Latest / Daniel and Kelly’s Extraordinary Universe / Listener Questions #52

Transcript

Listener Questions #52 · Daniel and Kelly’s Extraordinary Universe

• 0:07
  Speaker 1: When AI solves math for us, it feels less fun.
• 0:11
  Speaker 1: Is that true for physics, or is math the only one?
• 0:15
  Speaker 2: Fungi and trees form a wood-wide web through which resources flow.
• 0:21
  Speaker 2: Or so I've heard in pop sci books. But is
• 0:24
  Speaker 2: this really so?
• 0:25
  Speaker 1: I got nothing that can match wood-wide web, Kelly. It's amazing.
• 0:29
  Speaker 2: I know I didn't come up with that, Daniel. Yeah, no,
• 0:32
  Speaker 2: that wasn't me. That wasn't me.
• 0:35
  Speaker 1: When particles and their opposites meet and annihilate, can they
• 0:40
  Speaker 1: become a Higgs or a Z boson, or is a
• 0:42
  Speaker 1: photon their only fate?
• 0:45
  Speaker 2: Whatever questions keep you up at night, Daniel and Kelly's
• 0:48
  Speaker 2: answers will make it right.
• 0:49
  Speaker 1: Welcome to Daniel and Kelly's Extraordinary Universe of Listener Questions,
• 0:54
  Speaker 1: number 52.
• 0:56
  Speaker 2: 52!
• 0:58
  Speaker 3: 52! 52 is not prime and.
• 1:12
  Speaker 1: It doesn't sound like it.
• 1:14
  Speaker 2: Is not prime and it doesn't, that's right. Do we
• 1:17
  Speaker 2: point that out for all?
• 1:18
  Speaker 1: No, but 51 sounds prime, but it's not.
• 1:22
  Speaker 2: Oh, but I feel like that's a point you should
• 1:23
  Speaker 2: have made in episode 51.
• 1:29
  Speaker 1: This is only for the deep math nerds, okay? They'll
• 1:31
  Speaker 1: get it.
• 1:32
  Speaker 2: Okay, all right, good, good. Hello, I'm Kelly Wienersmith. I
• 1:35
  Speaker 2: study parasites and space, but not primes.
• 1:41
  Speaker 1: Hi, I'm Daniel. I'm a particle physicist who likes to
• 1:43
  Speaker 1: think about aliens. And I like primes, but not prime rib.
• 1:48
  Speaker 2: Oh, yeah. I don't know what I have. I'm fine
• 1:50
  Speaker 2: with prime rib. I have to admit.
• 1:53
  Speaker 1: You were figuring it out on the fly there. You're like,
• 1:55
  Speaker 1: how do I feel about prime rib, actually? Well, OK.
• 1:57
  Speaker 2: So here's my somewhat embarrassing opinion about eating meat, which
• 2:02
  Speaker 2: is like, you know, I love like chicken breast. I
• 2:05
  Speaker 2: love kind of I love cheeseburgers. But anytime there's something
• 2:09
  Speaker 2: that reminds me of the fact that it came from
• 2:10
  Speaker 2: an animal, like a rib in the middle, I have
• 2:14
  Speaker 2: a little trouble with it, which I think speaks poorly
• 2:17
  Speaker 2: of me because it suggests that I am trying to
• 2:19
  Speaker 2: disconnect from where my food is actually coming from, which
• 2:23
  Speaker 2: is why I try to not eat a lot of meat.
• 2:25
  Speaker 2: And if I do eat meat, I try to eat
• 2:26
  Speaker 2: it from one of the local farms where the animals
• 2:28
  Speaker 2: had one bad day as opposed to a lifetime of
• 2:32
  Speaker 2: bad days.
• 2:33
  Speaker 1: Yeah. Yes, I think you are morally troubled by eating
• 2:37
  Speaker 1: meat and trying to avoid facing it.
• 2:39
  Speaker 2: I absolutely am.
• 2:41
  Speaker 1: Yeah, yeah. No, exactly. But I think that everybody who
• 2:45
  Speaker 1: eats meat should acknowledge the source of it and should
• 2:47
  Speaker 1: be cool with that. Or you shouldn't eat the meat.
• 2:51
  Speaker 1: But anyway, that's not the topic we intend to talk
• 2:54
  Speaker 1: about today. No. Vegetarianism and morality. I used to be very,
• 2:58
  Speaker 1: very strictly vegetarian and militantly so. I used to walk
• 3:02
  Speaker 1: around with a t-shirt that said, I think, therefore, I
• 3:05
  Speaker 1: am vegetarian.
• 3:06
  Speaker 2: Whoa, you wore one of those in your face. I'm
• 3:09
  Speaker 2: going to be kind of annoying to be around vegetarians.
• 3:13
  Speaker 1: I definitely was.
• 3:14
  Speaker 2: I was a vegetarian for a long time, too. And
• 3:16
  Speaker 2: then I was feeling sleepy a lot. And I realized
• 3:19
  Speaker 2: that after I had a little meat, I stopped feeling sleepy,
• 3:21
  Speaker 2: which probably meant I should have found like the right supplements.
• 3:24
  Speaker 2: But I was definitely missing something. But I'm married to
• 3:26
  Speaker 2: a man who's been a vegetarian for over a quarter
• 3:29
  Speaker 2: of a century. And so we don't eat a lot
• 3:31
  Speaker 2: of meat in our family anyway. So, all right. We
• 3:35
  Speaker 2: have gotten off topic.
• 3:36
  Speaker 1: Well, today we're going to throw you some red meat
• 3:39
  Speaker 1: and talk about topics that are in the news, the
• 3:41
  Speaker 1: hot topics in academia, in the public, all about AI
• 3:45
  Speaker 1: and science. Is AI unraveling the scientific enterprise and ruining
• 3:50
  Speaker 1: our lives?
• 3:51
  Speaker 2: Oh, my gosh. Zach talks to me about what's happening
• 3:53
  Speaker 2: in the world of math and AI all the time.
• 3:57
  Speaker 2: So let's go ahead and segue directly into our first question.
• 4:00
  Speaker 2: And this is a question from Isaac, who is a
• 4:02
  Speaker 2: mathematician in Irvine, California.
• 4:04
  Speaker 4: Hi, Daniel and Kelli. My name is Isaac, and I'm
• 4:07
  Speaker 4: a mathematician from Irvine, California. I have a question for Daniel.
• 4:12
  Speaker 4: You always talk about wanting to meet aliens as they
• 4:15
  Speaker 4: might reveal the secrets of the universe to you. It
• 4:18
  Speaker 4: doesn't sound that such a reveal might take away your
• 4:20
  Speaker 4: pleasure as a human physicist from figuring out the mystery yourself.
• 4:25
  Speaker 4: But in mathematics at the moment, this is precisely what
• 4:27
  Speaker 4: is happening, except the alien is the AI. Many old,
• 4:31
  Speaker 4: famous conjectures and open problems are being solved by human
• 4:34
  Speaker 4: mathematicians prompting the AI in precisely the right way. These
• 4:38
  Speaker 4: mathematicians are being honest about this and posting papers online
• 4:41
  Speaker 4: saying it was the AI who had the clever idea,
• 4:44
  Speaker 4: and the human is just writing it down in a
• 4:46
  Speaker 4: more digestible form. The rate at which this is happening
• 4:49
  Speaker 4: is overwhelming and accelerating rapidly. Many mathematicians are lamenting this
• 4:53
  Speaker 4: state of affairs, as it is taking the joy out
• 4:56
  Speaker 4: of what we do both out of intellectual curiosity and,
• 4:58
  Speaker 4: more practically, for a living. It is also causing many
• 5:01
  Speaker 4: young mathematicians, especially grad students, to rethink a career in academia.
• 5:06
  Speaker 4: It is causing quite the existential crisis at the moment.
• 5:09
  Speaker 4: So my question is, if an AI were to reveal
• 5:12
  Speaker 4: the secrets of physics to you, as it is doing
• 5:14
  Speaker 4: in mathematics currently, would it take the joy out of
• 5:17
  Speaker 4: knowing the answers? Would the feeling be different if it
• 5:20
  Speaker 4: was an AI doing the revealing versus an alien? If so, why?
• 5:25
  Speaker 4: Thanks again for everything you both do.
• 5:27
  Speaker 2: Now, Daniel, do you know... Isaac, is he like in
• 5:30
  Speaker 2: the math department?
• 5:32
  Speaker 1: He is in the math department here at Irvine. He's
• 5:34
  Speaker 1: one of my neighbors and he's a listener to the podcast.
• 5:37
  Speaker 1: He started listening, I think, during the pandemic when he
• 5:41
  Speaker 1: got interested in physics and philosophy and started listening to
• 5:45
  Speaker 1: the podcast and is now writing papers about physics and
• 5:48
  Speaker 1: philosophy and using crazy number systems. I think it's called
• 5:52
  Speaker 1: the hyper reals to write physics theories in new ways.
• 5:55
  Speaker 1: So the guy's all in and he thinks deeply about
• 5:57
  Speaker 1: this stuff.
• 5:58
  Speaker 2: That's amazing.
• 5:59
  Speaker 1: Man. Yeah, it's really cool.
• 6:00
  Speaker 2: Okay. Isaac sounds awesome. But so let's go ahead. Can
• 6:03
  Speaker 2: we start with like what's happening in math before we
• 6:05
  Speaker 2: get to physics?
• 6:06
  Speaker 5: Yeah.
• 6:06
  Speaker 1: So what's happening in math is that AI, specifically chatbots, Claude, ChatGPT,
• 6:13
  Speaker 1: et cetera, are becoming really, really good at proving theorems.
• 6:17
  Speaker 1: You know, math is very logical. It's like a bunch
• 6:20
  Speaker 1: of Lego pieces you click together and the rules are
• 6:22
  Speaker 1: very crisp. It's very easy to check if the answer
• 6:25
  Speaker 1: you have is correct. You know, you could just like
• 6:27
  Speaker 1: plug in the numbers, does the equation work? And this
• 6:30
  Speaker 1: is exactly the kind of thing that AI is good at.
• 6:33
  Speaker 1: It's good at learning the rules and it's good at
• 6:35
  Speaker 1: playing the game. And something that's astonishing is how quickly
• 6:39
  Speaker 1: this has been changing. Like a few years ago, you
• 6:41
  Speaker 1: could put a math problem into ChatGPT and it would
• 6:44
  Speaker 1: hilariously get it wrong, you know, but it's been rapidly
• 6:47
  Speaker 1: getting better and everybody's sort of astonished at the pace
• 6:51
  Speaker 1: at which it's become very, very useful. And what we've
• 6:54
  Speaker 1: seen in the last few weeks is is AI solving
• 6:58
  Speaker 1: massive open problems. So it used to be like, you know,
• 7:00
  Speaker 1: solving little problems here and there, confirming things. But there
• 7:04
  Speaker 1: are these sort of open problems in mathematics, some of
• 7:07
  Speaker 1: them with bounties on them for like a million dollars
• 7:09
  Speaker 1: for anybody who solves the problem. They're called the millennium problems.
• 7:12
  Speaker 1: And one of them is called the Navier-Stokes equation, which
• 7:15
  Speaker 1: is a description of how fluids act. And there's sort
• 7:17
  Speaker 1: of mathematical questions about how it behaves and does it
• 7:20
  Speaker 1: behave well? And can you prove this or that about it?
• 7:22
  Speaker 1: And it's been an open problem for decades. And lots
• 7:25
  Speaker 1: of people spent like you know, half of their career
• 7:28
  Speaker 1: on it and failed. And OpenAI heard that some mathematicians
• 7:33
  Speaker 1: were making some progress on it, and they put tens
• 7:36
  Speaker 1: of thousands of AI agents on it for just a
• 7:38
  Speaker 1: few days, and they cracked a part of it, which
• 7:41
  Speaker 1: is sort of astonishing. And so what we're seeing in
• 7:44
  Speaker 1: math is that AI has astounding capabilities to do the
• 7:48
  Speaker 1: things that only math professors used to be able to do,
• 7:51
  Speaker 1: and now is superhuman in at least some of those categories.
• 7:55
  Speaker 2: This probably goes against all of the best practices that
• 7:57
  Speaker 2: you and I talk about on our show, but I'm
• 7:58
  Speaker 2: going to say it anyway. I saw a headline that
• 8:00
  Speaker 2: said something to the effect of, you know, the Navier-Stokes
• 8:03
  Speaker 2: equation has been solved, but we have learned nothing. And
• 8:06
  Speaker 2: I thought the, I'm guessing the point of the article,
• 8:10
  Speaker 2: and again, this is, on the show, we're never supposed
• 8:13
  Speaker 2: to say I read the headline and that was it.
• 8:14
  Speaker 2: But I was assuming that the point of the article
• 8:17
  Speaker 2: was that when you solve one of these problems, one
• 8:19
  Speaker 2: of the points of solving the problems is that you
• 8:20
  Speaker 2: come up with new procedures and and those new procedures
• 8:23
  Speaker 2: can then be applied to other problems. Is that one
• 8:26
  Speaker 2: of the problems with the fact that this was solved?
• 8:28
  Speaker 2: And what does this do to the math community? Like,
• 8:31
  Speaker 2: if the point isn't actually solving the problem, it's to
• 8:33
  Speaker 2: get a new tool. Have you actually done anything if
• 8:37
  Speaker 2: you haven't come up with new tools?
• 8:39
  Speaker 1: Yeah, I wouldn't say that the new tools are directly
• 8:41
  Speaker 1: the target. I think it's understanding. That's the goal. Like,
• 8:44
  Speaker 1: what is the job of math? What are mathematicians doing?
• 8:47
  Speaker 1: They're not fear-improving machines. They want to understand the universe.
• 8:52
  Speaker 1: and the nature of mathematics. And what does that mean? Well,
• 8:56
  Speaker 1: there are patterns in mathematics. And linking those patterns together
• 9:00
  Speaker 1: and drawing high-level conclusions and insight about the nature of
• 9:04
  Speaker 1: numbers and et cetera, that's the job of mathematics. That's
• 9:08
  Speaker 1: what they're doing. They're not just like, is this theorem
• 9:11
  Speaker 1: true or not? And so I think an issue with
• 9:15
  Speaker 1: these millennium problems is that traditionally the question that the
• 9:20
  Speaker 1: millennium problem is asking is not the interesting thing, as
• 9:22
  Speaker 1: you were saying, which interesting is the journey, the things
• 9:25
  Speaker 1: you learn about math on the way to it. And
• 9:28
  Speaker 1: so these were valuable problems because people would try to
• 9:30
  Speaker 1: solve them and fail, and then they would try again,
• 9:33
  Speaker 1: and they would have to invent new tools, as you said.
• 9:35
  Speaker 1: But those new tools often led to a new understanding
• 9:38
  Speaker 1: of mathematics, connections. And what do I mean by connections? Well,
• 9:41
  Speaker 1: like an easy to understand one is understanding the relationship
• 9:46
  Speaker 1: between geometry and algebra. Think about how you can write
• 9:49
  Speaker 1: down two equations on a piece of paper and you
• 9:52
  Speaker 1: can solve it for a solution.
• 9:53
  Speaker 3: Right?
• 9:54
  Speaker 1: Two linear equations, two equations, two unknowns, you can solve it.
• 9:57
  Speaker 1: There's like a single solution sometimes. Well, that turns out
• 10:00
  Speaker 1: to be exactly the same as drawing two lines on
• 10:02
  Speaker 1: a sheet of paper and seeing where they intersect because
• 10:05
  Speaker 1: each equation is a line and the solution is their intersection.
• 10:08
  Speaker 1: And maybe that's obvious to you, but It took people
• 10:10
  Speaker 1: hundreds of years to realize that geometry and algebra are
• 10:13
  Speaker 1: the same thing. They're deeply connected in this way. They're
• 10:16
  Speaker 1: different ways to express the same ideas. That's the kind
• 10:19
  Speaker 1: of understanding we're talking about, but of course, much, much
• 10:21
  Speaker 1: more sophisticated, right? And that's what mathematics is about. It's
• 10:25
  Speaker 1: not about, is this theorem true, yes or no? They
• 10:28
  Speaker 1: were using these theorems as motivators, as targets for as
• 10:32
  Speaker 1: ways to inspire people to develop new ideas. But the
• 10:36
  Speaker 1: targets themselves are not the metrics. And I think the
• 10:39
  Speaker 1: math community's response is essentially like, okay, you took down
• 10:42
  Speaker 1: that target, but you didn't get any understanding. We didn't
• 10:45
  Speaker 1: learn anything. Like the proof to this thing is like,
• 10:48
  Speaker 1: you know, 150 pages of unintelligible stuff with no insight
• 10:52
  Speaker 1: at all. And so it's sort of like, you know,
• 10:55
  Speaker 1: the way Kasparov was beat by Deep Blue and he
• 10:58
  Speaker 1: was beaten in a brute force manner. Like, Very powerful computer,
• 11:01
  Speaker 1: tries lots of different options, can do so really quickly.
• 11:04
  Speaker 1: And so it can beat Kasparov.
• 11:06
  Speaker 2: And Kasparov was a chess champion. And this is when
• 11:08
  Speaker 2: Deep Blue, the AI program, beat him.
• 11:10
  Speaker 1: Thank you.
• 11:11
  Speaker 3: Yeah.
• 11:11
  Speaker 1: But it doesn't yield any insight into chess. Like you
• 11:14
  Speaker 1: can't watch the machine play and be like, oh, that's interesting.
• 11:17
  Speaker 1: Here's a whole new strategy we hadn't considered. What you
• 11:20
  Speaker 1: learn from that is like, oh, if you have a
• 11:22
  Speaker 1: really fast computer, you can try all the solutions and
• 11:25
  Speaker 1: you can come up with one that works. And so
• 11:28
  Speaker 1: in the same way, I think the math community is
• 11:30
  Speaker 1: a little frustrated that these problems have been taken down
• 11:33
  Speaker 1: and people treat it as if like, oh, math is
• 11:35
  Speaker 1: solved now, but it's not, right? They pick these destinations
• 11:39
  Speaker 1: to make the journeys interesting. The destinations are less interesting
• 11:43
  Speaker 1: without the journey. I talked to another math prophet, Irvine.
• 11:46
  Speaker 1: I asked him what he thought. And he said that like,
• 11:49
  Speaker 1: it's cool that AI can do this and it makes
• 11:51
  Speaker 1: things faster, but that the limitation in math was never
• 11:54
  Speaker 1: like what theorems are true. We already have more knowledge
• 11:58
  Speaker 1: of mathematical truths than we understand. What we lack is understanding.
• 12:01
  Speaker 1: So like getting more effectively data about like which theorems
• 12:05
  Speaker 1: are true or false doesn't mean more understanding. If anything,
• 12:08
  Speaker 1: this inspires a need for more mathematicians to understand the
• 12:11
  Speaker 1: mathematical truths that will be pumped out more quickly by AI.
• 12:15
  Speaker 2: So is the task now then to like... go through
• 12:19
  Speaker 2: the 150 pages or whatever that the AI program came
• 12:23
  Speaker 2: up with and see how it did it. And that's
• 12:25
  Speaker 2: where the insights are going to come from.
• 12:27
  Speaker 1: Yes.
• 12:27
  Speaker 2: And just because a problem comes down doesn't mean you
• 12:29
  Speaker 2: can't try to find another way to solve it, right?
• 12:31
  Speaker 1: Yeah, exactly. Okay. And maybe there are insights in there,
• 12:34
  Speaker 1: but maybe there aren't, right? Maybe it's like, oh, you
• 12:37
  Speaker 1: can brute force this problem in this really boring way
• 12:40
  Speaker 1: that doesn't apply to anything else. Like, okay, cool. Thank you.
• 12:45
  Speaker 1: But it doesn't teach you anything. Or maybe there are
• 12:47
  Speaker 1: great insights. But it's still going to take some human
• 12:50
  Speaker 1: to go through it and think about it and try
• 12:51
  Speaker 1: to extract some understanding of it.
• 12:53
  Speaker 3: Okay.
• 12:54
  Speaker 1: So that's what's happening in the math community. I think
• 12:56
  Speaker 1: that's what Isaac is reflecting. And there's also this question
• 13:00
  Speaker 1: of who's getting credit for it. If you just dump
• 13:02
  Speaker 1: a problem in a chat GPT and you write a paper,
• 13:04
  Speaker 1: is that really yours? And those are sort of sociological
• 13:07
  Speaker 1: questions about how academia works. Mm-hmm. But what we haven't
• 13:10
  Speaker 1: seen so far is a similar thing in physics, right?
• 13:14
  Speaker 1: Physics has not been solved by AI yet.
• 13:17
  Speaker 2: You're next, though, Daniel. You're next. You can't take the
• 13:20
  Speaker 2: ecologists out of the loop, man. We've got to still
• 13:22
  Speaker 2: go outside and catch the squirrels, but you guys are next.
• 13:25
  Speaker 1: Yeah, well, if AI can build a large Hadron Collider,
• 13:28
  Speaker 1: I think it can catch a squirrel or two. But,
• 13:31
  Speaker 1: you know, in my field, I'm in experimental physics, and...
• 13:34
  Speaker 1: We also have targets, right? Targets like, what is dark matter?
• 13:38
  Speaker 1: How big is the universe? What is everything made out of?
• 13:41
  Speaker 1: And yes, you can get insights along the way. You know,
• 13:45
  Speaker 1: you can develop a better process, new tools, you know,
• 13:48
  Speaker 1: bigger accelerators, whatever, to solve them. But really, the targets,
• 13:51
  Speaker 1: I think, are better aligned here. Like, we actually want
• 13:55
  Speaker 1: to know those answers for themselves, right? We build experiments
• 13:59
  Speaker 1: to learn those answers. Some people really like building the experiment.
• 14:03
  Speaker 1: Some people really like developing the methods and that's cool.
• 14:06
  Speaker 1: And that's all changing because that's all being rapidly transformed
• 14:10
  Speaker 1: by AI. But the questions themselves are inherently interesting, maybe
• 14:14
  Speaker 1: in a way that's different from in math where they're
• 14:17
  Speaker 1: not that excited about this question. They just thought it
• 14:19
  Speaker 1: was a hard one and so useful target. Whereas like
• 14:22
  Speaker 1: I really do want to know what dark matter is.
• 14:24
  Speaker 1: And if AI could figure it out, or equivalently, if
• 14:27
  Speaker 1: the aliens could come and tell me, I would want
• 14:29
  Speaker 1: to know. If AI had a box with all the
• 14:32
  Speaker 1: answers to all of my questions on my desk, it
• 14:35
  Speaker 1: would not sit there closed. I would not think, let's not,
• 14:39
  Speaker 1: because it'd be more meaningful to figure it out ourselves.
• 14:42
  Speaker 3: No, no, no.
• 14:43
  Speaker 1: That box is getting insta-opened, right? Even if it's some
• 14:47
  Speaker 1: horrible Twilight Zone episode where every time I open the box,
• 14:49
  Speaker 1: 10 people die, I'm opening that box. I want to
• 14:53
  Speaker 1: know these things.
• 14:54
  Speaker 2: I'm not surprised. You would vaporize all of humanity to
• 14:57
  Speaker 2: understand physics better. We've established this, but okay.
• 14:59
  Speaker 1: Exactly. I would turn them into cheeseburgers for Kelly to
• 15:02
  Speaker 1: eat to up her iron intake or whatever. But, you know,
• 15:06
  Speaker 1: I'm never going to be faced with that question, so
• 15:07
  Speaker 1: I can speculate about this totally irresponsibly. But the point
• 15:11
  Speaker 1: is that we're deeply interested in these truths themselves. And
• 15:15
  Speaker 1: the process along the way is fascinating, cool, and it's
• 15:18
  Speaker 1: a fun journey sometimes. But I think we're more interested
• 15:21
  Speaker 1: in the actual targets than the mathematicians are. And maybe
• 15:25
  Speaker 1: other people disagree, but I think in physics, we'd want
• 15:28
  Speaker 1: to know the answers for sure.
• 15:30
  Speaker 2: Yeah. Isaac noted that AI might be impacting how many
• 15:33
  Speaker 2: people are considering careers in mathematics. Do you think AI
• 15:38
  Speaker 2: could eventually impact how many people want careers in physics?
• 15:42
  Speaker 2: Because maybe you only need five people to open the box.
• 15:45
  Speaker 1: No, the trend is the opposite. Think about how many
• 15:50
  Speaker 1: mathematicians or physicists there were before we had computers. There
• 15:55
  Speaker 1: were fewer. And we had something called human computers. Like
• 15:58
  Speaker 1: Einstein had a guy who would help do his calculations.
• 16:01
  Speaker 1: And in physics, we had people we called human trackers
• 16:04
  Speaker 1: who would like look at outputs from detectors and find
• 16:07
  Speaker 1: particle traces. And we replaced all of those with computers
• 16:10
  Speaker 1: that were much more powerful than humans. much faster. What happened?
• 16:14
  Speaker 1: Did we fire a bunch of physicists and mathematicians?
• 16:17
  Speaker 3: No.
• 16:17
  Speaker 1: We created new opportunities, and so there's so much more
• 16:21
  Speaker 1: physics and math that we could do, and the fields
• 16:23
  Speaker 1: have exploded. The fields are much larger because the tools
• 16:26
  Speaker 1: gave us more capacity to ask and to answer questions.
• 16:30
  Speaker 1: And that's what's happening now, is that we are changing
• 16:33
  Speaker 1: the process, the day-to-day work of what it means to
• 16:36
  Speaker 1: do physics and math, because the tools are becoming much
• 16:38
  Speaker 1: more powerful. but that just means we can ask more questions.
• 16:42
  Speaker 1: We can answer more questions. And so I think the
• 16:45
  Speaker 1: possibilities are much larger now, especially in physics. You know,
• 16:49
  Speaker 1: I saw somebody post on Twitter that they were leaving
• 16:52
  Speaker 1: physics thanks to AI because they came to climb a
• 16:55
  Speaker 1: bunch of mountains and the AI has built escalators. And
• 17:00
  Speaker 1: I was like, I thought that's interesting. Maybe this guy
• 17:03
  Speaker 1: came into the field because he liked climbing the mountains.
• 17:06
  Speaker 1: He wasn't actually interested in the view where it's like,
• 17:09
  Speaker 1: I'm a physicist. I want to know the answers. So
• 17:12
  Speaker 1: I want to get to the top of the mountain.
• 17:13
  Speaker 1: I'm happy to climb it. It's fun to climb it.
• 17:15
  Speaker 1: I enjoy it. But I also will take the escalator
• 17:18
  Speaker 1: because I want to know what's at the top. And
• 17:21
  Speaker 1: the other thing to note about that analogy is that
• 17:24
  Speaker 1: I think the mountain is infinitely tall. So it's not like, oh,
• 17:27
  Speaker 1: you spoiled it because now we got to the top.
• 17:29
  Speaker 1: There's nothing else to do. When we get to the
• 17:31
  Speaker 1: current top, we're going to discover, oh, that's not the peak.
• 17:33
  Speaker 1: There's more stuff to figure out. There's an infinite number
• 17:36
  Speaker 1: of questions to ask. So there's no shortage. So I'm
• 17:39
  Speaker 1: excited about the future where AI has empowered us to
• 17:43
  Speaker 1: more rapidly do work. But it is changing what it
• 17:46
  Speaker 1: means to be a physicist. And I think we're seeing
• 17:49
  Speaker 1: a separation between folks who enjoy the process and are
• 17:53
  Speaker 1: good at grinding. Like, let me, you know, climb really hard.
• 17:58
  Speaker 1: And that's gone now because grinding is done much better
• 18:01
  Speaker 1: by AI and the people who are interested in the
• 18:03
  Speaker 1: questions and wanting to sort of like just use the
• 18:06
  Speaker 1: tools to ask those questions about the universe. they are
• 18:09
  Speaker 1: becoming more empowered. And so I think that's the transformation,
• 18:12
  Speaker 1: but I don't think it's the first time academia has
• 18:14
  Speaker 1: been transformed by the creation of new computational tools. I
• 18:18
  Speaker 1: don't think it'll be the last time. And so I
• 18:21
  Speaker 1: think for those of us who are excited about the answers,
• 18:23
  Speaker 1: it's thrilling to have tools that bring us closer to them.
• 18:27
  Speaker 2: All right, well, let's see what Isaac has to say
• 18:29
  Speaker 2: about your answer.
• 18:30
  Speaker 4: Thank you, Daniel and Kelly, for answering my question. The
• 18:34
  Speaker 4: thing I appreciated most about your answer, Daniel, was your
• 18:36
  Speaker 4: tone of positivity. not just from the pleasure of finding
• 18:40
  Speaker 4: out the answers, but also that these AI advances will
• 18:43
  Speaker 4: actually encourage the need for more mathematicians and physicists rather
• 18:46
  Speaker 4: than fewer. In general, I would characterize the mood in
• 18:49
  Speaker 4: the mathematical community as quite pessimistic or conditionally optimistic, so
• 18:54
  Speaker 4: hearing your contrasting opinion was very uplifting for me. I
• 18:58
  Speaker 4: also appreciated your distinction between AI's impact thus far on
• 19:02
  Speaker 4: math versus physics, and also the distinction between potential impacts
• 19:06
  Speaker 4: on experimental versus theoretical physics. Your discussion of OpenAI's recent
• 19:10
  Speaker 4: solution to the Navier-Stokes equation and the ensuing lack of
• 19:14
  Speaker 4: understanding was very insightful and accurate. My hope is that
• 19:18
  Speaker 4: the AI companies will soon tire of using math as
• 19:21
  Speaker 4: a marketing tool for their product, as I do have
• 19:23
  Speaker 4: faith that, once this technology is primarily in the hands
• 19:26
  Speaker 4: of professional mathematicians, it will indeed prove to be a
• 19:30
  Speaker 4: useful tool for furthering our understanding and developing our theories.
• 19:34
  Speaker 4: Once again, I appreciate everything you both do for communicating
• 19:37
  Speaker 4: science and encouraging curiosity.
• 19:40
  Speaker 3: Thank you.
• 19:59
  Speaker 2: All right. On to Wood Wide Web. And I love
• 20:01
  Speaker 2: that you thought that I came up with that. But
• 20:04
  Speaker 2: where does it come from?
• 20:05
  Speaker 1: Let's give somebody some credit.
• 20:06
  Speaker 2: It's been around since 1997 when a nature paper commented
• 20:10
  Speaker 2: on another nature paper and they. Yeah, we can get
• 20:13
  Speaker 2: into that.
• 20:14
  Speaker 1: Everybody who works on mushrooms seems like a really fun guy.
• 20:17
  Speaker 2: Oh, stop it. Stop it. Let's introduce David. Do you
• 20:23
  Speaker 2: want to bring us back? Or do you want me to?
• 20:25
  Speaker 1: I thought we were already back.
• 20:26
  Speaker 2: Oh, okay. We can be back. Okay. All right. Now
• 20:30
  Speaker 2: that we've... Wait, where am I?
• 20:31
  Speaker 1: But let's hear the question from David.
• 20:33
  Speaker 2: Thank you.
• 20:34
  Speaker 1: Yes.
• 20:34
  Speaker 5: Hi, Daniel and Kelly. This is David Naylor. Hey, thank
• 20:37
  Speaker 5: you so much for taking my question. I love your show.
• 20:40
  Speaker 5: Just wanted to ask you about something I recently read
• 20:43
  Speaker 5: about in the news that I thought was really cool.
• 20:46
  Speaker 5: I saw this... article that discussed mycelium networks and how
• 20:51
  Speaker 5: both nutrients and rudimentary bits of information sharing could take
• 20:56
  Speaker 5: place between both trees and fungi. And I wanted to
• 21:00
  Speaker 5: get your thoughts on the subject. Thank you so much.
• 21:03
  Speaker 2: Okay, so David had a question about how information is
• 21:07
  Speaker 2: shared between trees and fungi, but I want to take
• 21:10
  Speaker 2: a step back and reconsider the initial part of his sentence,
• 21:14
  Speaker 2: which was about nutrients being shared between trees and fungi.
• 21:18
  Speaker 2: Because in 2023, or around 2023, there were three review
• 21:22
  Speaker 2: papers that came out All of them pushing back on
• 21:25
  Speaker 2: this popular science narrative about how trees and fungi pass
• 21:31
  Speaker 2: resources back and forth.
• 21:32
  Speaker 1: Oh, no. Did the Popsite articles not get the nuance right, Kelly?
• 21:36
  Speaker 2: No, they didn't. No, they didn't. But it actually looks like.
• 21:39
  Speaker 2: some of us, some scientists might be playing a role
• 21:42
  Speaker 2: in how this has been sort of perpetuated incorrectly. Fair.
• 21:46
  Speaker 1: Yes.
• 21:47
  Speaker 2: So in 1997, as we alluded to a moment ago,
• 21:50
  Speaker 2: there was this paper that came out that found that
• 21:53
  Speaker 2: carbon can pass from one tree of one species to
• 21:57
  Speaker 2: a different tree of another species. And it appeared to
• 22:00
  Speaker 2: be getting from one tree to another through the fungus
• 22:03
  Speaker 2: that connected it.
• 22:04
  Speaker 1: So, but back up, like trees breathe the air and
• 22:08
  Speaker 1: photosynthesis and they take CO2 out of the air and
• 22:12
  Speaker 1: they keep the carbon and they use the carbon to
• 22:14
  Speaker 1: basically build themselves. So trees are machines to pull carbon
• 22:17
  Speaker 1: out of the air and assemble their cellular structures. And
• 22:20
  Speaker 1: so now you're saying that the carbon in one tree
• 22:22
  Speaker 1: can end up in another tree.
• 22:24
  Speaker 2: Yes, thank you. And so the important thing is that
• 22:27
  Speaker 2: fungus get their carbon from trees, some sorts of fungal symbionts,
• 22:32
  Speaker 2: symbionts just being like organisms that sort of interact with
• 22:34
  Speaker 2: one another. They get the carbon from the trees and
• 22:37
  Speaker 2: then the fungus provide things like sugars to the trees.
• 22:42
  Speaker 1: How cozy.
• 22:43
  Speaker 2: How cozy, right? And so one of the trees was
• 22:46
  Speaker 2: in shade, which meant it couldn't get as much light,
• 22:48
  Speaker 2: so it couldn't make as much of its own carbon.
• 22:51
  Speaker 2: And it appeared to be getting carbon from a tree
• 22:54
  Speaker 2: that was not shaded. It was getting more light. It
• 22:56
  Speaker 2: was able to make more carbon. And it looked like
• 22:57
  Speaker 2: the fungus was connecting them and was shuttling the carbon
• 23:01
  Speaker 2: over to the tree that needed it.
• 23:02
  Speaker 3: Oh, wow.
• 23:02
  Speaker 1: Cool. Like a tree internet. Yeah.
• 23:05
  Speaker 2: Yes.
• 23:05
  Speaker 5: Right.
• 23:06
  Speaker 2: And so this paper came out in Nature and another
• 23:08
  Speaker 2: scientist who studied something similar called this connection the wood
• 23:12
  Speaker 2: wide web.
• 23:13
  Speaker 1: Nice. Love it.
• 23:14
  Speaker 2: Yes. So this was in the 90s when the Internet
• 23:16
  Speaker 2: was becoming like, you know, more a part of our lives. But,
• 23:19
  Speaker 2: you know, it still stands today.
• 23:20
  Speaker 1: And were people speculating that trees have like tree mail
• 23:23
  Speaker 1: also between themselves? Yes.
• 23:26
  Speaker 3: Oh, no.
• 23:28
  Speaker 2: I don't know. I don't know. Yeah.
• 23:31
  Speaker 1: And maybe, you know, they're recording their own sodcasts. Oh,
• 23:35
  Speaker 1: who knows what's going on out there?
• 23:36
  Speaker 2: Daniel, I'm cutting you off. I'm cutting you off, man.
• 23:38
  Speaker 2: All right. So here's the problem. OK, so this this
• 23:42
  Speaker 2: wood wide web idea has escaped into the popular consciousness.
• 23:48
  Speaker 2: And there's three parts to it that are worth examining.
• 23:51
  Speaker 2: So one, the idea is that fungus are connecting multiple
• 23:55
  Speaker 2: trees in the forest.
• 23:57
  Speaker 1: Okay.
• 23:57
  Speaker 2: Those connections are shuttling resources between those trees.
• 24:00
  Speaker 1: Mm-hmm.
• 24:01
  Speaker 2: And then there's another idea called like the mother tree.
• 24:04
  Speaker 3: Okay.
• 24:05
  Speaker 2: And the idea here is that mother trees are able
• 24:07
  Speaker 2: to shuttle resources specifically to the seedlings that are their offspring.
• 24:12
  Speaker 3: Oh, all right.
• 24:13
  Speaker 2: Okay. And so... You see these ideas in popular science
• 24:16
  Speaker 2: a lot. People talk about how it's made the forest
• 24:19
  Speaker 2: more magical and that it's like there's this implied altruism
• 24:23
  Speaker 2: where trees that are making extra carbon are then shuttling
• 24:26
  Speaker 2: it over to trees that need it and fungus are
• 24:28
  Speaker 2: the intermediaries making all of this like happy huggy stuff
• 24:32
  Speaker 2: happening um but these three papers that came out in
• 24:36
  Speaker 2: 2023 are pushing back against it so all right i
• 24:38
  Speaker 2: know i know so all right so the first claim.
• 24:40
  Speaker 1: Science has been ruining everything since 1542 or.
• 24:43
  Speaker 2: Whatever that's right you can get a shirt for that
• 24:45
  Speaker 2: at smbc-comics.com so um so the idea here is Fungus
• 24:51
  Speaker 2: is connecting different trees. Resources are shuttling through that. So
• 24:54
  Speaker 2: some people have gone out to try to see, like, okay,
• 24:57
  Speaker 2: are long strands of fungus connecting trees that are kind
• 25:00
  Speaker 2: of far away? And you can go out into the
• 25:03
  Speaker 2: forest and you can dig up some soil and say, okay,
• 25:06
  Speaker 2: I found a fungus of a particular genotype. And I
• 25:10
  Speaker 2: also found that fungus of a particular genotype at another tree.
• 25:14
  Speaker 2: So they're connected. But the problem is, yes, your face.
• 25:18
  Speaker 2: That was a different than the first few.
• 25:21
  Speaker 1: Yeah, I didn't follow that leap like. just because you
• 25:24
  Speaker 1: find a computer in my office and a computer in
• 25:26
  Speaker 1: your office doesn't mean they're connected.
• 25:28
  Speaker 2: Right, exactly. Yes. And so like when an earthworm moves
• 25:30
  Speaker 2: through the soil, it can break a fungal connection between
• 25:33
  Speaker 2: trees or when like, you know, an insect moves through
• 25:36
  Speaker 2: the soil. So just because you find the same fungal
• 25:39
  Speaker 2: genotype at multiple trees doesn't necessarily mean they're connected.
• 25:43
  Speaker 1: But did the original results have some sort of smoking
• 25:46
  Speaker 1: gun where they like you know, labeled carbon in one
• 25:49
  Speaker 1: tree and saw it appear in another tree or something?
• 25:51
  Speaker 2: Yeah, so they did. They did. But here's the problem
• 25:53
  Speaker 2: with that.
• 25:54
  Speaker 1: Wait, really? That's how they did it? I just totally
• 25:56
  Speaker 1: made that up.
• 25:56
  Speaker 2: You're very smart, Daniel.
• 25:59
  Speaker 1: Or maybe biology is just kind of easy.
• 26:01
  Speaker 4: Oh, my God.
• 26:01
  Speaker 2: You know, between the puns and the biology shade, I'm
• 26:06
  Speaker 2: almost done with you for today, Daniel.
• 26:08
  Speaker 1: Yeah, I threw in some SMBC red meat for you.
• 26:10
  Speaker 2: Well, that's nice for Zach, for my husband, not for me.
• 26:14
  Speaker 5: Wow.
• 26:15
  Speaker 1: Okay, not much solidarity over there.
• 26:17
  Speaker 2: We're connected.
• 26:17
  Speaker 1: All right, so tell us what we learned from the earthworms.
• 26:20
  Speaker 2: Yeah, okay.
• 26:20
  Speaker 5: Well, all right.
• 26:21
  Speaker 2: So as you noted, you can track where carbon is
• 26:23
  Speaker 2: going by radio labeling it and seeing where it ends up.
• 26:26
  Speaker 2: But one of the problems there is that, one, you
• 26:29
  Speaker 2: often can't be 100% sure that the same fungus is
• 26:31
  Speaker 2: connecting multiple individuals. And two, resources diffuse across soil in
• 26:37
  Speaker 2: like a source-sink dynamic sort of way. So you could
• 26:39
  Speaker 2: have carbon moving from an area where a tree is
• 26:44
  Speaker 2: making a lot of it to an area where a
• 26:46
  Speaker 2: tree is making less of it, and it doesn't necessarily
• 26:49
  Speaker 2: need to be passing through the fungus to get from
• 26:51
  Speaker 2: one place to another.
• 26:52
  Speaker 1: Wait, trees take carbon from the air and then it
• 26:54
  Speaker 1: just diffuses out of the tree? I thought it like,
• 26:57
  Speaker 1: it's part of the wood, right? Isn't wood basically cellulose,
• 27:00
  Speaker 1: which is made out of carbon? Why are they losing
• 27:02
  Speaker 1: their carbon?
• 27:03
  Speaker 2: Well, part of the deal with trees and fungi, like
• 27:06
  Speaker 2: their symbiosis, is that the trees extract carbon dioxide from
• 27:10
  Speaker 2: the atmosphere and then they use that carbon to build
• 27:13
  Speaker 2: themselves and they give some of that carbon to their
• 27:16
  Speaker 2: fungal symbiotes. And so I guess the idea is that
• 27:19
  Speaker 2: some of that carbon doesn't stay with the fungus.
• 27:21
  Speaker 1: Oh, I see.
• 27:22
  Speaker 4: Interesting.
• 27:22
  Speaker 2: Yeah. So one of the ways that people try to
• 27:24
  Speaker 2: study this is that they will like for a quote
• 27:27
  Speaker 2: unquote control tree, they'll put a tree in a mesh bag.
• 27:31
  Speaker 1: A control tree. I'm sorry.
• 27:33
  Speaker 2: What?
• 27:35
  Speaker 1: The one tree that controls them all. That's what it
• 27:38
  Speaker 1: sounds like. The control tree. All right.
• 27:40
  Speaker 4: Okay.
• 27:41
  Speaker 2: Well, so say you were trying to do an experiment
• 27:42
  Speaker 2: and you wanted to see if some chemical that you
• 27:45
  Speaker 2: put in tree one made it to tree two through
• 27:49
  Speaker 2: the fungus.
• 27:49
  Speaker 1: Yeah. Okay.
• 27:50
  Speaker 2: You would want to have another tree in the area
• 27:53
  Speaker 2: that didn't have a fungal connection so that you could
• 27:56
  Speaker 2: confirm that it didn't end up taking up that chemical.
• 27:59
  Speaker 2: And that would be a way of saying, oh, the
• 28:00
  Speaker 2: fungus is important in making sure that stuff gets from
• 28:03
  Speaker 2: tree one to tree two. Does that make sense?
• 28:05
  Speaker 1: So you want a positive signal where you see it
• 28:07
  Speaker 1: happening with the fungus and you want an absence of
• 28:10
  Speaker 1: a positive signal without the fungus.
• 28:11
  Speaker 2: Exactly.
• 28:12
  Speaker 1: Got it.
• 28:12
  Speaker 6: Okay.
• 28:13
  Speaker 1: Yes. See, biology is not that hard. Okay. Okay. You
• 28:19
  Speaker 1: got nothing to say to that, huh?
• 28:21
  Speaker 2: I'm just moving on because otherwise we're never going to
• 28:23
  Speaker 2: get to the end of this. All right. So they
• 28:27
  Speaker 2: put these trees in mesh bags. But the problem is
• 28:31
  Speaker 2: these mesh bags have to have a very fine mesh
• 28:33
  Speaker 2: so that you can make sure that the fungus can't
• 28:35
  Speaker 2: get in to connect with the roots of the tree
• 28:37
  Speaker 2: to transfer the nutrients. And if you have a mesh
• 28:40
  Speaker 2: that is that small, that might mess up diffusion through
• 28:44
  Speaker 2: the soil. And so, like, whatever chemical you injected in
• 28:46
  Speaker 2: the tree maybe could have made it to the other tree,
• 28:49
  Speaker 2: even if there was no fungus there, just because of
• 28:52
  Speaker 2: the way stuff moves through the soil on its own.
• 28:55
  Speaker 2: Does that make sense?
• 28:56
  Speaker 1: Turns out biology is complicated, actually.
• 28:57
  Speaker 2: Thank you. We were going to get there. We were
• 29:00
  Speaker 2: always going to get there. So this stuff is really
• 29:03
  Speaker 2: hard to study properly. in the lab, but it's also
• 29:05
  Speaker 2: even harder to study in the field. So most of
• 29:09
  Speaker 2: the evidence that we have is from the lab. And
• 29:11
  Speaker 2: we do know that it is possible for fungus to
• 29:14
  Speaker 2: connect multiple trees and transfer things between them. And part
• 29:18
  Speaker 2: of how we know that is from orchids, which aren't trees,
• 29:20
  Speaker 2: but like there are some kind of orchids that have
• 29:22
  Speaker 2: lost the ability to produce their own They don't have
• 29:25
  Speaker 2: they don't make chlorophyll anymore. They're white or sometimes like
• 29:30
  Speaker 2: multiple other colors that are not green. And they connect
• 29:33
  Speaker 2: to fungus and they steal carbon from other trees through
• 29:38
  Speaker 2: the fungus.
• 29:38
  Speaker 1: Wow. Interesting.
• 29:40
  Speaker 2: So we do know that fungus can be used to
• 29:42
  Speaker 2: sort of transport things like carbon around. But then the
• 29:46
  Speaker 2: question is, is it like, A, preferentially going to the
• 29:49
  Speaker 2: seedlings from a mother tree? And B, for things like
• 29:53
  Speaker 2: big trees in a forest where there's a lot of
• 29:55
  Speaker 2: other stuff going on, is it an important contribution to
• 29:59
  Speaker 2: the like nutrients and stuff that they need to survive?
• 30:03
  Speaker 2: And so for those claims, there's some evidence in the lab.
• 30:07
  Speaker 2: Sometimes the evidence in the lab is even mixed. And
• 30:10
  Speaker 2: in the field, there's very little evidence, in particular, very
• 30:13
  Speaker 2: little evidence for this mother tree idea. And there was
• 30:15
  Speaker 2: a whole book called Finding the Mother Tree. And the
• 30:18
  Speaker 2: review papers that I read were like, no, there's really
• 30:21
  Speaker 2: like no evidence for this.
• 30:23
  Speaker 1: And people should understand the difference between seeing something in
• 30:26
  Speaker 1: the lab and seeing something in the field. Like there's
• 30:29
  Speaker 1: lots of things which work in the lab because it's
• 30:31
  Speaker 1: a really simple environment on purpose. So you can understand
• 30:35
  Speaker 1: the simple version of it. But to be relevant to
• 30:37
  Speaker 1: what's actually happening in the world. right? It has to
• 30:40
  Speaker 1: work in the complicated environment of Earth. And that sometimes, like,
• 30:45
  Speaker 1: the answers can be very different, right, in the field
• 30:47
  Speaker 1: and in the lab.
• 30:48
  Speaker 2: Yeah, massively different.
• 30:49
  Speaker 1: Because biology is really hard, actually.
• 30:51
  Speaker 2: Thank you. And another thing to note is that often
• 30:56
  Speaker 2: when you read about this in, like, popular science articles
• 31:00
  Speaker 2: or books, it has a, like, nice altruistic kind of
• 31:04
  Speaker 2: feel to it. But it's worth noting that things like
• 31:07
  Speaker 2: Chemicals that trees produce to kill other trees that they're
• 31:10
  Speaker 2: competing with can also pass through these like fungal networks.
• 31:14
  Speaker 3: Oh, wow.
• 31:15
  Speaker 2: And it's not necessarily the case that, you know, when
• 31:17
  Speaker 2: messages are being sent that help another tree, it's not
• 31:21
  Speaker 2: necessarily the case that one tree was trying to help
• 31:24
  Speaker 2: another tree. So, for example, sometimes if a tree gets
• 31:27
  Speaker 2: attacked by an insect, it will release chemicals that attract
• 31:32
  Speaker 2: parasitoids that will then go kill those insects. Right. And
• 31:35
  Speaker 2: there's some evidence that the chemicals that it's releasing are
• 31:38
  Speaker 2: also initiating responses in nearby trees. And so it might
• 31:44
  Speaker 2: be tempting to think, oh, this one tree is trying
• 31:46
  Speaker 2: to tell the trees in the area, hey, I'm getting attacked.
• 31:49
  Speaker 2: You might be next. You should also start responding. But
• 31:52
  Speaker 2: it could just be that the other trees are eavesdropping.
• 31:55
  Speaker 2: They're like over evolutionary time. Other trees have been able
• 31:58
  Speaker 2: to cue in on a tree essentially screaming because it's
• 32:00
  Speaker 2: being attacked and being like, oh, no. I'm next. I
• 32:04
  Speaker 2: should start producing this compound so I can get a
• 32:06
  Speaker 2: head start on things.
• 32:08
  Speaker 1: You know, if trees screamed when we chopped them down,
• 32:11
  Speaker 1: do you think we would chop them down so much?
• 32:13
  Speaker 2: We'd have probably a very different relationship with trees, I think.
• 32:16
  Speaker 1: Unless they screamed all the time for no particular reason.
• 32:19
  Speaker 2: Yeah, unless they were crying wolf all the time.
• 32:24
  Speaker 1: This makes me think about the morality we're constantly overlaying
• 32:27
  Speaker 1: on these questions. It's like all this altruism and like
• 32:30
  Speaker 1: helpfulness and whatever. It's fascinating how we're trying to like
• 32:33
  Speaker 1: anthropomorphize these trees, right? And think about like judgments and
• 32:37
  Speaker 1: feeling good about the forest. You know, it just makes
• 32:40
  Speaker 1: no sense. You know, as an example, like if a
• 32:43
  Speaker 1: tree killed and ate another tree, it could still be
• 32:46
  Speaker 1: a vegetarian, you know, but that wouldn't make it like
• 32:48
  Speaker 1: good in some way, right? Anyway, my whole point is like...
• 32:53
  Speaker 1: putting these anthropomorphic moral questions onto trees just is really distraction.
• 32:58
  Speaker 2: Yeah, absolutely. And one of the review papers was arguing
• 33:01
  Speaker 2: that we need to stop like personifying trees and trying
• 33:04
  Speaker 2: to like, you know, lay our human values on top
• 33:07
  Speaker 2: of what we're seeing happening in a forest because they
• 33:10
  Speaker 2: mess with our ability to actually understand what's going on. And,
• 33:13
  Speaker 2: you know, these kinds of studies are important because they
• 33:16
  Speaker 2: can inform things like forestry projects. So if it is
• 33:19
  Speaker 2: the case that like a mother tree, has an outsourced
• 33:22
  Speaker 2: impact on the other trees in the area, you might
• 33:25
  Speaker 2: not want to cut the mother tree down, even if
• 33:27
  Speaker 2: that's the best tree for producing, I don't know, toilet
• 33:30
  Speaker 2: paper or lumber or something. Like it might change the
• 33:33
  Speaker 2: decisions that you make if one tree is having an
• 33:36
  Speaker 2: outsized effect on the other trees in the forest. So,
• 33:38
  Speaker 2: you know, these kinds of things are important, but also
• 33:41
  Speaker 2: these papers were pointing out that we have gotten way
• 33:44
  Speaker 2: ahead of ourselves in the narrative about what's happening in forests.
• 33:48
  Speaker 2: And to some extent, I think, There's been this desire
• 33:52
  Speaker 2: to not pull back because it's making people more excited
• 33:56
  Speaker 2: about forests and making them think that forests are more magical.
• 33:58
  Speaker 2: Maybe they care more about conserving forests. And so a
• 34:02
  Speaker 2: question that I've been seeing the field grapple with is like, well,
• 34:06
  Speaker 2: is it really doing any harm? And I would say yes,
• 34:09
  Speaker 2: because the truth almost always gets out eventually.
• 34:12
  Speaker 5: Hmm.
• 34:13
  Speaker 2: And if the public feels like they've been lied to,
• 34:15
  Speaker 2: I think you've done more harm than good in the
• 34:17
  Speaker 2: long term. And it's always, I think it's always in
• 34:19
  Speaker 2: our best interest to lead with what we really know
• 34:22
  Speaker 2: and what we don't know.
• 34:24
  Speaker 1: Everything you've been taught is a lie, man. It's a
• 34:27
  Speaker 1: very powerful message and it works.
• 34:29
  Speaker 2: Oh, and so your point is we need to be
• 34:30
  Speaker 2: honest from the beginning.
• 34:31
  Speaker 1: Yes.
• 34:32
  Speaker 2: Yeah.
• 34:32
  Speaker 1: Yes. Exactly.
• 34:33
  Speaker 2: Yes, absolutely.
• 34:34
  Speaker 1: Yeah.
• 34:34
  Speaker 2: So these three papers have been trying to push back
• 34:37
  Speaker 2: on like, look, here, this is really complicated. We don't
• 34:40
  Speaker 2: have a lot of data in the field. We do
• 34:42
  Speaker 2: know that some stuff can pass through fungus from one
• 34:45
  Speaker 2: tree to another. But like, what are the implications and
• 34:48
  Speaker 2: who's benefiting and how often that happens and how much
• 34:51
  Speaker 2: it matters for the trees? These are all questions that
• 34:53
  Speaker 2: we understand kind of in the lab, but we get
• 34:56
  Speaker 2: mixed results in the lab and we understand the results
• 34:59
  Speaker 2: even less in the field.
• 35:00
  Speaker 1: Which means we don't understand them at all.
• 35:02
  Speaker 2: Yeah, yeah. I mean, we understand them a little. You
• 35:05
  Speaker 2: got to start somewhere. But like, but yes, these things
• 35:08
  Speaker 2: are very difficult because ecology is very difficult. Designing these
• 35:12
  Speaker 2: experiments requires a lot of cleverness and, you know, trying
• 35:16
  Speaker 2: to tease apart when you're in a complicated environment, one effect.
• 35:20
  Speaker 2: Out of like, you know, a sea of stuff that's
• 35:22
  Speaker 2: happening can be really complicated.
• 35:24
  Speaker 1: All right. Well, let's send this conversation over to David
• 35:27
  Speaker 1: and see if we have scratched his itch.
• 35:31
  Speaker 5: Thank you so much for that answer. I really appreciate it.
• 35:33
  Speaker 5: It sounds like this topic has a lot more study
• 35:37
  Speaker 5: and perhaps debate needed to get to the final answer.
• 35:40
  Speaker 5: But it was a lot of fun hearing the both
• 35:42
  Speaker 5: of you talk about it. That's one thing I really
• 35:44
  Speaker 5: appreciate about the show is, quite frankly, it's your candor.
• 35:48
  Speaker 5: I like that you seem to really want to help
• 35:50
  Speaker 5: the greater community to understand the delicate nuances that come
• 35:54
  Speaker 5: with the study of the natural world. So thanks again
• 35:57
  Speaker 5: for all you do.
• 35:57
  Speaker 1: Thank you.
• 36:18
  Speaker 2: Okay, next up we have a question from Chris about
• 36:21
  Speaker 2: matter and antimatter annihilation.
• 36:24
  Speaker 7: Hello, Daniel and Kelly. I have a question about the
• 36:27
  Speaker 7: result of matter-antimatter annihilation. My understanding is that when matter
• 36:32
  Speaker 7: and antimatter meet, the electric charge cancels out, but the
• 36:35
  Speaker 7: mass needs to go somewhere, so it gets converted into energy,
• 36:39
  Speaker 7: and that energy gets dumped into the electromagnetic field, and
• 36:42
  Speaker 7: the result is photons. Do I have that right?
• 36:45
  Speaker 1: If so.
• 36:46
  Speaker 7: Do we have any idea why the energy goes into
• 36:49
  Speaker 7: the photon field and not, say, the Higgs field or
• 36:52
  Speaker 7: the Z boson field? They're both electrically neutral, right? Thank
• 36:56
  Speaker 7: you so much for your answer.
• 36:58
  Speaker 1: All right. Thank you, Chris, for asking this question. I
• 37:00
  Speaker 1: get this kind of question a lot, and it's a
• 37:02
  Speaker 1: wonderful opportunity to clear up some misunderstandings about annihilation and
• 37:06
  Speaker 1: matter and antimatter.
• 37:09
  Speaker 2: All right. And let's start with what is antimatter? I
• 37:12
  Speaker 2: know on the show, we've had some really interesting conversations
• 37:14
  Speaker 2: about like, why do we have more matter than antimatter?
• 37:16
  Speaker 2: What happened early in the universe to bring that about?
• 37:19
  Speaker 1: Yeah.
• 37:19
  Speaker 2: But let's start at the beginning. What is antimatter?
• 37:21
  Speaker 1: So particle physics is about seeing patterns. You know, we're
• 37:25
  Speaker 1: trying to understand what is the nature of the universe.
• 37:27
  Speaker 1: And the way we do it is by like, looking
• 37:28
  Speaker 1: at all the different kinds of particles out there and
• 37:31
  Speaker 1: looking for patterns among them, symmetries, clues as to what
• 37:35
  Speaker 1: might be like the next layer of reality, even deeper.
• 37:38
  Speaker 1: The way that we like looked at the periodic table
• 37:39
  Speaker 1: and we saw patterns and they give us clues about
• 37:42
  Speaker 1: atomic structure. Now we're looking at the pattern of the
• 37:44
  Speaker 1: fundamental particles and we're looking for clues that we hope
• 37:47
  Speaker 1: will reveal like the squiglions underneath or whatever it is.
• 37:50
  Speaker 1: And so we're very sensitive to these patterns. And one
• 37:53
  Speaker 1: of the fascinating patterns is is that every particle that
• 37:56
  Speaker 1: has a charge seems to have a partner. It's like
• 37:59
  Speaker 1: a mirror symmetry where you can take a particle and
• 38:01
  Speaker 1: you can say, okay, I found a particle with a
• 38:03
  Speaker 1: negative charge. Is there the equivalent particle that's exactly the
• 38:06
  Speaker 1: same but with the opposite charge? And the answer seems
• 38:09
  Speaker 1: to be so far always yes. So this is matter
• 38:12
  Speaker 1: and antimatter. It's a little bit confusing because we call
• 38:15
  Speaker 1: one matter and one antimatter. but it's really more a relationship.
• 38:19
  Speaker 1: It's not like there's anything mattery or antimattery about any
• 38:22
  Speaker 1: of the particles. It doesn't matter if you have positive
• 38:25
  Speaker 1: or negative charge, you can be matter and be positive,
• 38:27
  Speaker 1: you can be matter and be negative. The matter-antimatter thing
• 38:30
  Speaker 1: is a relationship. The electron and the positron, which have
• 38:34
  Speaker 1: opposite charges but the same mass, have that relationship. They're
• 38:38
  Speaker 1: a matter-antimatter pair. And which one you call matter and
• 38:41
  Speaker 1: which one you call antimatter doesn't even matter. We call
• 38:45
  Speaker 1: some of them matter because they're around in the universe
• 38:47
  Speaker 1: and the antimatter is gone. The thing to understand there
• 38:51
  Speaker 1: is that it's a relationship and every particle we found
• 38:54
  Speaker 1: so far has an antimatter partner.
• 38:55
  Speaker 2: And longtime listeners of the show will not be surprised
• 38:58
  Speaker 2: that physicists came up with a kind of confusing name
• 39:00
  Speaker 2: for this stuff. But Daniel is here to help lead
• 39:03
  Speaker 2: the way.
• 39:06
  Speaker 1: I'm not going to defend physics against your shade throwing
• 39:08
  Speaker 1: about naming because it's totally fair. Okay. So the second
• 39:12
  Speaker 1: topic we're dealing with today is annihilation. This is something
• 39:14
  Speaker 1: you hear about a lot. It's in PopSci. Matter and
• 39:17
  Speaker 1: antimatter meet and they instantly annihilate and turn into pure energy, man.
• 39:21
  Speaker 3: Wow.
• 39:21
  Speaker 1: And that's a little bit misleading. You know, you should
• 39:24
  Speaker 1: think about particle, anti-particle annihilation just the same way we
• 39:29
  Speaker 1: think about particles interacting, right? So when we have matter,
• 39:33
  Speaker 1: anti-matter annihilation, what happens is like an electron comes in,
• 39:36
  Speaker 1: a positron comes in, they turn into a photon. So
• 39:39
  Speaker 1: it's not pure energy. It always turns into some other
• 39:41
  Speaker 1: kind of particle. And so what's happening there is you
• 39:44
  Speaker 1: have two particles coming in, electron and positron, and one
• 39:46
  Speaker 1: particle coming out, right? So the particles that came in,
• 39:50
  Speaker 1: they're gone. The amazing thing about particle physics is that
• 39:53
  Speaker 1: it's not chemistry where you're rearranging the little pieces into
• 39:56
  Speaker 1: new organizations. It's alchemy. You're changing the kind of matter, right?
• 40:01
  Speaker 1: The electron and the positron are gone. They no longer exist.
• 40:05
  Speaker 1: You're transforming them into a photon.
• 40:07
  Speaker 2: Is it a photon plus energy or just a photon?
• 40:10
  Speaker 1: It's just a photon. Okay. is just energy though. It's
• 40:14
  Speaker 1: energy in the electromagnetic field. And so this is the
• 40:18
  Speaker 1: typical thing for particle physics is that the interactions turn
• 40:21
  Speaker 1: two particles into one or one particle into two. So
• 40:24
  Speaker 1: it's two to one or one to two. So examples
• 40:28
  Speaker 1: of two to one processes are like electron plus positron
• 40:32
  Speaker 1: gives you a photon, right? But you can also turn
• 40:34
  Speaker 1: this around. It's like a little tinker toy. You can
• 40:36
  Speaker 1: turn around in any direction. You can also do like
• 40:39
  Speaker 1: Electron absorbs a photon, stays an electron. That's a two-to-one process.
• 40:43
  Speaker 1: The photon is gone.
• 40:44
  Speaker 2: So that would not be annihilation.
• 40:48
  Speaker 1: That's not annihilation. That's right.
• 40:49
  Speaker 2: Okay.
• 40:50
  Speaker 1: Or you can have a positron absorb a photon and
• 40:52
  Speaker 1: stay a positron. It's fundamentally the same process, just in
• 40:56
  Speaker 1: a different order. But the one where we have matter
• 40:59
  Speaker 1: and antimatter come in and disappear to turn into a photon,
• 41:02
  Speaker 1: that's the one we call annihilation. It has a special name.
• 41:05
  Speaker 1: But they're really all different manifestations of the same fundamental thing,
• 41:09
  Speaker 1: which is two particles interact, one particle come out. Now,
• 41:12
  Speaker 1: the interesting thing about annihilation is that none of the
• 41:15
  Speaker 1: original particles come out, right? Whereas in the other ones,
• 41:18
  Speaker 1: like electron plus photon gives you electron, you start with
• 41:21
  Speaker 1: an electron, you end with an electron. So it feels
• 41:24
  Speaker 1: like it's the same electron. It's not really the same electron.
• 41:28
  Speaker 1: You know, it's like you have two particles come in,
• 41:30
  Speaker 1: one particle comes out. It doesn't have to be one
• 41:32
  Speaker 1: of the particles that it was originally. It can be.
• 41:35
  Speaker 1: but it's really a fundamentally different particle now because it's
• 41:37
  Speaker 1: like absorbed this photon. So when none of the original
• 41:41
  Speaker 1: particles survive and you get something new, we call that annihilation.
• 41:44
  Speaker 1: But it's, you know, it's just one example of kind
• 41:46
  Speaker 1: of things particles can do.
• 41:49
  Speaker 2: When you have an electron plus a photon and you
• 41:50
  Speaker 2: get an electron and that electron is different, is it
• 41:54
  Speaker 2: different in some, is it, how is it different? Yeah.
• 41:59
  Speaker 1: Well, you know, Every electron is the same in some sense.
• 42:03
  Speaker 1: They're all identical fluctuations of the same field, but it's
• 42:06
  Speaker 1: a different fluctuation now. It has more energy. So it's
• 42:10
  Speaker 1: not the same electron in that sense. And this philosophical
• 42:13
  Speaker 1: question of like, can you trace the same electron through
• 42:17
  Speaker 1: the universe or whatever, you know, I think is putting
• 42:19
  Speaker 1: like a classical framing on something which really is quantum.
• 42:24
  Speaker 2: Okay.
• 42:24
  Speaker 1: You know, because these particles can disappear, appear all the time.
• 42:28
  Speaker 1: So tracking an individual one doesn't really make any sense. Okay.
• 42:31
  Speaker 1: So you could also do the opposite. You can go
• 42:33
  Speaker 1: from one particle to two, like a photon- can turn
• 42:36
  Speaker 1: into an electron and a positron. That's exactly the opposite
• 42:40
  Speaker 1: of annihilation. We call it pair creation.
• 42:42
  Speaker 2: Why don't you call it anti-annihilation?
• 42:47
  Speaker 1: Because we're not as smart as biologists.
• 42:48
  Speaker 2: Kelly, that's why. Oh, I knew that. Yeah.
• 42:51
  Speaker 1: You can also have an electron emit a photon. So
• 42:54
  Speaker 1: it goes from electron to electron plus photon, right? And
• 42:57
  Speaker 1: so the ones where we have particle, antiparticle come in
• 43:01
  Speaker 1: and turn into something else, we call those annihilation because
• 43:04
  Speaker 1: it sounds cool. But it's really just an ordinary interaction
• 43:07
  Speaker 1: where neither of the initial particles survive. You can also
• 43:10
  Speaker 1: have annihilation without particle-antiparticle. Like you can have an electron
• 43:15
  Speaker 1: and anti-neutrino come together, and they turn into a W particle.
• 43:19
  Speaker 1: So that's not a particle and an antiparticle, but both
• 43:21
  Speaker 1: of the original ones are gone, right? So that's just
• 43:24
  Speaker 1: another example of an interaction. Basically, the point is, anytime
• 43:28
  Speaker 1: you have an interaction, the initial particles come in, new
• 43:31
  Speaker 1: particles come out. Sometimes some of the new particles are
• 43:33
  Speaker 1: the same as the old particles. Sometimes they're not. It
• 43:36
  Speaker 1: doesn't have to be. There's all sorts of rules about it.
• 43:39
  Speaker 1: So that's the basics of matter, antimatter, annihilation, and particle interaction.
• 43:44
  Speaker 1: We don't think of annihilation as special. It's just one
• 43:47
  Speaker 1: example of interactions that's gotten a lot of popular attention.
• 43:51
  Speaker 2: Okay, so pure energy, that phrase is just like hokum.
• 43:56
  Speaker 1: Well, you know, really what's happening here is that it
• 43:58
  Speaker 1: doesn't have to be matter. Like electrons and positrons can
• 44:01
  Speaker 1: turn into a photon, which we don't consider matter, it's radiation.
• 44:04
  Speaker 1: And so a photon is just made of energy, there's
• 44:07
  Speaker 1: no mass to it. But it's not like it's energy
• 44:10
  Speaker 1: disassociated from any sort of field. It's always energy in
• 44:14
  Speaker 1: a field. You have the electron field, which has an
• 44:16
  Speaker 1: electron and a positron in it, has ripples, they come together.
• 44:19
  Speaker 1: They annihilate, it dumps that energy into the electromagnetic field,
• 44:23
  Speaker 1: then it ripples. So yeah, pure energy is sort of misleading.
• 44:27
  Speaker 1: It turns into a photon. And Chris's question is, does
• 44:30
  Speaker 1: it have to? Can it turn into something else? This
• 44:33
  Speaker 1: is an excellent question, Chris, and you're totally right that
• 44:36
  Speaker 1: it can turn into something else. For example, the electron
• 44:39
  Speaker 1: and positron can turn into a photon, and they most
• 44:42
  Speaker 1: often do because that's the most powerful interaction they have.
• 44:46
  Speaker 1: but they could also turn into a Z boson, right?
• 44:49
  Speaker 1: There's nothing preventing that. The Z boson has net zero
• 44:52
  Speaker 1: electric charge. So it satisfies all the accounting rules where
• 44:56
  Speaker 1: you have to conserve electric charge. You come in with
• 44:58
  Speaker 1: a positive and a negative. So the total charge of
• 45:01
  Speaker 1: the initial state is zero. So the total charge of
• 45:03
  Speaker 1: the final state has to be zero. So you need
• 45:06
  Speaker 1: something with zero charge and the options are photon, we
• 45:09
  Speaker 1: know that happens, Z, that definitely happens, or a Higgs.
• 45:12
  Speaker 1: We haven't seen that happen, but we're pretty sure that
• 45:15
  Speaker 1: it does happen. And so electron and positron can turn
• 45:18
  Speaker 1: into anything that's neutral that satisfy all of the particle
• 45:21
  Speaker 1: physics accounting rules.
• 45:22
  Speaker 2: So is this one of those amazing situations where the
• 45:25
  Speaker 2: listener is 100% correct?
• 45:29
  Speaker 1: He is. And he's even more correct than he thought
• 45:31
  Speaker 1: because the same insight he had applies to other situations.
• 45:35
  Speaker 1: Like two quarks can annihilate and they can annihilate into
• 45:38
  Speaker 1: a photon or a Z or a Higgs. They can
• 45:41
  Speaker 1: also turn into a gluon because they have that kind
• 45:44
  Speaker 1: of interaction. So there are other things that you can
• 45:46
  Speaker 1: turn into. So yeah, Chris, you're 100% right. And when
• 45:50
  Speaker 1: people talk about annihilation and they say pure energy, they're
• 45:52
  Speaker 1: really probably referring to photons. But it's more general. You
• 45:56
  Speaker 1: can turn into a photon, a Z, a Higgs. And
• 45:59
  Speaker 1: more broadly, you should just think about annihilation as one
• 46:02
  Speaker 1: kind of particle interaction. It feels important whether or not
• 46:07
  Speaker 1: the initial particles end up in the final particles, but
• 46:09
  Speaker 1: it's not really. From a particle physics point of view,
• 46:12
  Speaker 1: we sort of give up in the initial particles anytime
• 46:14
  Speaker 1: there's an interaction. Whether the final particles share a label
• 46:18
  Speaker 1: with the initial particles doesn't really matter. We don't think
• 46:20
  Speaker 1: of them as the initial particles anyway.
• 46:23
  Speaker 2: All right, well, let's ship this answer to Chris and
• 46:25
  Speaker 2: see what he's got to say.
• 46:27
  Speaker 6: Hi, Danielle and Kelly. Thanks for answering my question. That
• 46:30
  Speaker 6: was very interesting. I knew that pure energy was just
• 46:33
  Speaker 6: a scam. I'm also very intrigued by what you said
• 46:36
  Speaker 6: about the photon interaction being the most powerful interaction. But
• 46:40
  Speaker 6: I suppose that's for another show. Can't wait to hear
• 46:42
  Speaker 6: more particle goodness.
• 46:44
  Speaker 3: Thank you very much.
• 46:45
  Speaker 1: All right. Thank you, everybody, for asking these questions. We
• 46:47
  Speaker 1: really do love hearing from you. And we love that
• 46:49
  Speaker 1: you are curious about the universe. And I'm just happy
• 46:52
  Speaker 1: to be along for the ride.
• 46:53
  Speaker 2: We massively appreciate that you come to us when you
• 46:56
  Speaker 2: have a question about how accurate a topic is or
• 46:59
  Speaker 2: you want to dig in a little bit more deeply.
• 47:02
  Speaker 2: Thank you for trusting us with your questions. We appreciate it.
• 47:11
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• 47:17
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• 47:19
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• 47:23
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• 47:24
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• 47:34
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• 47:39
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