When A.I. Comes for Your Purpose in Life

From the New York Times, I'm Rachel. [music] Abrams, and this is the Daily. This morning, one of the hardest. problems in mathematics may have just. been solved, not by a human, but by. artificial intelligence. So many people. are freaked out about AI taking their. jobs. And [music] now you can add. mathematicians to that list. In the span of just a few months, AI. [music] systems have tackled some of the. biggest problems in math. And they've. solved them.

>> For decades, mathematicians have been. working to solve this problem. They. haven't been able to crack it. But AI, it just did it easily. >> Last month, it was one of the field. so-called millennium problems. And then. last week, OpenAI released a torrent. [music] of over 350 other results. covering virtually every corner of. mathematics. These are problems that. literally the [music] smartest humans on. Earth have been working on for decades. and sometimes even centuries. This AI. takeover of the field has raised some.

uncomfortable questions and unleashed. [music] a range of emotions. And so today we speak with a. mathematician, [music] Cornell math. professor Steven Strogatz, about what. happens when AI doesn't just [music]. come for your job, but for your purpose. It's Tuesday, [music] October 13th. >> [music]. >> Steve Strogatz, thank you so much for. joining us. >> Hi Rachel. I'm really happy to see you. and and be here with you.

>> So Steve, one of the reasons we're. talking to you is of course because you. are an esteemed professor at Cornell, but we are really talking to you because. you are known for translating math into. English for those of us who do not speak. math. And you actually have a book that. gets at this very thing. It is called. Big Math. And so we are turning to you. to help sort of translate this. destabilizing moment in math that you. and your colleagues are living through. >> Yeah. >> Can you just start by explaining to us. how would you sum up the mood in math.

right now? >> Ooh, the mood. Yeah, the mood. The mood. is um discombobulated. We're all a swirl. We're spinning. Some. people are exhilarated. M. >> they see a great golden age upon us with. the help of our computer friends. Most. people I think are feeling grief and. we're in different stages. Some people. are bargaining, some are accepting, some are still denying. >> What stage of grief are you in.

>> today? I'm feeling pretty good. I have. been dark. There have been days when I. uh actually cried during interviews. M. >> you know what's actually being lost is. um we like to think of ourselves as. heroes and maybe that's over. >> That's the big news. >> It's hard to be a hero if you can't be. the first to solve a problem or to. understand something or to explain it to. the world. >> And for the thousands of years that our.

culture of math has existed, >> we some of us did fantasize about being. heroes in our own story. And now that's. over. At least I mean this is the grief. version of myself talking. I can of. course like any professor argue against. myself but in the sad version we won't. get to be heroes anymore. >> I want to get into this Steve because I. feel like a lot of people listening. aren't going to quite understand what. you mean by heroes and what you mean by. solving problems that nobody's ever. solved before.

>> Sure. >> Like when I think of for example a math. college professor teaching students, I'm. thinking of that scene in Goodwill. Hunting. I also put an advanced FIA. system on the main hallway chalkboard. and I'm hoping that one of you might. prove it by the end of the semester. >> Where Matt Damon's character is solving. a very long complicated math problem on. a chalkboard. >> Sorry. What do you do? >> Sorry. That's people's work. You get graffiti. here. >> Am I Matt Damon in this version of the. story? >> You're either Matt Damon or you're the.

guy that finds Matt Damon's proof and. thinks, "My god, this guy's a genius and. he just needs a little bit of support.". Oh my god. >> Looks [music] right. >> I'm probably that guy. >> Okay. So, is it I'm sure that your days. are more complicated than this movie. scene. I'm just trying to get a sense of. like are you helping people solve what. lay people might think of as complicated. puzzles or. >> That's a good word for it. Yes. Our. world is about puzzles. >> Mhm. >> It's puzzles that we create depending on. which type of math we're doing.

Sometimes it's puzzles that nature hands. to us. That's the kind of math I do. actually. >> Wait, tell us what you mean by that. That's really interesting. >> Well, here's something I've personally. been fascinated with, which is how do. groups get organized? [music]. >> Could be groups of people, could be. groups of crickets. I mean, that's one. thing I might be known for. We have. crickets here in Ithaca, a species. called the snowy tree cricket. They. chirp in enormous [music] choruses, all.

chirping in unison, in sync. And. normally like if you see people say in a. symphony all the musicians playing in. perfect time they're very highly trained. expert musicians but yet these crickets. can do it too. >> How do they chirp in unison? [music] And. we now know the answer that when one. cricket hears the chirp of another. >> it actually makes a little adjustment. into its nervous system that changes the. timing of its next chirp. >> And we can measure that. So now the math.

problem is suppose I have a thousand of. them in a field. How does that. collective get itself so that all. thousand are chirping in unison? >> And and you might say I don't care about. crickets but your heart also has things. that go in unison. You have special. cells that trigger the rest of your. heart to beat and pump blood to your. body and to your brain. And if those. 10,000 cells are not organized to all. zap in unison telling your ventricles to.

pump, you're going to be very sick. >> So what I'm saying is to a. mathematician, these are the same. problem. >> This is variations on a theme. And what. we try to do is abstract the messy real. world into a very beautiful pristine. mathematical world where we can figure. out how can we encourage synchrony, how. can we discourage it. There are cases. where it's bad, like people who have. Parkinson's disease or epilepsy. Their. brain cells are firing in sync when they. shouldn't be. This is a math problem.

[music]. [music]. So, just to summarize, Steve, what. you're saying is that understanding a. thousand crickets and why they move and. make the noise that they do, that might. not be of great interest or importance. to many people. However, understanding. that can help you understand how the. heart works, for example. And so, it. might not matter to me how a cricket. makes its noise, although I am actually. interested in that, listening to you.

explain it. But it should matter to me. because the downstream effects of that, the medical implications, that matters. to me. >> That's the argument. This is a a spooky. aspect of history that if you let people. follow their curiosity, >> great things can happen, but it might. take centuries. I feel like what I'm. getting from what you're describing is. the sense that you are a person who. doesn't just care about math because. it's good for your job or to get to an. end result. I'm getting the sense from.

you that you care about understanding. math because you are curious about the. world around you. >> Uhhuh. >> And I wonder if you could just talk a. little bit about how people feel about. themselves in the math field and the. feelings they get from actually solving. some of these puzzles. M yeah it's good. to go here this question of feeling is. what's at stake among other things you. mentioned puzzles. >> think about what it is to do whatever. your favorite puzzle is jigsaw sodoku.

cross word puzzle it sort of feels good. to be stumped and then it's satisfying. when you have a breakthrough. >> when you finally get the word of the. cross word yes. >> yeah isn't it good everything clicks. into place. >> people like that feeling and a lot of us. live for that we want that. It's like an. addiction. We want that feeling over and. over. >> So, we're in the business of of. pleasuring ourselves that way. Now, >> c can I ask you to describe that. slightly differently? >> I wanted to say it that way because I.

actually think it is sexual slightly. [laughter]. >> Oh god. >> Because think of it or abstractly if I. don't want to be too risque here, but. what's going on is there's a quest. In. our case, we want answers. We want. solutions and we want understanding. We. want aha moments. And so the fact that. the problems resist and have to be. seduced into yielding their secrets, it's kind of sexy. Steve, >> I'm sorry. I You Why are you resistant. to that? >> I'm not. I'm just thinking about how. many 17 18-year-olds are hearing this.

and are suddenly going to sign up to be. math majors. You just recruited an. entire generation of mathematicians. Um, no, this interview took a bit of a turn, but I think a lot of people can. appreciate and relate to the idea of the. satisfaction. >> I I hope that you will keep this part of. it because this is real. >> You know, I'm not the mathematician, Steve, so you are the expert. I'm going. to defer to you about the pleasure you. people get from math. But I just I just. want to really underscore that to a lot. of people this is not just a job that. for some people this is a passion that. goes beyond a career. And and so this.

kind of gets me to my next point, which. is I want to talk about this kind of. dawning realization that you and other. people in your field have had about how. disruptive AI could be. And I want to go. back and have you explain to me and to. our audience what your initial. perception of AI was. >> Yeah. You know, at first we were. laughing at AI. You may remember when it. first came out, it was hallucinating all. the time. Couldn't be trusted. There was. a joke going around and it was real. It.

couldn't count the number of Rs in. Strawberry. >> Sure. >> But that was like 2022. >> As the years went by, AI got better at. math very fast. It soon was as good as a. good high school student. Then it was a. good college student. Then in March of. this year, it started to be at the level. of a very good graduate student, which. is a a measure that means something to. us as math professors because we deal. with grad students all day long. >> Can you just explain what you started.

seeing that made your perception change? >> Sure. So, this is the gets into the word. that's everywhere nowadays, benchmarks. >> We give them tasks and we see can they. solve a problem and how do they solve. it? Do they solve it with style? I guess. I keep coming up with adjectives that. will surprise you. Besides our our sexy, passionate nature, we're also very. interested in style. We want to be. elegant. The highest compliment you. could give to another mathematician is,

"Oh, what an elegant proof.". >> What does elegant mean for us? Elegant. for us is something that has a surprise. value, but is also inevitable once you. see it. >> You know, it's a head slapping thing. like that is so perfect. That's such a. great argument. Why didn't I think of. that? So for a long time, the AIs, the. computers were not producing elegant. math. They behaved like machines. >> They would get the job done, but with no. panache, no flare. >> But by May of this year,

>> everybody sat up straight when an AI. solved a problem that the world's best. mathematicians were interested in. And. it did it with flare. >> Okay. So take us to May, Steve. What did. you see exactly and how did it start. changing your perception about what the. models could do? >> Well, first of all, it solved a problem. that a very charming old mathematician. had posed because it's part of the. culture. I have to tell you why this. problem is a beloved problem. Tell me.

>> this is a puzzle associated with a. mathematician named Paul Erdish, a. Hungarian American mathematician who was. completely ridiculously. unable to function in the real world. >> What do you mean? >> He didn't really have a home. He would. show up at your door, meaning you're any. mathematician around the world. He had a. beaten up old suitcase and he would show. up and say, "My brain is open." which. means I'm ready to work with you on a.

math problem. >> And that meant you had to take him into. your house and take care of him because. he couldn't make toast. >> Wow. I'm really starting to get the. sense that mathematicians are real. characters, Steve. >> He was also a methamphetamine junkie. >> Wow. Okay. >> This guy would take so many drugs. because he was hyped up all the time. wanting to do math. Wow. >> Now, why would anyone put up with him? Well, he was extremely brilliant and he. would come up with these really good. questions which it turns out is not so. easy to do.

>> So, he was a really good puzzle poser. and he also left the legacy on the order. of a thousand great puzzles that we. still don't know how to solve. >> I want to say one last thing about him. So he was constantly winning money. because he was so good at math that. people would give him prize money for. various things and he would always give. the money away to young people. >> including a person who today is. considered maybe the greatest living. mathematician. >> Terry Tao. >> and so there's a picture of little Terry.

Tao who is often called the Mozart of. math because he was a child prodigy. Um, sorry I'm getting a little far clempted. here. Like they say on Saturday Night. Live. >> No, something you can't. >> It's so sweet this generational passing. You see the picture of old man Erdish. with his gray hair. [laughter]. >> The two of them are um are sitting. together with their their paper and. pencil. Such nerds with very serious.

faces. Terry Tow is like eight years. old. two great mathematicians working. together happy as clams. on some real world inconsequential. problem for the pleasure of being. together. So my point was that Erish was. a sweet man in addition to being a. junkie and a genius. [snorts and laughter]. >> Not mutually exclusive. >> Not all exclusive. Okay. So the fact. that Erdish left us these beautiful. problems and aing machine you could.

bleep me solved the unit distance. conjecture of Erdish. >> Okay. Wait wait wait. We have to. translate this into English. So in May. one of the puzzles created by this man. Erdish that people have spent a lot of. time trying to solve and it is a passion. it sounds like. >> Oh yeah. >> What happens to it? It's solved by AI. Is that what you're telling us? >> It's solved by AI. >> Okay. >> Open AI. Somebody in there sets their. machine to working on the one of these. great airish problems and.

it solves it in a way that is very. beautiful, >> elegant, >> absolutely elegant, very imaginative, an. aesthetic high point. All of us are a. Gogg because this is so clever and we. can learn from it. That this is. important. It's not opaque. We can. follow what it did. It took a certain. part of math. You don't need to know. what I mean by this, but it's called. discrete geometry. >> Okay? >> And then it took a different part of.

math called number theory. Two parts. that hadn't talked to each other much in. the past and found that if you put them. in conversation with each other, that. was like the magic key. >> No one thought of making that connection. before it, it was the key to the. problem. >> So, objectively, what it did was really. beautiful and brilliant, but it wasn't. human. Now, it stole, some people would [music]. say it stole from us because it learned. from thousands of years of human.

ingenuity. >> It built from other people's work. >> It read every book. It read every math. paper. It has knowledge like no human. [music] mathematician has ever had. So, it's a totally alien kind of. intelligence [music] that we have. created that's now teaching us. That's. what happened in May. That was just May. This is October. >> [music]. >> We'll be right back.

I want to get back to the story of this. dawning realization in your world, Steve, about AI. So, you have this big. aha moment in May. We are now, as we. speak in the month of October. Last. month, OpenAI announced that it had. solved this huge math problem, this. millennium problem. And it made all kinds of news that had. broken outside of the math community. It. got to lay people like me for instance. And I want to just explain first of all.

to our listeners what is the millennium. problem and why did this feel like a. watershed moment? >> So there are seven of these problems. They were all formalized to commemorate. the new century. That's why they're the. millennium problems. M. >> in the year 2000 some of the world's. finest mathematicians got together to. take stock like where are we in our. field and what are seven great mountains. that we would like to climb. >> these are problems created for the sake.

of solving them like what you were. talking about earlier these are problems. made for the satisfaction of solving. them. >> that's right they're aspirational. puzzles so these were our big dreams. seven of them in seven different parts. of math. >> okay. >> so One of the seven millennium problems. was already solved a few years back by a. really crazy Russian mathematician who. didn't like to cut his fingernails. So. that they got very long. Like imagine if. you never cut your fingernails ever.

>> I don't want to make all mathematicians. sound crazy or drug addicts or whatever. Uh. >> I'm just giving you [laughter] the truth. here. >> You're going to have to list some other. ones then. >> But I'm just so. >> Okay, one of them was solved already. There were six remaining. By um. September, rumor started to leak out that one of. the AI companies was on the verge of. solving one of the last six millennium. problems. And the rumor was that it was. the one called the Navier Stokes.

Millennium problem. >> And what was that? >> Well, it's about water and air and how. they flow. >> Okay. And the equations we use to. describe the flow of anything that can. flow, honey, lava, blood, that's. governed by these equations, the Navier. Stokes equations. And it was to show. that the Navier Stokes equations could. break down is what the problem was. Show. that the Navier Stokes equations have a. fatal flaw. >> And so Steve, when you start hearing.

rumors that the AI had solved this. problem, your reaction is what? >> There was a feeling of like this is it. This day has come. >> Wait, that sounds is I can't tell if. you're doomer about this. >> That's the problem. No one knows how to. feel. You might have an instinctive. doomer feeling or techno optimist. feeling and then you felt happy or you. felt terrified. My first feeling I guess. was terrified. [music].

Are we as a species. going to be able to control this thing. we created? M. >> we knew that it would get to be. superhuman in intelligence. We already. saw it in chess. But chess doesn't. matter. Math matters. And now that it's. superhuman in at least one aspect of. math, the problem solving aspect, [music]. it's going to be a short hop and skip. and a jump till it's superhuman at all. aspects of math, I [music] think. And where does that leave us in math?

But also, are we the canary in the coal. mine. >> for all of human creative work, whether. it's poetry or [music]. journalism or medicine or you name it. Now, maybe it's a great thing. Maybe it. cures cancer for us. Maybe [music] it. develops new kinds of batteries. I mean, this is not just idle speculation. It. it's quite possible if we're smart. enough to harness this well, we might be. in the best time ever to be alive.

>> But why I'm [music] tend to be a doomer. is because we don't understand this. [music] thing we built. If you ask it, how did you solve the. Navier Stokes Millennium [music]. problem? It gives a crappy explanation. >> It can't explain itself very well. Maybe. it will get better at explaining [music]. itself. Even if it does, I think it will. become so advanced so soon that even the.

best, most patient [music] pedagogical. teacher will not be able to explain. itself to us because it'd be like me. teaching calculus to my dog. >> My dog can't understand it. I'm too. advanced for it. And I think we are. going to be in the relation to AI. [music] that my dog is to me soon. And therefore, how do I know it's going to have my as a. species, my human species best interest. at heart?

[music]. I want to just zoom out here for a. second, Steve, because I'm hearing two. things from you. I'm hearing that you. are both scared of the actual tool. itself and it getting out of the control. of humans, but I am also hearing from. you that you're worried about this tool. taking away the satisfaction that people. get and the passion people have of just. solving problems. Sort of this.

fundamental thing that we can all relate. to. >> Yes, both. It's just so easy to dismiss. the second one. Mh. >> The satisfaction that we get from. solving problems. and the joy we get as a community to. work on things together, all that, all. the human stuff. There's a certain hardboiled kind of. person who says that's not that. valuable. Like, so what? Get over it. Suck it up. >> Really, but everybody understands what. it means to have something that gives.

you fulfillment. >> They should. A sensitive person acting. in good faith gets that. But I can tell. you if you look at the internet, especially Go on X, where there's a lot. of people talking about AI and math, there's a very popular joke that I. actually think is pretty funny going. around that says, "Put the fries in the. bag, Terrence.". >> Terrence Tao, who we spoke about, >> Terrence Tao. Yeah, that same Terry Tao. You know, there's certain people who are. gloating, who have a feeling of.

satisfaction that the smartest kid in. the class is now going to be out of a. job just like the rest of us. That when. automation comes, it's bad for everybody. Now it's your. turn. But just for a moment, if you'll. indulge me, I I want to talk about the. optimistic case for a second and just. pose that to you because the good faith. optimistic case for AI has always been. that AI could do the grunt work and then. free you Steve up to do more meaningful. work, right? Like the AI companies will. say, "This is a tool and it's only going.

to allow you to continue doing what. you're trying to do but better." And I. just sort of wonder if you think that. that argument could be correct. I do. think it could be correct. I really do. I'm going to put on my happy face now. And I'm not faking it. I actually do. find it very exciting and uplifting. And. and the teacher in me who wants young. people to have hope. I don't want to lie. to them. I want them to have hope and I. want to mean it.

>> At Cornell, I'm teaching kids, juniors, seniors in college. >> Mhm. and they are in the best position. to make use of this new tool because. they're young and adaptable. I mean the. other day at a town hall meeting the. math department of course were all. freaking out. So all the students are. gathered all the faculty at this meeting. one of my colleagues said it's like a. new telescope. Where are you going to. point your telescope? So that's the. question. Where are you going to point. this thing that's going to determine.

your success in the future? Mhm. >> I guess the question though is in this. optimistic scenario, are we sure that. the AI won't take all of it? Like who. says it won't start to have great taste. or it won't come up with good questions? So far it has no aptitude for asking. good questions. It's there's no Paul. Erdish AI yet. >> That can podcast host is still a pretty. safe profession. >> I don't know. Maybe. Okay, that's the. question. [laughter]. >> Are you using AI in your own work, Steve, yet at all? Yes, I do use it and.

I really enjoy it. >> For what? >> I use it for all kinds of things. Uh, I. use it to teach myself. I can ask it. questions like if I wanted to learn. about the millennium problem, the Navier. Stokes problem. I'm not an expert in. that. I ask it to tutor me and it's so. good. >> But not just math. I mean, I'm really. curious about all of school. I always. have been. I loved all the subjects. So, this will really get us to a strange. place. But it's true. I've been thinking.

a lot about the meaning of life. Like. what's the point? >> Which is a question a lot of us are. asking in my profession. What is the. point? >> What have you come up with? >> Oh, you want to know the answer to that. one too? >> I would love the answer. [laughter]. >> Yeah. >> I I'll just tell you about my journey, not the answer. I know that people have. thought about this for millennia. And um. I was reminded of this old birds song, turn. [music].

[singing and music]. >> So that's biblical, >> right? That's from um Ecclesiastes, >> which I had never read. So I wanted to. learn what's Ecclesiastes about because. there's a famous passage in there where. he says basically he's looking around. and trying to understand what's the. point of it all. >> And his really very modern and very dark. initially dark seeming conclusion is. there is no point. It's all vanity. He.

says it's vanity of vanities. Everything. I do is in vain. It accomplishes. nothing. >> I got nowhere by the people I loved, the. work I did. It was all for nothing. And. he says in there, he the writer says, you know, that in one famous passage. like I saw. um that the the race I'm sorry, it's so. beautiful. >> No, >> he says the the race is not always to.

the swift. Like the the fastest person doesn't. necessarily win. um or the battle is not. to the strong. >> Mhm. >> In other words, people don't get what. they deserve. It doesn't work like that. Life is. unfair. The race is not to the swift. The battle is not for the strong. Time. and chance happenth to them all. Like. there's a lot of randomness. >> We get old. A lot of random stuff.

happens. >> no matter how strong or fast we are. It's true for all of us. Okay. So then. you think, Jesus, who wants to live in a. world like that? Well, >> wait, but you're you know what you're. making me think of is that isn't math. the way you've described in an attempt. to understand and organize and make. things a little bit. >> right. We're pushing back. Sure, we. would like to find meaning, but but if. you really go to this place. in the book, then you know you're a. blip. You only live a short time,

certainly in cosmic sense. So what are. you going to do with yourself while. you're here? The re Okay. Why am I going. to this insane place? Because this is. kind I think this is where we are in. math because this is when the student in. the back of the room at our town hall. says, "What's the point? If the AI can. do math better than any human being, sure, I could sit there and learn from. it, but I want to discover things too. I. want that thrill. >> I don't want my life to be in vain.". >> Yeah, I that's exactly right. I don't.

want my life to be in vain. So, if you. take that away from me, what am I doing? So, you could see why I'm looking to um. this ancient wisdom. And as I understand it, I'm no biblical. scholar, but as I understand it, the. lesson is well that your life is. precious because it's short. That you have to make the most of it. while you're here. So, um. there's another line in there that says.

everything is beautiful. in its time. Like, God, [clears throat] I'm sorry. I'm a wreck. But if everything is. beautiful in its time, we get to have a. turn where. we're in love. We're creative. We do. what's meaningful. We get to walk our. dog, whatever. We do have a time. Otherwise, we're dust, right? We're. ashes to ashes, dust to dust. The. beautiful part is the in between.

Am I getting to you? No, I'm not crying. You're crying. [laughter]. >> I think I did get to you. [gasps]. >> I'm the only one that gets to make. people cry on this show, Steve. >> Whoa. >> What did you say to the student? What. did you say to the student who wanted? >> I didn't answer. It was a town hall, so. there were like there were a hundred. people there, but I want to talk to her. later. >> What would you say? >> Said, "Let's read this book together.". [laughter].

>> Are you saying that AI is going to make. you more religious? I think it it sure. seems like it, doesn't it? >> Yeah, maybe. >> But I don't think it's about God. I. think it's about people. [music]. >> Um cuz that I do believe in. >> Maybe. you [music] are also appreciating what. makes us human more and what makes our. time here special. And I wonder if that. is [music] going to maybe in your mind. be one result of all of this. >> I think it might. [music] It might. I.

hope so. Man, you're killing me, Rachel. [laughter]. This was supposed to be a math show, but. you took me to a really interesting. place. Thank you for coming with me. >> I hope that our [music] listeners get as. much out of this conversation as I did. So, Steve Strogat, thank you so much for. joining us. >> Wow. Thank you, [music] Rachel. I'm. going to have to go home and lie down. [laughter].

>> [music]. [music]. [music]. >> We'll be right back. [music]. Here's what else you need to know today. In an effort to boost Republicans in the. midterms, Elon Musk has funneled more. than $276 million [music] to Republicans.

and his super PAC since the beginning of. July. That makes Musk [music] by far the. largest known donor this election cycle. To help Republicans maintain their. Senate majority, Musk's team says he. [music] gave $158 million to the party's. Senate Leadership Fund. That's one of. the largest political donations in. [music] American history. and Homeland Security agents searched. the DC home of Representative Ilhan Omar. on Monday. [music].

The search comes after Homeland Security. Secretary Mark Wayne Mullen said last. month that his department was [music]. investigating Omar and her family's. immigration to the US. He suggested that. one outcome of that investigation. [music] could be deportation. Omar has long said that such allegations. of immigration fraud are false and. politically motivated. The search took place less than 60 days. before the midterms. [music] In spite of. a Justice Department policy that. discourages unnecessary investigative. steps in [music] that window out of fear. of influencing elections, Omar is.

running for reelection in Minnesota. Today's episode was produced by [music]. Jack Dadoro, Adrienne Hurst, and Michael. Simon Johnson with help from Eric. Krupkkey. It was edited by Annie Minoff. with help from Paige [music] Cowat. contains music by Pat McCusker and. Marian Lozano. Original music by Martin. [music] Courtourtney. It was engineered. by Alyssa Moxley. Our theme music is by. Wonderly. And special thanks [music] to. Dan Powell.

That's it for the Daily. [music] I'm. Rachel Abrams. See you tomorrow.