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Speaker 1: But knowledge to work and grow your business with c

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Speaker 1: T dot com put Knowledge to Work. Hello, and welcome

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Speaker 1: to another episode of The Odd Lots podcast. I'm Joe

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Speaker 1: Wisenthal and I'm Tracy Alloway. So, Tracy, before we uh,

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Speaker 1: before we got on here, you were joking that today's episode,

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Speaker 1: if we wanted, we could probably go for three hours,

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Speaker 1: or at least I could. Yeah, I'm I was joking

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Speaker 1: about that, and I'm laughing right now because I think

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Speaker 1: I can sense your excitement from thousands and thousands of

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Speaker 1: miles away from New York. Yeah, I'm not going to

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Speaker 1: deny that I'm extremely excited about today's episode, and if

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Speaker 1: we didn't have time constraints, we could probably go a

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Speaker 1: long time. But I really think our guest today is

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Speaker 1: right in the sweet spot of some of the biggest

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Speaker 1: themes that we've covered on this podcast and all the

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Speaker 1: time that we've done it. Wait, so who is it

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Speaker 1: and why do you think that? So we're not We're

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Speaker 1: We're just gonna completely dispense with all the preamble and

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Speaker 1: jump straight into the guests and just just get right

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Speaker 1: into it. Well, I have a major preamble that I

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Speaker 1: want to give before we get to the guests. But

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Speaker 1: why don't you say who it is? Because I know

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Speaker 1: that you you want to talk about him. Okay, I'm

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Speaker 1: very excited. Our guest today is maybe the first sort

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Speaker 1: of legit celebrity that we've had on the Odd Lots podcast.

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Speaker 1: His name is Hikaru Nakamura. He is one of the

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Speaker 1: top chess players in the entire world right now. He's

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Speaker 1: rated around number seven. I believe he he was a prodigy,

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Speaker 1: he earned his he became a grand master at age ten.

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Speaker 1: If Wikipedia is correct. He's mad, he's giving a face.

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Speaker 1: I don't know if that's completely agurd, but very young.

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Speaker 1: And in addition to being a chess phenomen he also

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Speaker 1: does other stuff like trading options and at times play

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Speaker 1: poker competitively. So he's just a completely sweet spot Odd

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Speaker 1: Lots guest here, all right, And obviously we've had a

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Speaker 1: lot of episodes about trading and investing. We've also had

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Speaker 1: episodes in the past about chess and poker, so I

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Speaker 1: can see how you would be very very enthusiastic about

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Speaker 1: this one. Here is my major preamble for you, Joe.

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Speaker 1: I know absolutely nothing about chess. In fact, I find

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Speaker 1: chess extremely frustrating because whenever I play with my husband,

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Speaker 1: he doesn't let me win ever. In fact, he uses

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Speaker 1: that what's that thing where you win in like two moves?

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Speaker 1: That's so cringe, so cringe the the scholars mate, Well,

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Speaker 1: whatever it is, he's done it to me several times now,

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Speaker 1: and I fall for it every single time. So everyone

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Speaker 1: is just going to have to bear with me asking

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Speaker 1: very very basic questions about chess. But I actually read

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Speaker 1: on Hikaru's Wikipedia page as well that he has an

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Speaker 1: uncommon enthusiasm for chess and is known for being far

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Speaker 1: more approachable than other players of his abilities. So I'm

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Speaker 1: I'm sure he's going to humor me, right, Well, I

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Speaker 1: think that sounds like an ideal guest. So I say

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Speaker 1: we should just get started, Karo. Welcome up, Welcome to

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Speaker 1: the show. That's good to be here, Joe. So you

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Speaker 1: were making a face. I said something about when you

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Speaker 1: achieved grand master status? When did you become a grand master?

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Speaker 1: And uh, I think it said you got there faster

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Speaker 1: than Bobby Fisher did, Is that right? Yeah? So, um,

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Speaker 1: so when you referred to being ten years old, that's

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Speaker 1: when I became the youngest master. That's slightly lower ranking,

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Speaker 1: but um, at the time, at least they had they

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Speaker 1: kept that record in the Guinness Book of World Records.

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Speaker 1: So when I was very young, like I was trying

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Speaker 1: to get to that level and so that was important.

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Speaker 1: But I became a grand master I was fifteen years old.

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Speaker 1: I was about a month and a half younger than

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Speaker 1: Bobby Fisher. So that made me the youngest American grand

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Speaker 1: master in the world at the time. I guess that's impressive.

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Speaker 1: Uh when did you so, you know, it's always sort

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Speaker 1: of an interesting question with sort of young prodigies phenomens.

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Speaker 1: What was the moment early on where someone or you

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Speaker 1: recognize that you were on a different level than other

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Speaker 1: people who were learning at the same time. Well, I think, um,

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Speaker 1: it's not so much that I was on a different level.

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Speaker 1: There are there are a lot of talented young kids

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Speaker 1: who played chess, and um, I think it was just

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Speaker 1: that I had this this will will in, this motivation

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Speaker 1: with within me to just keep trying to win every

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Speaker 1: game all the time and UM. And when when you

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Speaker 1: start performing, even at like eleven or twelve years old,

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Speaker 1: by being strong grand masters, UM, it certainly shows that

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Speaker 1: you have talent and and it's it's a matter of

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Speaker 1: taking that talent and just keep keeping up and keep

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Speaker 1: going forward with it and UM. And basically, I would

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Speaker 1: say from the time I was about fifteen, I had

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Speaker 1: a certain inclination that I might become a professional chess player,

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Speaker 1: but it wasn't until I was probably nineteen or twenty

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Speaker 1: that that actually came to fruition because I've had a

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Speaker 1: lot of many other interests in my life, so there

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Speaker 1: there have been many other things that I've I've been

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Speaker 1: doing with my time as well. Was chess something that

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Speaker 1: you were naturally attracted to at a young age? And

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Speaker 1: you know, if so, what did you like about it? Well?

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Speaker 1: I think when when you think about chess, you hear

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Speaker 1: a lot of stories about these kids who are just

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Speaker 1: great from the time that they first started playing. It's

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Speaker 1: just they're they're just amazing and they just win every

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Speaker 1: game the start. UM. Whereas for me, that certainly wasn't

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Speaker 1: the case when I when I started out playing my

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Speaker 1: first tournament, for example, I played four games and I

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Speaker 1: lost every single one, and the first year that I

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Speaker 1: played chess, I did not have many great results, and

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Speaker 1: my parents actually stopped me from playing for about six months.

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Speaker 1: And it was only when I came back that that

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Speaker 1: some something clicked and I just happened to start winning games,

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Speaker 1: um and then from there it just kept, you know,

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Speaker 1: I just kept going forward. But certainly it's not something

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Speaker 1: that I had a natural talent for, and actually most things, um,

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Speaker 1: I don't seem to have a natural talent, which in

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Speaker 1: a way, I think is a good thing, because I

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Speaker 1: think the harder you have to work for something, um,

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Speaker 1: the better you become at it. And when you hit

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Speaker 1: the when you hit the wall, you kind of you

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Speaker 1: you keep trying, you find a way to persevere. Whereas

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Speaker 1: there there have been a lot of other kids who

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Speaker 1: were much better than I was when they were young,

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Speaker 1: but it's just it was a straightforward path, and so

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Speaker 1: they just became really good, really quickly, and then at

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Speaker 1: a certain point they hit the wall and they didn't

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Speaker 1: know how to how to go from there because they

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Speaker 1: just couldn't couldn't improve. And I basically had to learn

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Speaker 1: how to improve from the very start. So I think

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Speaker 1: it's it's uh for me at least, it's been very

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Speaker 1: useful that I don't have a natural talent for a

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Speaker 1: lot of things. Talk about that a little bit more

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Speaker 1: breaking through because I think had something a lot of

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Speaker 1: people experience and all kinds of endeavors where maybe they

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Speaker 1: hit a wall for their talent or they can't figure

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Speaker 1: out how to take it to the next level. What

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Speaker 1: was it? I mean, anyone could say you have to

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Speaker 1: work hard and concentrate, but even that often doesn't do

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Speaker 1: it for a lot of people. So that process of

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Speaker 1: figuring out your problem, solving it, and actually being able

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Speaker 1: to make material gains in your improvement, what did that entail? UM?

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Speaker 1: I mean obviously working hard, I mean it is a

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Speaker 1: big part of it, but I think in general it's

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Speaker 1: just you know, believing, believing in yourself, believing in the

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Speaker 1: process no matter what. UM I think. There there were

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Speaker 1: many times when I stopped, stopped improving, and and one

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Speaker 1: one good example was when I was about seventeen years old.

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Speaker 1: I had become a really strong grand master. I was

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Speaker 1: top one hundred in the world, but I was stuck

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Speaker 1: around fiftie in the world, and for a period of time,

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Speaker 1: I went to college I quit chest for for about

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Speaker 1: seven to eight months, um, and just by being away

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Speaker 1: from it. When I came back, of course I i

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Speaker 1: started working hard again, but I just learned how to

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Speaker 1: enjoy it. And I think it's it's important that besides

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Speaker 1: the hard work that you have to you have to

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Speaker 1: enjoy it. You have to really be into and be

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Speaker 1: passionate about it, because otherwise, uh, otherwise you're just not

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Speaker 1: going to go anywhere. And um, I think I think

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Speaker 1: for me it was it was just learning how to

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Speaker 1: enjoy it again and kind of like remembering what the

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Speaker 1: whole point of it was in the first place, which

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Speaker 1: is you know, it's a game, but you you want

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Speaker 1: you want to have fun, you want to do well,

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Speaker 1: but just enjoying enjoying everything about it. How did you

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Speaker 1: actually turn professional? And how does the business of being

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Speaker 1: a professional chess player actually work? Like? How are you

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Speaker 1: rewarded for playing games? Are there sponsorships? How do you

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Speaker 1: make money? So? Yeah, so chess, um, it's it's one

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Speaker 1: of those fields where it's very much a meritocracy, so

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Speaker 1: it's all based on your results and going professional. It's

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Speaker 1: not like like a major sport, for example, where you

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Speaker 1: know a team drafts you or something along those lines. Basically, uh,

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Speaker 1: you just decide on your own to just devote your

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Speaker 1: life to laying chess and um, and they're there are

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Speaker 1: people in the world who would consider themselves professional chess

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Speaker 1: players who are who are not even grand masters. So um,

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Speaker 1: there there are. There are a lot of different ways

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Speaker 1: of defining a professional. But UM, I would say that

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Speaker 1: probably the top twenty five thirty players in the world

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Speaker 1: make a very good living playing it. Um in large

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Speaker 1: part because the major events with big sponsors, UM, they

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Speaker 1: are invitational only, so you have maybe the top ten,

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Speaker 1: top fifteen players in the world and in some order

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Speaker 1: who are invited and therefore, um, outside of that, it's

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Speaker 1: very hard. Usually you go from tournament to tournament, country

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Speaker 1: to country just trying to earn a living, and it's

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Speaker 1: it's very difficult, which is why there are a lot

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Speaker 1: of people who I think when they're younger, like in

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Speaker 1: their early twenties, they try to play chess professionally, but

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Speaker 1: at some point if they don't break into that top

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Speaker 1: twenty five, top thirty in the world, UM, they they

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Speaker 1: eventually quit. And as far as the rankings go, every game, UM,

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Speaker 1: you you gain points or you lose points based on

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Speaker 1: what you're elo ranking is UM and whether the opponent

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Speaker 1: you're playing as lower ranked or higher ranked than you.

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Speaker 1: So let's saig here a little bit, because in the

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Speaker 1: intro we talked about how chess isn't your only pursuit.

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Speaker 1: It's not your only passion. You're also into trading and

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Speaker 1: options trading. In fact, you tweet, UM. I think you

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Speaker 1: actually tweet a lot. I've seen more tweets I think

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Speaker 1: about options, various options trades that you do than chess,

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Speaker 1: at least as far as I've noticed. Where did that

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Speaker 1: come from? UM, I've I've always had an interest in

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Speaker 1: finance since I was very young. UM. When I was

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Speaker 1: about fourteen or fifteen, my my my mom bought me

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Speaker 1: a couple of books that it was just about general

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Speaker 1: general investing UM, and I happen to read them and

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Speaker 1: I found it found it quite interesting, quite fascinating, UM.

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Speaker 1: How the markets work, UM, you know, in investing in companies,

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Speaker 1: trying trying to make money UM over the long term.

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Speaker 1: And so I kind of started with a very small

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Speaker 1: account UM. And it remained that way for a long time.

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Speaker 1: Primarily I was just trading in equities UM. But about

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Speaker 1: a year and a half ago I started I started

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Speaker 1: getting into options. Originally it was because I started looking

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Speaker 1: into um uh you know, writing calls against stock that

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Speaker 1: I had purchased, and from there kind of I started

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Speaker 1: looking at more options strategies and a few people I know,

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Speaker 1: I'm very fortunate in that regard um have been options

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Speaker 1: traders professionally, and so they they've helped me along the

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Speaker 1: way to you know, kind of figure out how how

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Speaker 1: to learn strategies and go from there. This might be

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Speaker 1: a slightly random question, but is there any direct relation

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Speaker 1: between what you do when it comes to chess and

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Speaker 1: the way you trade options. I think one of the

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Speaker 1: main things is not just options, but markets in general,

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Speaker 1: is um that chess players tend to be very analytical.

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Speaker 1: And I think that when you're looking at the markets,

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Speaker 1: whether it's options, whether it's equities, no matter what the

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Speaker 1: time frame, you have to be analytical. You have to

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Speaker 1: look at it from a perspective of you know, planning ahead,

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Speaker 1: trying to figure out what can go right, what can

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Speaker 1: go wrong? What are what are the very scenarios? UM.

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Speaker 1: It's much with options. You know, if you're if you're

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Speaker 1: doing um, you know, say like a a spread for example,

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Speaker 1: or if you're doing like you know, ratios or just

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Speaker 1: straight you know, calls or puts um trying to figure out,

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Speaker 1: you know, based on certain events, what happens, you know,

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Speaker 1: what's the breaking point assuming it you know, you don't

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Speaker 1: it doesn't go uh, you know, straight up or straight down, um,

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Speaker 1: and going from there. So in general, it's just being

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Speaker 1: very analytical and thinking ahead. That's that's the biggest correlation there.

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Speaker 1: There are, in fact, a lot of people I know

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Speaker 1: growing up who actually have ended up in finance as well,

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Speaker 1: and I don't think that's a coincidence. Now, a lot

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Speaker 1: of people would say that there's a fundamental difference between

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Speaker 1: any sort of trading activity and chess, And of course

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Speaker 1: the sort of big gap is the role of chance

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Speaker 1: and the role of psychology playing a much more outsized

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Speaker 1: role in markets. And so you can calculate something perfectly

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Speaker 1: in chess and then you can execute it, and if

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Speaker 1: it's if you calculated correctly, there's nothing your opponent can do,

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Speaker 1: where as all kinds of things can happen in markets.

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Speaker 1: You could have the perfect strategy and then something could

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Speaker 1: come out of nowhere, or there could be a bubble

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Speaker 1: or some sort of panic that totally destroys the calculations.

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Speaker 1: You know, there's this element that doesn't exist on the chessboard.

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Speaker 1: How hard is it to shift thinking from one realm

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Speaker 1: to another? For me, it's not that hard. And and

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Speaker 1: actually it brings up something else that I'm kind of

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Speaker 1: curious about, which is I've actually thought that trading is

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Speaker 1: a little bit closer to poker in some ways from

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Speaker 1: that standpoint, and um, I'm a little bit curious to

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Speaker 1: kind of try it out because originally I played, I

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Speaker 1: played some poker, and then then then I eventually got

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Speaker 1: into trading. So it's kind of like, because I was

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Speaker 1: a strong chess player, I couldn't handle the chance element

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Speaker 1: in poker, whereas I seem to handle handle the chance

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Speaker 1: and the risk management much better in trading. So I'm

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Speaker 1: kind of curious to say, like, go from this and

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Speaker 1: then play some poker again soon and see if see

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Speaker 1: if it changes, because that is a very big difference.

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Speaker 1: That that is. Uh, the biggest difference is that in chess,

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Speaker 1: if you play well, um, I mean I would say

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Speaker 1: probably at the time something good will happen. You might

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Speaker 1: not win the games. Your opponent plays very well but

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Speaker 1: you aren't gonna lose if you make, say, forty good

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Speaker 1: moves in a row, and so therefore that that is

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Speaker 1: a big difference. But for me, UM, I've I find

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Speaker 1: that I don't have that much difficulty switching and making

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Speaker 1: just straightforward decisions. UM. For me, it seems much more

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Speaker 1: logical actually with trading that it does with chess in

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Speaker 1: a way. Yeah, So I have to say, UM, poker

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Speaker 1: is probably the analogy that we hear the most when

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Speaker 1: it comes to trading. Uh. And you said you played

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Speaker 1: a little bit of poker in the past. Did you

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Speaker 1: find it more difficult than chess or can you kind

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Speaker 1: of walk us through what your performance was like on

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Speaker 1: that front. Yeah, I found a poker quite difficult. And

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Speaker 1: it's for the exact reason that you would think that

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Speaker 1: the trading should be difficult compared to chess, which is that, UM,

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Speaker 1: I felt like I was making a lot of the

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Speaker 1: right decisions more often than not, but the results were

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Speaker 1: were not right. I would end up you know. I mean.

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Speaker 1: The best example is when I played the World Series

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Speaker 1: of Poker or the Main Event and an eleven and UM,

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Speaker 1: in that event, I played tight, I played normal, normal

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Speaker 1: solid poker and then at the end of the second day,

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Speaker 1: and probably like the last twenty minutes, I ended up

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Speaker 1: with this hand where I had kings and the other

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Speaker 1: guy had a pair of eights, and uh, on the

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Speaker 1: flop there was an eight and and I busted out

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Speaker 1: that way despite really not doing anything anything wrong at all. Um.

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Speaker 1: And that's just one example. But I think that at

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Speaker 1: the time I found it very hard because again compared

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Speaker 1: to chess, uh, that there is that luck. You can

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Speaker 1: make all the right decisions, but then somehow you you lose,

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Speaker 1: so you're all the tournament or you you know, you

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Speaker 1: bust in the cash game. It's like it's a very

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Speaker 1: strange feeling. Um from that standpoint. But for for me,

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Speaker 1: I don't know what the difference is, but I have

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Speaker 1: found with trading it's much easier for me than than

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Speaker 1: than it was with poker. But of course I haven't

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Speaker 1: played poker seriously in a few years, and maybe it

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Speaker 1: would be an interesting experience experiment to go back to

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Speaker 1: playing some poker now just to see if see if

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Speaker 1: because I have this experience with trading, whether it's a

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Speaker 1: little bit different. Let's take a right now for a

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Speaker 1: word from our sponsor. But first we want to take

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Speaker 1: and we're back with Karu Nakamura, one of the top

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Speaker 1: chess players in the entire world and therefore obviously also

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Speaker 1: one of the top players in the US. We're also

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Speaker 1: talking about his experience having played poker, his experience and

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Speaker 1: options trading car The obvious sort of follow up is

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Speaker 1: that all of these areas, whether it's poker, trading, chess,

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Speaker 1: the nature of active participation has changed dramatically due to

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Speaker 1: the use of computers artificial technology the top. It was

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Speaker 1: probably a point or no one imagined that the top

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Speaker 1: chess computers could compete with the top humans. Now that

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Speaker 1: question has been changed. Obviously, computers have completely changed the

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Speaker 1: way markets trade or you know, changed the balance of um,

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Speaker 1: how humans interact with it. Computers are getting better and

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Speaker 1: better all the time at a poker as well. Let's

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Speaker 1: talk about this a little bit in chess. How have

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Speaker 1: computers in your view changed the game? How have they

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Speaker 1: changed your preparation? How do you see them having influenced it? Basically,

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Speaker 1: I think the main thing that's changed with computers UM

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Speaker 1: is that at this point in time, because it becomes

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Speaker 1: so strong, you just automatically assume that they're right. So,

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Speaker 1: for example, if you put a certain chess position and

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Speaker 1: you have one of the computer engines analyzing it, you

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Speaker 1: just trust the evaluation what it says. You know, white's

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Speaker 1: whites better, whites winning, or the position is completely even,

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Speaker 1: you trust it without without a second thought. Whereas in

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Speaker 1: in the early days, UM saying the nineties, for example,

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Speaker 1: computers could come up with evaluations were wrong and a

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Speaker 1: human would still be right. A human wouldn't know the

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Speaker 1: understand the position better, whereas now, UM, that's not the case.

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Speaker 1: You just it's just flat out the computer's right, you're wrong.

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Speaker 1: You trust what it says, period, end of story. And

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Speaker 1: so because of that, Um, one of the main things

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Speaker 1: that that has changed is there are a lot of

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Speaker 1: positions which in the past you would have thought one side,

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Speaker 1: the way they're playing is very unusual, it's dubious, it's

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Speaker 1: not quite correct, it's not correct play. And because of computers,

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Speaker 1: if you see a position now where there's some strange moves,

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Speaker 1: the computer says it's completely fine, and you just you

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Speaker 1: believe that it's not. It's not like before, whereas if

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Speaker 1: I played some position against a cast brought, for example,

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Speaker 1: he would just you know, he would just go, that's

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Speaker 1: not good. You don't know what you're doing, your complete

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Speaker 1: pots or whereas now because of the computer. Uh, he

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Speaker 1: couldn't really say that, because the computer just it understands

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Speaker 1: chest so so much better. And basically the main thing

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Speaker 1: is that now almost any position in the game of chess,

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Speaker 1: if you find the right moves, is probably okay unless

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Speaker 1: you're just complete losing. There always are going to be

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Speaker 1: defensive resources. So Joe and I have talked about this

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Speaker 1: a number of times now. But in markets, you know,

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Speaker 1: the rise of machines as well as passive investing has

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Speaker 1: I think it's fair to say, given way to a

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Speaker 1: sense or maybe an existential crisis for fund managers, like

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Speaker 1: there's just a sense that you can never beat the

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Speaker 1: market or the machines now. Um So when it comes

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Speaker 1: to chess and computers, does the rise of computers just

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Speaker 1: make playing chess less fun? Like knowing that a computer

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Speaker 1: is all powerful and always has the right decision, does

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Speaker 1: that make the game less enjoyable for you? It does

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Speaker 1: at times. I think. Another another big thing that's happened

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Speaker 1: in chess as you have these huge databases with millions

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Speaker 1: and millions of games as well, So it's not just

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Speaker 1: the computer analysis, it's also the factory of databases, which

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Speaker 1: means that probably the first like ten to fifteen moves

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Speaker 1: of every single game, both sides are gonna know exactly

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Speaker 1: what they're doing, which means that it's getting deeper and

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Speaker 1: deeper where you reach a new position where it's just

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Speaker 1: the human is just you know, like I'm playing against

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Speaker 1: someone else. It's it's become deeper and deeper in the game.

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Speaker 1: That makes it quite a bit less fun at times,

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Speaker 1: because because it's just like you, you don't play at

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Speaker 1: the start. You you're just repeating whatever the computer says.

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Speaker 1: And then uh, somewhere in the early middle game, somewhere

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Speaker 1: around like the move in the game, you're you're starting

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Speaker 1: to play the game, whereas in the past it might

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Speaker 1: have been like the fifth move or the tenth move.

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Speaker 1: So it just keeps getting deeper and deeper, and that

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Speaker 1: that does take quite a bit of the fun out

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Speaker 1: of it because it makes it a lot harder to win.

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Speaker 1: But at the same time, um, it's also a challenge,

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Speaker 1: and it's it's fun to to try and out prepare

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Speaker 1: your opponents, to try and try and just you know,

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Speaker 1: outsmart them and beat them, so it cuts both ways.

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Speaker 1: So just to clarify what you're saying is you'll play

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Speaker 1: games with someone and the first fifteen moves or more

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Speaker 1: will be identical to numerous games that have been played,

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Speaker 1: and only deeper than that will one of you have

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Speaker 1: played a new move that hadn't been played before. So

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Speaker 1: in theory, you know, as as a trader, in theory,

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Speaker 1: you shouldn't be able to make money trading. I mean

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Speaker 1: there's this whole school of thought essentially efficient markets that

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Speaker 1: someone trying to do active stock selection or active any

431
00:22:25,720 --> 00:22:29,000
Speaker 1: sort of active trading shouldn't really be doable, that no

432
00:22:29,040 --> 00:22:32,560
Speaker 1: one has any more information or insight or anything, and

433
00:22:32,600 --> 00:22:34,800
Speaker 1: that if you do make money over a period of

434
00:22:34,840 --> 00:22:38,120
Speaker 1: time that it might be luck something like that, And

435
00:22:38,280 --> 00:22:44,560
Speaker 1: with computers being able to research and analyze probabilities so aggressively,

436
00:22:44,600 --> 00:22:47,440
Speaker 1: in theory, it should be even harder than it has

437
00:22:47,480 --> 00:22:51,000
Speaker 1: been in the past to exploit inefficiencies. Why why is

438
00:22:51,000 --> 00:22:53,840
Speaker 1: that an endeavor that you think you can make money in? Um, Well,

439
00:22:53,880 --> 00:22:55,600
Speaker 1: I mean, to be be fair, I do them next.

440
00:22:55,640 --> 00:22:58,200
Speaker 1: So it's not just I'm not a hundred percent trading

441
00:22:58,240 --> 00:23:01,400
Speaker 1: I also do do pass that in a trading component.

442
00:23:01,880 --> 00:23:04,919
Speaker 1: Why do you feel like it's an area that is

443
00:23:04,920 --> 00:23:09,280
Speaker 1: worth your time and worth trying to beat the system?

444
00:23:09,760 --> 00:23:12,760
Speaker 1: I mean I I I enjoy a challenge. I mean,

445
00:23:12,800 --> 00:23:15,160
Speaker 1: to to be frank, I enjoy a challenge. On It's

446
00:23:15,200 --> 00:23:17,400
Speaker 1: quite difficult. It takes a lot of time, a lot

447
00:23:17,440 --> 00:23:20,399
Speaker 1: of a lot of work, and even then you still

448
00:23:20,440 --> 00:23:22,919
Speaker 1: aren't always going to be right. Um. I mean I

449
00:23:22,960 --> 00:23:26,000
Speaker 1: wasn't right this morning on caterpillar for example. But um,

450
00:23:26,560 --> 00:23:29,840
Speaker 1: but but in general, uh, you know, I think, um,

451
00:23:30,040 --> 00:23:33,000
Speaker 1: I really, I really enjoy the process. So maybe over

452
00:23:33,040 --> 00:23:35,200
Speaker 1: the long run it's not going to pan out, But

453
00:23:35,280 --> 00:23:37,879
Speaker 1: but I enjoy the process, and I've been successful in

454
00:23:37,920 --> 00:23:43,080
Speaker 1: the past year. Wait, what was your caterpillar trade? Oh?

455
00:23:43,119 --> 00:23:46,240
Speaker 1: I was for some reason, I was expecting, uh, expecting

456
00:23:46,240 --> 00:23:48,800
Speaker 1: them to go down. I did a put spread on them.

457
00:23:48,840 --> 00:23:51,800
Speaker 1: I was expecting, uh, because of the commodity prices for

458
00:23:51,840 --> 00:23:55,760
Speaker 1: them to not uh not report and the and the

459
00:23:55,800 --> 00:23:57,800
Speaker 1: stock for what it's worth is up about six and

460
00:23:57,840 --> 00:24:01,199
Speaker 1: a half percent. You know, I'm letting our viewers now,

461
00:24:01,240 --> 00:24:03,560
Speaker 1: I know you you probably are feeling the pain of

462
00:24:03,600 --> 00:24:05,520
Speaker 1: that trade today. I just want to you know, but

463
00:24:05,560 --> 00:24:08,240
Speaker 1: another thing also, uh, you know, like there's a there's

464
00:24:08,240 --> 00:24:10,439
Speaker 1: another trade, for example, which I'm not doing well in,

465
00:24:10,440 --> 00:24:14,159
Speaker 1: which is Tesla. But I think one thing that that

466
00:24:14,240 --> 00:24:16,280
Speaker 1: I also enjoy is because I'm just a you know,

467
00:24:16,440 --> 00:24:21,080
Speaker 1: a retail UH investor, is that through traveling around a lot,

468
00:24:21,080 --> 00:24:23,880
Speaker 1: I get to have a different perspective on certain things.

469
00:24:23,880 --> 00:24:27,600
Speaker 1: You know. It's like, um, everyone says Tesla's UH is

470
00:24:27,640 --> 00:24:30,479
Speaker 1: overvalued right now, and I think I think it is.

471
00:24:30,520 --> 00:24:32,560
Speaker 1: But for example, I was in Italy last week, and

472
00:24:32,600 --> 00:24:34,720
Speaker 1: I was in southern Italy, no less, and I was

473
00:24:34,720 --> 00:24:37,600
Speaker 1: really surprised to see that on the weekend down town

474
00:24:37,640 --> 00:24:40,159
Speaker 1: there was there was some some sort of exhibition. It

475
00:24:40,240 --> 00:24:43,000
Speaker 1: was for electric cars. And I think that when you

476
00:24:43,040 --> 00:24:45,800
Speaker 1: see things like that, it also it also it changes

477
00:24:45,840 --> 00:24:48,879
Speaker 1: the perspective a little bit. You don't have the standard

478
00:24:48,960 --> 00:24:50,760
Speaker 1: standard view like I would if I'm just here when

479
00:24:50,800 --> 00:24:53,080
Speaker 1: I actually get out and I see certain things, um

480
00:24:53,119 --> 00:24:56,159
Speaker 1: like that or unfortunately things like in retail where you

481
00:24:56,200 --> 00:24:59,240
Speaker 1: go to malls and you see, uh, nobody, nobody there.

482
00:24:59,280 --> 00:25:03,000
Speaker 1: I mean, I think I also find that to be

483
00:25:03,440 --> 00:25:05,920
Speaker 1: one of the advantages as well of just being being

484
00:25:05,960 --> 00:25:07,960
Speaker 1: who I am, traveling around a lot, I get a

485
00:25:07,960 --> 00:25:10,040
Speaker 1: lot of different perspectives and I think that does help

486
00:25:10,080 --> 00:25:12,840
Speaker 1: me with trading. Tracy and I were in a Hong

487
00:25:12,920 --> 00:25:15,840
Speaker 1: Kong a little over a year ago, and I remember

488
00:25:15,880 --> 00:25:18,520
Speaker 1: being pretty stunned by how many Tesla's were on the road.

489
00:25:18,560 --> 00:25:20,200
Speaker 1: So I know what you mean in terms of getting

490
00:25:20,200 --> 00:25:22,720
Speaker 1: out and being uh, you know, having feeling like you

491
00:25:22,720 --> 00:25:25,280
Speaker 1: have a new piece of information that you didn't have before. Yeah,

492
00:25:25,320 --> 00:25:27,400
Speaker 1: I mean, because I think it's like I've traveled around

493
00:25:27,400 --> 00:25:29,240
Speaker 1: the US a lot, but I mean, if you go

494
00:25:29,359 --> 00:25:31,399
Speaker 1: to somewhere in the middle of the country, I mean,

495
00:25:31,480 --> 00:25:33,120
Speaker 1: one place where I've spent a fair amount of time

496
00:25:33,119 --> 00:25:35,720
Speaker 1: would be St. Louis, I mean, you don't see Tesla's.

497
00:25:35,760 --> 00:25:37,400
Speaker 1: I don't think I've seen a single Tesla when I've

498
00:25:37,400 --> 00:25:39,480
Speaker 1: been out there. I've seen Tesla's, you know, I'll see

499
00:25:39,520 --> 00:25:41,760
Speaker 1: them in New York or San Francisco or Florida, but

500
00:25:42,000 --> 00:25:44,280
Speaker 1: never in the middle of the country, despite traveling around

501
00:25:44,320 --> 00:25:47,160
Speaker 1: a lot. So it's I think, uh, it's it's quite

502
00:25:47,280 --> 00:25:50,800
Speaker 1: nice to have that extra perspective, that extra viewpoint for um,

503
00:25:50,880 --> 00:25:54,280
Speaker 1: for for making possible trades. Yeah, I think research and

504
00:25:54,440 --> 00:25:59,720
Speaker 1: data is clearly of growing importance for trading and investing.

505
00:26:00,040 --> 00:26:02,800
Speaker 1: And Joe and I we've talked about this on the

506
00:26:02,800 --> 00:26:05,080
Speaker 1: show before as well. But this idea that we have

507
00:26:05,640 --> 00:26:09,320
Speaker 1: news sources of proprietary data now like you know, you

508
00:26:09,359 --> 00:26:12,080
Speaker 1: have hedge funds that have access to satellite data that

509
00:26:12,240 --> 00:26:16,760
Speaker 1: shows which factories in China are manufacturing a lot, and

510
00:26:16,880 --> 00:26:20,680
Speaker 1: that kind of throws up some questions about data inequality

511
00:26:20,880 --> 00:26:24,880
Speaker 1: in the market. I'm just wondering when it comes to chess.

512
00:26:25,600 --> 00:26:27,880
Speaker 1: You mentioned the idea that you know, you have these

513
00:26:27,920 --> 00:26:32,199
Speaker 1: big databases that show all the moves in thousands and

514
00:26:32,280 --> 00:26:36,119
Speaker 1: thousands of various games of chess. Do certain people have

515
00:26:36,440 --> 00:26:42,119
Speaker 1: better databases or better computational power when it comes to that, Uh,

516
00:26:42,200 --> 00:26:45,480
Speaker 1: In terms of database is known. In terms of computational power,

517
00:26:45,680 --> 00:26:48,880
Speaker 1: certainly there there is a difference because some people will

518
00:26:48,920 --> 00:26:51,520
Speaker 1: spend some extra money and go out and buy supercomputers

519
00:26:51,640 --> 00:26:55,000
Speaker 1: or just have clusters of various computers altogether. Um, And

520
00:26:55,280 --> 00:26:57,400
Speaker 1: I think when I say that not, I would say

521
00:26:57,440 --> 00:27:00,360
Speaker 1: not all the top players have that. So there there

522
00:27:00,400 --> 00:27:05,040
Speaker 1: are some computational advantages if if you, you know, spend

523
00:27:05,040 --> 00:27:10,240
Speaker 1: the money. Obviously, this whole debate about humans versus computers

524
00:27:10,400 --> 00:27:15,000
Speaker 1: or computers augmenting the ability of humans is it has

525
00:27:15,119 --> 00:27:19,080
Speaker 1: much broader macro implications for the economy. This is an

526
00:27:19,200 --> 00:27:22,520
Speaker 1: endless source of debate about whether humans will have any

527
00:27:22,600 --> 00:27:24,960
Speaker 1: jobs to do, or whether there will be a few

528
00:27:24,960 --> 00:27:27,639
Speaker 1: people with jobs programming computers and the rest of us

529
00:27:27,640 --> 00:27:30,640
Speaker 1: will be unemployed and living on a drip of soilent

530
00:27:30,920 --> 00:27:34,560
Speaker 1: and basic income check or something like that. I'm curious,

531
00:27:34,840 --> 00:27:39,440
Speaker 1: you know, you whether through the chess experience, because I

532
00:27:39,480 --> 00:27:42,920
Speaker 1: think in chess there's two things. There's man versus computer chess,

533
00:27:42,920 --> 00:27:46,919
Speaker 1: but also chess or computers. As you've said, aiding your play,

534
00:27:47,280 --> 00:27:50,720
Speaker 1: whether you have some perspective on where you see sort

535
00:27:50,760 --> 00:27:54,359
Speaker 1: of the future of work and the future of human ingenuity. Well,

536
00:27:54,359 --> 00:27:57,200
Speaker 1: the first thing that I'm gonna say is actually on Chustell.

537
00:27:57,359 --> 00:28:00,320
Speaker 1: One of the really weird things is that when humans

538
00:28:00,359 --> 00:28:02,800
Speaker 1: play against humans most games and in a draw, it's

539
00:28:02,840 --> 00:28:05,959
Speaker 1: something like six of games and in a draw. But

540
00:28:06,359 --> 00:28:09,560
Speaker 1: when you have computer programs playing against each other, there

541
00:28:09,560 --> 00:28:11,960
Speaker 1: are a lot less draws. Most of the games are decisive,

542
00:28:12,000 --> 00:28:15,680
Speaker 1: which should not make any sense whatsoever, because you figure

543
00:28:15,720 --> 00:28:18,440
Speaker 1: the computers. Again, what is the difference between one computer

544
00:28:18,480 --> 00:28:21,800
Speaker 1: and another computer? Um, And I'm not a computer guy,

545
00:28:21,840 --> 00:28:24,240
Speaker 1: so I can't really explain it. But It's kind of

546
00:28:24,240 --> 00:28:26,160
Speaker 1: amazing when you think about it. That like when two

547
00:28:26,200 --> 00:28:28,800
Speaker 1: computers that should be the same same computational power and

548
00:28:28,840 --> 00:28:32,639
Speaker 1: ability they play, one computer wins, which I think bodes

549
00:28:32,760 --> 00:28:35,240
Speaker 1: very well for the future of chess UM in general.

550
00:28:35,440 --> 00:28:39,400
Speaker 1: But us as far as the world UM, I don't know.

551
00:28:39,440 --> 00:28:43,160
Speaker 1: It's it's hard to say because UM. I actually was

552
00:28:43,160 --> 00:28:45,640
Speaker 1: was down in Florida recently. UM and there's this this

553
00:28:45,720 --> 00:28:49,120
Speaker 1: I T company which builds UM builds various platforms through

554
00:28:49,160 --> 00:28:54,560
Speaker 1: AI UM and using bit bit chain technology. And uh,

555
00:28:54,640 --> 00:28:57,800
Speaker 1: certainly jobs are going to disappear with AI going forward.

556
00:28:58,000 --> 00:29:00,400
Speaker 1: But at the same time, you also need people to

557
00:29:00,440 --> 00:29:04,360
Speaker 1: train and build build these platforms and and and everything.

558
00:29:04,480 --> 00:29:07,000
Speaker 1: And uh, like when I was in Florida, there were

559
00:29:07,000 --> 00:29:08,600
Speaker 1: a lot of a lot of kids coming straight out

560
00:29:08,640 --> 00:29:11,440
Speaker 1: of college who are doing this. So I think jobs

561
00:29:11,440 --> 00:29:13,880
Speaker 1: are certainly going to disappear. I don't know if it's

562
00:29:14,160 --> 00:29:16,160
Speaker 1: the end of the world, but I mean, I think

563
00:29:16,640 --> 00:29:19,080
Speaker 1: going forward, there will probably have to be some sort

564
00:29:19,120 --> 00:29:23,360
Speaker 1: of a shift in terms of jobs, job skills and

565
00:29:23,200 --> 00:29:26,320
Speaker 1: and all that, because certain jobs like I think being

566
00:29:26,320 --> 00:29:29,440
Speaker 1: a mechanic for example, UM are just gonna simply vanish

567
00:29:29,760 --> 00:29:32,600
Speaker 1: over the next ten to fifteen years. It's really odd

568
00:29:32,640 --> 00:29:37,280
Speaker 1: that games played between computers are more decisive than games

569
00:29:37,280 --> 00:29:39,880
Speaker 1: played between humans. But on some level, I guess if

570
00:29:39,920 --> 00:29:43,560
Speaker 1: you're a chess spectator, that would be more desirable, right, Like,

571
00:29:44,000 --> 00:29:45,960
Speaker 1: would you ever get a situation where we all go

572
00:29:46,040 --> 00:29:48,680
Speaker 1: to chess tournaments and we just watch one robot play

573
00:29:48,720 --> 00:29:53,520
Speaker 1: another robot? Um? I I don't know. Maybe if that happens,

574
00:29:53,520 --> 00:29:56,280
Speaker 1: I'm not gonna be playing chess a better better just

575
00:29:56,400 --> 00:30:00,280
Speaker 1: to do something else all the time. Um, But yeah,

576
00:30:00,320 --> 00:30:02,160
Speaker 1: I think you know that that that's actually a big,

577
00:30:02,200 --> 00:30:05,120
Speaker 1: big problem with chess, for example, is because the the

578
00:30:05,240 --> 00:30:07,600
Speaker 1: database has become so so good in the players have

579
00:30:07,640 --> 00:30:10,040
Speaker 1: also just become so good through learning from the computers.

580
00:30:10,280 --> 00:30:13,280
Speaker 1: The margin of difference is so small that most games

581
00:30:13,280 --> 00:30:16,040
Speaker 1: do end and draws, and for spectators that's not exciting

582
00:30:16,080 --> 00:30:18,920
Speaker 1: at all. Um. So there have to be some changes

583
00:30:18,960 --> 00:30:20,680
Speaker 1: in chess, and I think it's a it's if you

584
00:30:20,720 --> 00:30:22,680
Speaker 1: look at it, that's, you know, a microcosm of the

585
00:30:22,920 --> 00:30:25,720
Speaker 1: bigger picture as well. Do you ever worry that? Like,

586
00:30:25,880 --> 00:30:28,320
Speaker 1: so there's one At one point when people thought the

587
00:30:28,400 --> 00:30:31,680
Speaker 1: chess that computers would never be able to compete with humans.

588
00:30:32,040 --> 00:30:35,280
Speaker 1: There was obviously this view that there was something in chess,

589
00:30:35,360 --> 00:30:37,560
Speaker 1: or there's something in poker or any of these other

590
00:30:37,640 --> 00:30:42,000
Speaker 1: games that required an intuitive, creative element that could never

591
00:30:42,040 --> 00:30:45,480
Speaker 1: be programmed into the computer. And then of course computers

592
00:30:45,520 --> 00:30:48,239
Speaker 1: don't have any creativity or intuition, but they do have

593
00:30:48,360 --> 00:30:51,640
Speaker 1: raw calculation power, and it turns out that that's enough

594
00:30:51,680 --> 00:30:54,040
Speaker 1: to make them really good at chess. Do you ever

595
00:30:54,080 --> 00:30:57,200
Speaker 1: worry there's like something unromantic about it, in the sense

596
00:30:57,280 --> 00:31:00,880
Speaker 1: that it turns out that sort of intuition and creativity

597
00:31:01,000 --> 00:31:03,400
Speaker 1: and these sort of skills that we think of as

598
00:31:03,560 --> 00:31:07,600
Speaker 1: innately human maybe aren't worth very much. Yeah. I think

599
00:31:07,680 --> 00:31:10,040
Speaker 1: it's it's a little bit depressing, you know. For me actually,

600
00:31:10,360 --> 00:31:12,640
Speaker 1: like Caspar Lost a Deep Blue, it was already I

601
00:31:12,640 --> 00:31:14,880
Speaker 1: think probably twenty years ago now, so I think there's

602
00:31:14,960 --> 00:31:17,160
Speaker 1: ninety six, wasn't it. It was? Yeah, I remember being

603
00:31:17,160 --> 00:31:19,400
Speaker 1: there and like at that point, like we had started

604
00:31:19,440 --> 00:31:21,160
Speaker 1: to accept the computers are better, and like for me

605
00:31:21,240 --> 00:31:24,360
Speaker 1: it was it was so bizarre. Um. Actually, another speaking

606
00:31:24,400 --> 00:31:27,640
Speaker 1: of another game was go where Uh it was it's,

607
00:31:27,680 --> 00:31:30,440
Speaker 1: you know, it's more difficult than chess. You can't compute it.

608
00:31:30,840 --> 00:31:33,720
Speaker 1: And then then like you have this computer just destroy

609
00:31:33,880 --> 00:31:36,640
Speaker 1: this world champion. It was just like this whole weird

610
00:31:36,680 --> 00:31:40,320
Speaker 1: flashback to to what happened like twenty years ago. Um

611
00:31:40,960 --> 00:31:44,600
Speaker 1: so I think in a way it's sad, but um

612
00:31:44,640 --> 00:31:48,640
Speaker 1: if we can still learn from computers and uh become

613
00:31:48,720 --> 00:31:51,200
Speaker 1: better at various skills, and I don't think it's all

614
00:31:51,240 --> 00:31:54,880
Speaker 1: all for not. I have one question, which is, if

615
00:31:54,960 --> 00:31:58,760
Speaker 1: you are a human, what's your what's your number one

616
00:31:59,160 --> 00:32:02,280
Speaker 1: tip for someone with a very basic knowledge of chess

617
00:32:02,360 --> 00:32:06,160
Speaker 1: who is very tired of constantly losing. Just pick up

618
00:32:06,160 --> 00:32:09,240
Speaker 1: a book or go online and find some some some

619
00:32:09,240 --> 00:32:11,640
Speaker 1: some quick tactics, like some tactics in the first like

620
00:32:11,680 --> 00:32:15,160
Speaker 1: five moves of the games, some basic tactical sequences, just tricks,

621
00:32:15,280 --> 00:32:19,200
Speaker 1: play for trucks, all right, I have to I have

622
00:32:19,320 --> 00:32:22,240
Speaker 1: two final questions. Uh. The first one is very closely

623
00:32:22,280 --> 00:32:26,160
Speaker 1: related to tracies. I have a thirteen month old daughter,

624
00:32:26,440 --> 00:32:29,000
Speaker 1: and I want her to be the next car Nakamura.

625
00:32:29,120 --> 00:32:32,280
Speaker 1: Of course, what's the how would you you know? I'm

626
00:32:32,320 --> 00:32:34,440
Speaker 1: not just looking for technical tricks, but I want to

627
00:32:34,840 --> 00:32:37,200
Speaker 1: turn her into a great chess player one day if

628
00:32:37,240 --> 00:32:39,360
Speaker 1: she wants to be. What's the best way to uh

629
00:32:39,480 --> 00:32:43,560
Speaker 1: start training her? Well, nowadays it's it's so easy, um.

630
00:32:43,600 --> 00:32:46,520
Speaker 1: I mean just going online and on any of the

631
00:32:46,600 --> 00:32:50,560
Speaker 1: various chess sites, just just studying tactical sequences um or

632
00:32:50,600 --> 00:32:52,880
Speaker 1: just looking at various games. I mean, there's so much

633
00:32:52,920 --> 00:32:55,320
Speaker 1: information out there that it's not hard to get started,

634
00:32:55,480 --> 00:32:57,760
Speaker 1: um by by any means. As I said before, there

635
00:32:57,760 --> 00:32:59,880
Speaker 1: are a few a few different chess sites like chest

636
00:33:00,000 --> 00:33:01,800
Speaker 1: dot com. I think it is probably the main one

637
00:33:01,840 --> 00:33:04,320
Speaker 1: where you can just go get a free account and uh,

638
00:33:04,440 --> 00:33:06,640
Speaker 1: look at games or do some tactics and and that's

639
00:33:06,680 --> 00:33:09,000
Speaker 1: the easiest way to get started. All right, I'll sit

640
00:33:09,040 --> 00:33:11,200
Speaker 1: here in front of the computer today and this show

641
00:33:11,560 --> 00:33:14,040
Speaker 1: you know longer with the chess dot com account by

642
00:33:14,080 --> 00:33:17,520
Speaker 1: your one year older book. Yeah, I was gonna say that.

643
00:33:17,640 --> 00:33:19,560
Speaker 1: That's another thing, is like when I became grand master

644
00:33:19,640 --> 00:33:22,200
Speaker 1: at fifteen, that was a big deal. That was quite

645
00:33:22,280 --> 00:33:24,840
Speaker 1: young at the time. But nowadays they're there are kids

646
00:33:24,840 --> 00:33:27,800
Speaker 1: who are becoming grand masters at uh twelve or thirteen.

647
00:33:28,160 --> 00:33:30,800
Speaker 1: So it just keeps getting younger and younger and uh

648
00:33:30,920 --> 00:33:33,200
Speaker 1: and and again who knows where that's going to lead

649
00:33:33,200 --> 00:33:35,800
Speaker 1: as well? I have one other question. So I used

650
00:33:35,840 --> 00:33:38,120
Speaker 1: to be I'm not very good at chess, but I

651
00:33:38,160 --> 00:33:39,880
Speaker 1: used to play a lot when I was in college,

652
00:33:40,440 --> 00:33:43,200
Speaker 1: and that was I graduate in two thousand and two,

653
00:33:43,320 --> 00:33:46,560
Speaker 1: so you're probably like early teens at that point. And

654
00:33:46,600 --> 00:33:48,360
Speaker 1: my friends and I who played, we were aware of you.

655
00:33:48,520 --> 00:33:51,360
Speaker 1: So it's very exciting because we knew about this young guy,

656
00:33:51,480 --> 00:33:53,840
Speaker 1: Kara Nakamura was becoming one of the best in the world.

657
00:33:53,920 --> 00:33:56,200
Speaker 1: And I yesterday I texted a friend and I said, oh,

658
00:33:56,240 --> 00:33:59,080
Speaker 1: we have Nackamara on the podcast, and he was really excited.

659
00:33:59,160 --> 00:34:01,120
Speaker 1: But he wants to know you have a prediction for

660
00:34:01,160 --> 00:34:04,480
Speaker 1: the two thousand eighteen Chess Olympiad because I know the

661
00:34:04,600 --> 00:34:08,600
Speaker 1: US one in its pretty extraordinary. That was in Buku

662
00:34:08,680 --> 00:34:13,239
Speaker 1: and Azerbaijan. How's it looking for we? We should be

663
00:34:13,280 --> 00:34:16,960
Speaker 1: the favorites. Uh, I mean, the one thing I will

664
00:34:16,960 --> 00:34:19,120
Speaker 1: say is it felt way too easy winning this last

665
00:34:19,160 --> 00:34:21,960
Speaker 1: time in Azerbaijan, So there's there's some kind of, uh,

666
00:34:22,880 --> 00:34:25,080
Speaker 1: there's this weird feeling that like somehow if we do

667
00:34:25,160 --> 00:34:26,839
Speaker 1: when it's not going to be as easy as it was.

668
00:34:26,920 --> 00:34:30,400
Speaker 1: So I think we're we're probably the favorites. But uh,

669
00:34:30,520 --> 00:34:32,239
Speaker 1: it's also Russia, I would say, one of the two,

670
00:34:32,480 --> 00:34:36,960
Speaker 1: one of the classic the classic countries, and again exactly alright,

671
00:34:37,040 --> 00:34:39,960
Speaker 1: Hakaro knackermar thank you so much. Been a great pleasure

672
00:34:40,040 --> 00:34:41,920
Speaker 1: to chat with you, and thanks for coming on the

673
00:34:41,920 --> 00:34:55,600
Speaker 1: Odd Lots podcast. Problem. So, Tracy, I'm not gonna lie.

674
00:34:55,719 --> 00:34:58,480
Speaker 1: I really enjoyed that it totally lived up to the hype,

675
00:34:58,640 --> 00:35:01,640
Speaker 1: as I said at the end, and I've been following

676
00:35:01,680 --> 00:35:04,000
Speaker 1: her Car's I have have been aware of her Car's career

677
00:35:04,080 --> 00:35:06,840
Speaker 1: for like fifteen years now, and so it's pretty thrilling

678
00:35:06,880 --> 00:35:09,839
Speaker 1: to get to chat with him. You know, I'm just

679
00:35:10,000 --> 00:35:13,319
Speaker 1: reading the Wikipedia entry again and it sends it says

680
00:35:13,400 --> 00:35:16,640
Speaker 1: that he is sometimes nicknamed the h bomb because of

681
00:35:16,719 --> 00:35:20,800
Speaker 1: his explosive style of playing. Do you know what that means?

682
00:35:22,040 --> 00:35:27,520
Speaker 1: I imagine it's aggressively, aggressively tactical and stuff. But he's

683
00:35:27,520 --> 00:35:29,520
Speaker 1: still here real quickly, her Carl, what what does that?

684
00:35:29,760 --> 00:35:32,319
Speaker 1: Where's that nickname come from? Yeah, it's it's just it's

685
00:35:32,320 --> 00:35:34,440
Speaker 1: just being a very aggressive player, trying to win every

686
00:35:34,480 --> 00:35:36,319
Speaker 1: game at all costs, from from when I was a

687
00:35:36,320 --> 00:35:39,120
Speaker 1: bit younger. Well, I'm lead up. We got to get

688
00:35:39,160 --> 00:35:41,799
Speaker 1: an answer to that, no, And I really sort of

689
00:35:41,920 --> 00:35:45,440
Speaker 1: appreciated the perspective. I mean, you know, we sort of

690
00:35:45,640 --> 00:35:50,880
Speaker 1: talk about this eternal sort of epic battle of humans

691
00:35:51,040 --> 00:35:54,040
Speaker 1: versus computers and robots, and we sort of act like it's, uh,

692
00:35:54,560 --> 00:35:57,520
Speaker 1: like there's this zero sum element and one is going

693
00:35:57,560 --> 00:35:59,680
Speaker 1: to win out. But I like this idea that, yeah,

694
00:35:59,680 --> 00:36:02,160
Speaker 1: there is of that, but then there's also this way

695
00:36:02,200 --> 00:36:06,000
Speaker 1: in which computers make humans way better at doing human tasks,

696
00:36:06,080 --> 00:36:09,719
Speaker 1: like being able to play a much more advanced game

697
00:36:09,719 --> 00:36:12,200
Speaker 1: of chess, or being able to learn chess at a

698
00:36:12,280 --> 00:36:15,280
Speaker 1: much earlier age, and now they're all these young grand masters.

699
00:36:15,320 --> 00:36:18,759
Speaker 1: So kind of gave me an optimistic, kind of optimistic

700
00:36:18,800 --> 00:36:23,279
Speaker 1: on that. Really I don't know, so, I mean I

701
00:36:23,640 --> 00:36:26,040
Speaker 1: just kind of see like chess in some respects is

702
00:36:26,120 --> 00:36:30,399
Speaker 1: kind of at the forefront of the man versus machine argument, right,

703
00:36:30,440 --> 00:36:34,840
Speaker 1: and like, yes, yes, people can use computers to augment

704
00:36:35,160 --> 00:36:40,480
Speaker 1: their own performance, but ultimately, when humans are pitted against computers,

705
00:36:40,560 --> 00:36:44,160
Speaker 1: the computers are the ones that went out. And I mean,

706
00:36:44,160 --> 00:36:46,799
Speaker 1: just going back to my experience, I get really frustrated

707
00:36:46,840 --> 00:36:49,400
Speaker 1: with the game because I never ever win. And so

708
00:36:49,480 --> 00:36:52,720
Speaker 1: I wonder, if you're a human constantly competing against a machine,

709
00:36:52,840 --> 00:36:56,560
Speaker 1: if eventually you just give up, you know, you should

710
00:36:57,200 --> 00:37:01,239
Speaker 1: really buy a booker. I'm sure you're not that you

711
00:37:01,239 --> 00:37:05,080
Speaker 1: could find some local grandmasters to teach you probably you know,

712
00:37:05,560 --> 00:37:10,120
Speaker 1: you know, actually try to improve it the game, yes, okay,

713
00:37:10,200 --> 00:37:12,759
Speaker 1: I I will try maybe, And I will say one

714
00:37:12,800 --> 00:37:15,400
Speaker 1: other thing is frustrating, is it It might be to

715
00:37:15,480 --> 00:37:18,719
Speaker 1: lose every game. There's nothing more boring than winning every game.

716
00:37:18,760 --> 00:37:21,600
Speaker 1: So you're actually, I think you're getting on the You're

717
00:37:21,680 --> 00:37:24,360
Speaker 1: you're on the right side of the equation. Is that

718
00:37:24,440 --> 00:37:27,320
Speaker 1: how the computers feel? You think they're like, oh my god,

719
00:37:27,360 --> 00:37:32,080
Speaker 1: I've just won another game. They probably are on that note.

720
00:37:32,400 --> 00:37:35,920
Speaker 1: This has been another episode of the Odd Lots podcast.

721
00:37:36,000 --> 00:37:38,520
Speaker 1: I'm Joe Wisn't Thal. You can follow me on Twitter

722
00:37:38,640 --> 00:37:41,600
Speaker 1: at the Stalwart, and I'm Tracy Alloway. I'm on Twitter

723
00:37:41,680 --> 00:37:45,240
Speaker 1: at Tracy Alloway. And you can follow Hakaru on Twitter

724
00:37:45,560 --> 00:37:48,200
Speaker 1: at GM Hakaru, where you can see all of his

725
00:37:48,360 --> 00:37:50,560
Speaker 1: uh later. I don't know about all of them, but

726
00:37:50,680 --> 00:37:54,480
Speaker 1: his chest exploits and many of his options trades, including

727
00:37:54,600 --> 00:37:58,520
Speaker 1: a recent trade on Young China that apparently worked out

728
00:37:58,560 --> 00:38:13,799
Speaker 1: pretty well. So thanks for listening. Put knowledge to work

729
00:38:13,840 --> 00:38:17,040
Speaker 1: and grow your business with c i T. From transportation

730
00:38:17,200 --> 00:38:21,320
Speaker 1: to healthcare to manufacturing. C i T offers commercial lending, leasing,

731
00:38:21,400 --> 00:38:25,080
Speaker 1: and treasury management services for small and middle market businesses

732
00:38:25,280 --> 00:38:27,920
Speaker 1: learn more at c I T dot com put knowledge

733
00:38:27,960 --> 00:38:28,400
Speaker 1: to work