it so
• 28:16
  Speaker 1: hard to predict when it's going to rain or when
• 28:18
  Speaker 1: a hurricane is going to come. If it's following the
• 28:20
  Speaker 1: laws of physics, and we have amazing satellites that are
• 28:23
  Speaker 1: like gathering all this data about what the temperature is everywhere,
• 28:26
  Speaker 1: it's a very similar problem. The reason that weather it
• 28:30
  Speaker 1: is hard to predict is not because it doesn't follow
• 28:32
  Speaker 1: the laws of physics. It's because it's very, very sensitive
• 28:36
  Speaker 1: to the exact tiny details of the situation. A small
• 28:40
  Speaker 1: change in temperature over here leads to a big change
• 28:43
  Speaker 1: over there. That's something we call chaos. Right, It's this
• 28:46
  Speaker 1: idea that the kind of goes back to that whole
• 28:48
  Speaker 1: butterfly idea, right, Like like they say that if a
• 28:52
  Speaker 1: butterfly flaps its wing in one part of the world,
• 28:55
  Speaker 1: it can actually affect the weather in another part of
• 28:58
  Speaker 1: the world, just because it's so sensitive to even small
• 29:02
  Speaker 1: things like a butterfly flapping its wing. That's right, and
• 29:05
  Speaker 1: not every system is chaotic. Some things are not right
• 29:08
  Speaker 1: some things. For example, when you throw a ball, you
• 29:10
  Speaker 1: throw a ball, if you change the angle you're throwing
• 29:13
  Speaker 1: it at by a tiny bit, then the outcome is
• 29:16
  Speaker 1: changed by a tiny bit. Right, you throw it a
• 29:19
  Speaker 1: tiny bit harder, it lands, it goes a tin a
• 29:21
  Speaker 1: bit further. But other things, if you change how you
• 29:24
  Speaker 1: do them by a tiny bit, you get a very
• 29:25
  Speaker 1: different outcome, Like rolling a die. If you roll a
• 29:29
  Speaker 1: dice slightly differently, you spin your hand to tying a
• 29:31
  Speaker 1: bit differently, you get a four instead of a two,
• 29:33
  Speaker 1: or a five instead of a one. So the outcome
• 29:36
  Speaker 1: depends very very sensitive and exactly how you did it.
• 29:39
  Speaker 1: It's not random, it's following the laws of physics. In theory,
• 29:43
  Speaker 1: it's predictable, but in practice it's very difficult because the
• 29:47
  Speaker 1: outcome depends very sensitively on how you flap those butterfly wings, yeah,
• 29:51
  Speaker 1: or how you throw the die. Like to predict a
• 29:53
  Speaker 1: die roll, you would need to really kind of like
• 29:56
  Speaker 1: pay attention to the exact angle that each I is
• 30:01
  Speaker 1: at when it leaves your hand, and you have to
• 30:03
  Speaker 1: predict also or simulate how like when the corners hit
• 30:07
  Speaker 1: the ground, how that's going to affect the spin of
• 30:09
  Speaker 1: the die. And so it's really it's just a much
• 30:12
  Speaker 1: more complicated system to simulate and predict than like just
• 30:16
  Speaker 1: throwing a baseball. So then the question is, is your
• 30:19
  Speaker 1: brain like a big bag of dice with very each
• 30:22
  Speaker 1: one very difficult to predict and bouncing off the other one.
• 30:24
  Speaker 1: It's a very complex thing, or is it made out
• 30:27
  Speaker 1: of things which are easy to predict? And if you
• 30:30
  Speaker 1: knew pretty much the current situation that you could predict
• 30:34
  Speaker 1: how it's going to come about? You know, is your
• 30:36
  Speaker 1: brain a hurricane or is it just a baseball right?
• 30:39
  Speaker 1: Or you know, if I have butterflies in my stomach
• 30:41
  Speaker 1: and those they flap, what's how is it going to
• 30:44
  Speaker 1: affect my decision to eat a cookie or not exactly?
• 30:47
  Speaker 1: So the point is that even if you knew exactly
• 30:49
  Speaker 1: the current situation of the brain, could you have a
• 30:52
  Speaker 1: powerful enough computer to predict what it's going to happen
• 30:55
  Speaker 1: going forward? And in the end, that's really the limitation.
• 30:58
  Speaker 1: For example, for hurricanes, if we the location of every
• 31:01
  Speaker 1: drop of water on Earth and its current position and velocity,
• 31:04
  Speaker 1: and we had a super duper powerful computer, then yeah,
• 31:07
  Speaker 1: we could probably predict the weather very accurately. But we
• 31:11
  Speaker 1: don't have those computers right well, were I feel like
• 31:14
  Speaker 1: we're getting better though, you know, like sometimes I'm really
• 31:16
  Speaker 1: impressed by how far ahead we can predict the weather
• 31:19
  Speaker 1: and how somewhat accurately, we can. This is just a
• 31:22
  Speaker 1: hurdle of technology. Something of the fastest computers in the world,
• 31:26
  Speaker 1: like the supercomputers, are all devoted to this problem predicting
• 31:30
  Speaker 1: the future of the weather, understanding the atmosphere and all
• 31:32
  Speaker 1: of its chaos, and so it's really just like throwing
• 31:35
  Speaker 1: more computational power at the problem. Well, and that's because
• 31:39
  Speaker 1: weather is a chaotic system, right, Definitely, weather is very chaotic.
• 31:42
  Speaker 1: It's very difficult to predict the outcome, even if you
• 31:45
  Speaker 1: know the current conditions. But do we know if the
• 31:48
  Speaker 1: brain is chaotic or humans or chaotic? Humans seem chaoic
• 31:52
  Speaker 1: to me. I mean some of them I've known for
• 31:53
  Speaker 1: decades and I still don't understand why. Then good decisions
• 31:56
  Speaker 1: they do, and not just their children, not just of children. No,
• 32:01
  Speaker 1: we don't know for sure, but it seems to me
• 32:03
  Speaker 1: very likely. I mean, it's a hyper connected, very sensitive
• 32:07
  Speaker 1: set of neurons. It seems to me very unlikely that
• 32:10
  Speaker 1: it wouldn't be a chaotic, But we don't know. It
• 32:12
  Speaker 1: might be that there are sort of emergent phenomenon that
• 32:16
  Speaker 1: you're not really sensitive to all the little details, and
• 32:19
  Speaker 1: that you could build a model that predicts roughly where
• 32:22
  Speaker 1: things are going. If you don't care about the details,
• 32:25
  Speaker 1: you can tell whether somebody's gonna have a cookie and
• 32:28
  Speaker 1: who they're going to vote for the next election. It's
• 32:30
  Speaker 1: possible that you could build those models, but that requires
• 32:32
  Speaker 1: a sort of another layer of insight. Right. Imagine you
• 32:36
  Speaker 1: only knew the baseball in terms of little particles inside
• 32:39
  Speaker 1: of it. You're like, oh, there's no way I could
• 32:41
  Speaker 1: predict the way this baseball is gonna move. There's ten
• 32:43
  Speaker 1: to the twenty three particles. It's impossible. But if you understand,
• 32:47
  Speaker 1: if you took a step back and saw the flight
• 32:49
  Speaker 1: of it, you can say, oh, actually, I can describe
• 32:51
  Speaker 1: this and ignore all the particles. I can ignore all
• 32:53
  Speaker 1: those details they're not relevant, and I can just describe
• 32:56
  Speaker 1: this in terms of simple motion. So it's possible that
• 32:59
  Speaker 1: there's an emergent theory of psychology mathematical psychology that could
• 33:03
  Speaker 1: describe the motion of the brain. We just don't know,
• 33:06
  Speaker 1: or I wonder if it might vary with people. You know,
• 33:09
  Speaker 1: some people might be more predictable than others. Yeah, I
• 33:13
  Speaker 1: certainly know some people who seem pretty chaotic. All right, Well,
• 33:17
  Speaker 1: those are two pretty big hurdles, and but then there's
• 33:19
  Speaker 1: um another hurdle coming up and and or possibly a
• 33:23
  Speaker 1: loophole that might still let us have some free will
• 33:27
  Speaker 1: in this deterministic machine like universe. So let's get into that.
• 33:31
  Speaker 1: But first let's take a quick break, all right, Daniel.
• 33:47
  Speaker 1: The last topic here is about randomness and whether randomness
• 33:53
  Speaker 1: at the quantum level can maybe effect what we see
• 33:57
  Speaker 1: as free will, or whether it can affect with or
• 34:00
  Speaker 1: not we can predict what people will think or do.
• 34:02
  Speaker 1: The whole premise here, The assumption we're making is that
• 34:04
  Speaker 1: the brain should be predictable if it's various bits are predictable,
• 34:08
  Speaker 1: if it's made out of predictable bits, then you should
• 34:10
  Speaker 1: be able to put those predictable bits together into a
• 34:13
  Speaker 1: predictable brain. But listeners of the podcast are probably wondering,
• 34:17
  Speaker 1: are those bits predictable? The brain is made of atoms,
• 34:20
  Speaker 1: and atoms are made of protons and electrons and all
• 34:22
  Speaker 1: this stuff, and we know that those things are quantum mechanical,
• 34:25
  Speaker 1: and we talked on the podcast recently about the crazy
• 34:28
  Speaker 1: probabilistic nature or quantum mechanics, that things are not determined
• 34:33
  Speaker 1: until their measures, that there is really true randomness at
• 34:36
  Speaker 1: the quantum scale. So you might be wondering, how can
• 34:39
  Speaker 1: you build determinism on top of this fuzzy quantum randomness. Yeah,
• 34:43
  Speaker 1: because we talked about some of the challenges, right, We
• 34:45
  Speaker 1: talked about scanning your brain and about chaos, and but
• 34:48
  Speaker 1: those are technical problems. You're saying that maybe just in
• 34:52
  Speaker 1: the bits themselves of the brain, there is some inherent
• 34:55
  Speaker 1: randomness that might make it impossible to predict. Absolutely, we
• 35:00
  Speaker 1: know that there's inherent randomness. Everything, as we say, is
• 35:03
  Speaker 1: made of atoms, and those things are governed by fundamentally
• 35:06
  Speaker 1: quantum mechanical properties. What we don't know is if that matters, right,
• 35:11
  Speaker 1: It certainly doesn't matter for predicting the motion of a
• 35:14
  Speaker 1: mechanical watch. People can build mechanical watches that are super
• 35:18
  Speaker 1: accurate for years at a time. We can predict the
• 35:20
  Speaker 1: flight of a baseball without even knowing that quantum mechanics
• 35:23
  Speaker 1: was the thing. I remember. Quantum mechanics affects things only
• 35:26
  Speaker 1: on super duper tiny scales. The uncertainty principle delta x
• 35:31
  Speaker 1: delta P the relationship between the uncertainty and motion and position.
• 35:36
  Speaker 1: The uncertainty there is is related to this. Planks constant
• 35:39
  Speaker 1: each bar, which is like the fundamental unit of the universe.
• 35:42
  Speaker 1: But this is a super tiny number. It's ten to
• 35:45
  Speaker 1: the minus thirty four jewels. And so it might be
• 35:49
  Speaker 1: that on top of the quantum randomness, we do have
• 35:52
  Speaker 1: a layer of physics which is deterministic. Or it could
• 35:55
  Speaker 1: be that you know that the quantum randomness sort of
• 35:57
  Speaker 1: seeps up from below and affects the world working of
• 36:00
  Speaker 1: the brain. Okay, so you're saying that there is a
• 36:03
  Speaker 1: fundamental randomness in the universe and in my brain cells,
• 36:08
  Speaker 1: Like at the to the smallest level, I can't possibly
• 36:13
  Speaker 1: predict my brain, but maybe if I go up a
• 36:16
  Speaker 1: few levels, then the brain starts to be more predictable.
• 36:20
  Speaker 1: We see this in physics. We see at the very
• 36:22
  Speaker 1: smallest levels you cannot predict what's going to happen. You
• 36:25
  Speaker 1: shoot the same photon into the same experiment twice, you
• 36:27
  Speaker 1: get two different outcomes. There's really true randomness there, and
• 36:31
  Speaker 1: that breaks determinism. So photons not deterministic, electrons not deterministic.
• 36:36
  Speaker 1: Protons not deterministic. But somehow, when you put these things together,
• 36:42
  Speaker 1: you put enough of them together, all those random fluctuations
• 36:45
  Speaker 1: average out that basically cancel each other. This is a
• 36:48
  Speaker 1: deep theorem in physics. It's called the air infest theorem,
• 36:51
  Speaker 1: that you have enough of these things and it doesn't
• 36:53
  Speaker 1: matter anymore as long as you're measuring things on the
• 36:55
  Speaker 1: sort of classical scale and sizes that we care about centimeters, millimeters.
• 37:00
  Speaker 1: Even then the quantum mechanic effects average out, which is
• 37:03
  Speaker 1: why we didn't notice quantum mechanics for thousands of years.
• 37:06
  Speaker 1: It's hidden inside the summation of all of these quantum
• 37:09
  Speaker 1: little events. Yeah, which is why it was so hard
• 37:12
  Speaker 1: to accept. Right, we had just accepted the mind boggling
• 37:15
  Speaker 1: consequences of a deterministic universe, like, wow, the universe seems
• 37:19
  Speaker 1: to follow rules and we can predict it. And then
• 37:21
  Speaker 1: we discovered, oh, actually no, at its deepest level, it
• 37:23
  Speaker 1: seems weirdly random. And that was totally in contradiction with
• 37:27
  Speaker 1: everything we thought we understood, which is why quantum mechanics
• 37:29
  Speaker 1: are so counterintuitive and so fuzzy. But still, it didn't
• 37:33
  Speaker 1: mean that all those things we learned were wrong. It
• 37:36
  Speaker 1: just meant that they applied to sort of the larger scales,
• 37:39
  Speaker 1: that those same rules can't be applied to electrons and protons.
• 37:43
  Speaker 1: But they can still be applied to baseballs and watches.
• 37:46
  Speaker 1: So then the question is is your brain a baseball
• 37:49
  Speaker 1: and watch or is it an electron. Yeah, it's kind
• 37:52
  Speaker 1: of like if I said, um, hey, Dana, I'm gonna
• 37:55
  Speaker 1: flip this coin, and if it's head, i'm gonna take
• 37:57
  Speaker 1: your cookie, and if its tails, I'm not going to
• 37:59
  Speaker 1: take your cookie. Then it then it's kind of like
• 38:02
  Speaker 1: then you might say it's unpredictable what am I gonna do? Right,
• 38:04
  Speaker 1: because you don't know except that a coin is deterministic.
• 38:07
  Speaker 1: A coin is a classical object. If I knew exactly
• 38:10
  Speaker 1: how you're gonna flip it, I could predict exactly how
• 38:12
  Speaker 1: that coin was gonna land, and I would know that
• 38:15
  Speaker 1: you were going to take the cookie no matter how
• 38:16
  Speaker 1: the coin flipped, actually, because I know you as a person,
• 38:19
  Speaker 1: but but yeah, exactly the coin flip is not written.
• 38:22
  Speaker 1: But if you, for example, how a radioactive particle and
• 38:25
  Speaker 1: you said, I'm gonna wait one minute. If the particle
• 38:28
  Speaker 1: decays in within this minute, I'm eating the cookie. And
• 38:30
  Speaker 1: if it doesn't decay within this minute, I'm not eating
• 38:33
  Speaker 1: the cookie. Then I can't predict whether or not you're
• 38:35
  Speaker 1: gonna eat the cookie. That's because you linked a macroscopic
• 38:38
  Speaker 1: action eating the cookie to a quantum mechanical thing. That's rare. Right,
• 38:42
  Speaker 1: that's a whole Shrowdinger's box argument. It's very difficult to
• 38:46
  Speaker 1: find quantum mechanic effects that you can see on the
• 38:48
  Speaker 1: macroscopic scale. Oh, I see, like, if if I made
• 38:52
  Speaker 1: my cookie eating dependent on whether this particle decays or not,
• 38:59
  Speaker 1: then it's unpredictable. But if I make my eating the
• 39:03
  Speaker 1: cookie depending on whether the particle will decay over a minute,
• 39:07
  Speaker 1: then we know a lot more. Right, it's less unpredictable
• 39:10
  Speaker 1: because you know that on average, most of these particles,
• 39:14
  Speaker 1: let's say, like these particles decay within a minute. Yeah,
• 39:17
  Speaker 1: that's true. And if you want to make statements about averages,
• 39:21
  Speaker 1: then you're a rock solid footing even quantum mechanically, because
• 39:23
  Speaker 1: quantum mechanics doesn't mean things don't follow laws. It just
• 39:26
  Speaker 1: means those laws are probabilistic. And so you can say,
• 39:29
  Speaker 1: on average, this is going to happen. On average, that's
• 39:31
  Speaker 1: gonna happen for one particular particle. You can't make any
• 39:34
  Speaker 1: predictions on a quantum mechanical scale, but you know what
• 39:37
  Speaker 1: quantum mechanical effects do you observe as a person, Like
• 39:41
  Speaker 1: for quantum, for something to affect your life that's really random,
• 39:45
  Speaker 1: it has to have an impact like that somebody's measuring
• 39:48
  Speaker 1: this quantum mechanical thing and making a decision based on it.
• 39:51
  Speaker 1: It's really pretty rare for quantum mechanical randomness to affect
• 39:54
  Speaker 1: the macroscopic world. People do really complicated experiments to try
• 39:58
  Speaker 1: to set this kind of thing up. Bose Einstein condensate.
• 40:02
  Speaker 1: It's like a macroscopic quantum state that you can see
• 40:05
  Speaker 1: that actually behaves quantum mechanically. It's really weird and amazing,
• 40:08
  Speaker 1: and people want the Nobel Prize for that. So two,
• 40:12
  Speaker 1: for the brain to be dependent on quantum mechanical randomness,
• 40:15
  Speaker 1: you'd have to show somehow that like the motion of
• 40:18
  Speaker 1: these electrons and this weird quantum mechanical effect was triggering neurons,
• 40:23
  Speaker 1: and neurons were somehow sensitive to these things. Yeah, So
• 40:26
  Speaker 1: it's so it's it's it's fundamentally random. But the question,
• 40:30
  Speaker 1: it seems like the core and the key question here
• 40:32
  Speaker 1: is whether that randomness at the quantum level really affects
• 40:36
  Speaker 1: whether I'm going to eat my cookie or not, or
• 40:38
  Speaker 1: whether that all gets drowned out by the Brazilian electrons
• 40:41
  Speaker 1: that I have in my brain. And there are some
• 40:43
  Speaker 1: folks who have made that argument, and I think that
• 40:46
  Speaker 1: that argument is largely in response to fear of determinism.
• 40:50
  Speaker 1: They don't want to think that the that the brain
• 40:52
  Speaker 1: is just a mechanical watch that can be predicted. So
• 40:54
  Speaker 1: there's sort of striving for some way to leave a
• 40:58
  Speaker 1: window open for free will. And they, oh, well, if
• 41:00
  Speaker 1: we can connect it to quantum randomness, then you know
• 41:03
  Speaker 1: you can't be predicted and therefore there might be room
• 41:05
  Speaker 1: for free will. And famous people like Roger Penrose make
• 41:08
  Speaker 1: this argument, but it's just a it's more like a
• 41:11
  Speaker 1: an outline of an argument. There's no evidence that the
• 41:14
  Speaker 1: brain is dependent on quantum mechanic. It's just like, can
• 41:17
  Speaker 1: we find some path maybe go down that leads us
• 41:20
  Speaker 1: to a nondeterministic brain. There's no real evidence or argument there.
• 41:24
  Speaker 1: It's just like a suggestion. Well, the I think the
• 41:27
  Speaker 1: thing is that it's it's a it's a yes or
• 41:30
  Speaker 1: no decision, right, whether I eat the cookie, it's either
• 41:32
  Speaker 1: yes or no. So I'm kind of like sitting on
• 41:35
  Speaker 1: the edge of a really thin knife, right, and you
• 41:39
  Speaker 1: know who knows. Are you can you really rule out
• 41:42
  Speaker 1: that you know how one particular electron behaved it was
• 41:46
  Speaker 1: resulted in pushing me one way or the other, Or
• 41:49
  Speaker 1: are you saying that pretty much that's unlikely. No, I'm
• 41:54
  Speaker 1: not saying that we can rule it out at all.
• 41:55
  Speaker 1: You're totally right, and it could be that there are
• 41:57
  Speaker 1: quantum mechanic effects that determine whether you make that decision.
• 42:01
  Speaker 1: It's possible, but I'm saying we have no evidence for that.
• 42:04
  Speaker 1: Nobody has shown that we don't even understand the mechanism
• 42:06
  Speaker 1: of it. That doesn't mean it's not happening. It just
• 42:08
  Speaker 1: means that it's more of a suggestion, an open door,
• 42:12
  Speaker 1: than an actual idea. All right, well, I feel I
• 42:15
  Speaker 1: feel pretty good. I feel like we um we're right
• 42:17
  Speaker 1: where we predicted we would be. I think you deserved
• 42:20
  Speaker 1: a cookie or a banana at the very list. Oh no, wait,
• 42:25
  Speaker 1: do you say? I would say, there you go? But
• 42:28
  Speaker 1: I think that there's some fascinating questions there, like even
• 42:31
  Speaker 1: if you knew the answer to this question, even if
• 42:33
  Speaker 1: you showed that the brain was deterministic or wasn't that
• 42:36
  Speaker 1: the brain was quantum mechanically random, what would that mean
• 42:40
  Speaker 1: for this experience of free will. Let me see if
• 42:43
  Speaker 1: I can readat what we learned. We learned that it's
• 42:46
  Speaker 1: predicting what people are going to do is super technically
• 42:50
  Speaker 1: hard with the scanning all of the neurons in your
• 42:53
  Speaker 1: brain and chaos maybe playing a large party making it unpredictable.
• 42:58
  Speaker 1: But let's say like we invented technology to take care
• 43:00
  Speaker 1: of that, there's still sort of the question of whether
• 43:03
  Speaker 1: randomness trickles up to influence decisions, you know, randomness at
• 43:09
  Speaker 1: the quantum level, whether that trickles up to influenced decisions,
• 43:12
  Speaker 1: And it sounds like we we we don't know. It
• 43:14
  Speaker 1: sounds like we can't say either way. That's right. Quantum
• 43:16
  Speaker 1: mechanics might make it theoretically impossible to predict the brain.
• 43:21
  Speaker 1: We don't know, And if it doesn't, if it doesn't
• 43:23
  Speaker 1: make it impossible, and we know, it's still super duper
• 43:26
  Speaker 1: hard because you need to know the exact state of
• 43:29
  Speaker 1: the brain and you need to overcome the potential overwhelming
• 43:32
  Speaker 1: chaos of your billions and billions of neurons. So even
• 43:36
  Speaker 1: if it's not impossible, it's definitely very very tricky, right,
• 43:40
  Speaker 1: And then we could probably have a whole podcast just
• 43:42
  Speaker 1: on the implications on how we feel about free will
• 43:45
  Speaker 1: and whether it would still exist even if it was
• 43:48
  Speaker 1: governed by quantum randomness. That's right, And this is an
• 43:51
  Speaker 1: area of philosophy, And neither you nor I have formal
• 43:55
  Speaker 1: training in philosophy. What do you mean have a doctorate
• 43:57
  Speaker 1: in philosophy in engineerings. So then let me ask you, Jorge,
• 44:03
  Speaker 1: if I could prove that you were deterministic, does that
• 44:06
  Speaker 1: mean you don't have free will? This being a big
• 44:09
  Speaker 1: mechanical robot mean you're not making choices? Or is that
• 44:11
  Speaker 1: just describe the choices you are you are making? I
• 44:15
  Speaker 1: think that would be bananas. No, I think for me
• 44:18
  Speaker 1: it sort of doesn't matter. I feel like it doesn't matter.
• 44:20
  Speaker 1: I'm not I'm not someone who sweats free will too much.
• 44:23
  Speaker 1: To be honest, Like, I feel like it might be
• 44:25
  Speaker 1: a robot. That's fine, And um, you know, currently it's
• 44:29
  Speaker 1: almost like who would want to predict what I'm going
• 44:31
  Speaker 1: to do? Do you know what I mean? Like, it's
• 44:33
  Speaker 1: such like, why is this problem even interesting? Who would
• 44:37
  Speaker 1: take the time to care about what I'm going to
• 44:39
  Speaker 1: think and do? Um? So, as long as nobody else
• 44:42
  Speaker 1: can think or would want to know, what I think
• 44:44
  Speaker 1: and do. Then to me like, okay, I could be
• 44:46
  Speaker 1: a robot or I could not be a robot. Well,
• 44:48
  Speaker 1: I think there's an implication of moral responsibility. We've been
• 44:51
  Speaker 1: talking about you eating a cookie. What if that was
• 44:53
  Speaker 1: somebody else's cookie, right, and you ate that person's cookie,
• 44:57
  Speaker 1: Then could you say, Hey, I didn't make that choice.
• 44:59
  Speaker 1: I have no free will, and therefore I can't be
• 45:02
  Speaker 1: more morally culpable. If everybody is deterministic and people aren't
• 45:05
  Speaker 1: making choices, than our sort of whole theory of morality
• 45:08
  Speaker 1: kind of falls apart and we can't really punish anybody
• 45:12
  Speaker 1: for anything. All right, we'll say that. For our other podcast,
• 45:15
  Speaker 1: Daniel and Jorge explain the Moral Universe. Daniel and Jorge
• 45:19
  Speaker 1: um blather on about philosophy. They don't really know what
• 45:21
  Speaker 1: they're talking about. I would listen to that, and I
• 45:24
  Speaker 1: predict that a lot of people will probably not. But
• 45:27
  Speaker 1: I think these are, like many of the things that
• 45:29
  Speaker 1: we talked about on the show, there are deep implications
• 45:31
  Speaker 1: for just what it means to be human being. Often
• 45:34
  Speaker 1: we talk about how the universe was created and how
• 45:37
  Speaker 1: it came to be in his future, and that has
• 45:39
  Speaker 1: deep importance for people, how what people think about their
• 45:43
  Speaker 1: place in the cosmos and how they should live their lives.
• 45:45
  Speaker 1: And this is similar. It's a question, you know, is
• 45:47
  Speaker 1: this the threshold that science will ever cross? And if so,
• 45:50
  Speaker 1: what does it mean? And so I love the fact
• 45:52
  Speaker 1: that science is so relevant sometimes that everything touches the
• 45:55
  Speaker 1: deep core of how you're gonna live your life and
• 45:57
  Speaker 1: what it means to be human. Yeah, it's amazing to
• 45:59
  Speaker 1: think that physics can have such an impact in who
• 46:02
  Speaker 1: we are as a in our souls, in our conception
• 46:06
  Speaker 1: of who we are. Well, physics is deep in my
• 46:08
  Speaker 1: soul and now maybe you're discovering it's actually deep in
• 46:11
  Speaker 1: your soul as well. We got you, We infected your soul,
• 46:16
  Speaker 1: dear audience, physics. All right, Well, thanks for joining us.
• 46:19
  Speaker 1: We hope you enjoyed that, and we hope that meant
• 46:22
  Speaker 1: all of your expectations. But what this podcast was going
• 46:25
  Speaker 1: to be, that's right. Thanks for tuning in. And for
• 46:27
  Speaker 1: those of you who wonder whether science can actually explain
• 46:31
  Speaker 1: the universe, remember that we don't know. Science has worked
• 46:34
  Speaker 1: pretty well so far and been able to explain a
• 46:36
  Speaker 1: lot of what we've seen, but there might be some
• 46:38
  Speaker 1: day in the future where we find some phenomenon that
• 46:40
  Speaker 1: can't be explained by science, or by our current visioning
• 46:43
  Speaker 1: of science or our mathematical rules. So we are continuing
• 46:47
  Speaker 1: on this journey of trying to explore and explain the
• 46:49
  Speaker 1: universe we find around us. Thanks for joining us, see
• 46:52
  Speaker 1: you next time. Before you still have a question after
• 47:02
  Speaker 1: listening to all these explanations, please drop us the line.
• 47:05
  Speaker 1: We'd love to hear from you. You can find us
• 47:07
  Speaker 1: on Facebook, Twitter, and Instagram at Daniel and Jorge That's
• 47:11
  Speaker 1: one Word, or email us at Feedback at Daniel and
• 47:15
  Speaker 1: Jorge dot com. Thanks for listening and remember that Daniel
• 47:18
  Speaker 1: and Jorge Explain the Universe is a production of I
• 47:21
  Speaker 1: Heart Radio. For more podcast from my heart Radio, visit
• 47:24
  Speaker 1: the i heart Radio app, Apple Podcasts, or wherever you
• 47:28
  Speaker 1: listen to your favorite shows.