Speaker 1: Hello, and welcome to another episode of The Thoughts Podcast.
Speaker 1: I'm Tracy Alloway and I'm Joe Wisenthal. So, Joe, I
Speaker 1: think we have to come clean about this particular episode.
Speaker 1: We do have to come clean before we get into
Speaker 1: the discussion. There's a big, what pound gorilla in the
Speaker 1: room that we have to address. Yeah, So the gorilla
Speaker 1: is that last week we recorded an amazing podcast all
Speaker 1: about technology and its role in finance and the broader world,
Speaker 1: and uh, then we were hit by our own technological snaffoo.
Speaker 1: It's right, So we recorded the greatest episode in the
Speaker 1: history of the entire podcast. It was amazing, one of
Speaker 1: a kind, the kind of conversation that you dream of,
Speaker 1: and then unfortunately the audio was bad and the entire
Speaker 1: thing was ruined. Yes, something happened with the computer. The
Speaker 1: computer said no, we're still trying to figure out what
Speaker 1: the exact issue was. But we learned an important lesson
Speaker 1: about the pitfalls of technology, which gives us an excuse
Speaker 1: to have our guest come on and try to have
Speaker 1: the conversation all over again. So here we go. And
Speaker 1: since this episode is kind of about the relationship between
Speaker 1: technology and finance, we can at least pretend that there's
Speaker 1: some lesson here and what happened to us that's relevant
Speaker 1: to the episode. But really, like as we, I wasn't
Speaker 1: really exaggerated when I said it was a great conversation,
Speaker 1: and it would have been so hard to It would
Speaker 1: have been very hard to try to replicate that or
Speaker 1: to try to pretend we were just doing it again
Speaker 1: for the first time. So just in the spirit of
Speaker 1: honesty and recreating spontaneity, we wanted to get it out
Speaker 1: of the way and be honest with our listeners that
Speaker 1: this is a take two of that conversation. Who knows,
Speaker 1: maybe it will be even better the second time around.
Speaker 1: The important thing is we learned a lesson about backup
Speaker 1: systems and tech. All right, So so here goes, well,
Speaker 1: we can't yes, but we can't now pretend to do
Speaker 1: our stick where we don't know. We're like, what are
Speaker 1: we going to talk about this time? Is that would
Speaker 1: really be contrived? After that? No, No, I wasn't going
Speaker 1: to Okay, I'm going to just bring our guests on.
Speaker 1: Our guest for today for the second time is Alfred Specter.
Speaker 1: He's the chief technology officer of Two Sigma. He's also
Speaker 1: a former engineer at Google, and he was also at
Speaker 1: IBM for a very long time. He's an extremely well
Speaker 1: known name in the realm of technology and also in
Speaker 1: quant driven finance, and he's been nice enough to join
Speaker 1: us yet again on odd lots. So thank you, Alfred,
Speaker 1: really appreciate it. It's my pleasure to be here. And
Speaker 1: by the way, the probability that there's a failure and
Speaker 1: a technology system is somehow proportional to the seniority of
Speaker 1: the person that's involved, So if we ever give a
Speaker 1: demo to like a really seenior person, it's much more
Speaker 1: likely to fail. I'm afraid I engendered the failure. No,
Speaker 1: not at all. But you know, Tracy introduced you as
Speaker 1: you know, the CTO at Too Sigma. You don't seem
Speaker 1: like a guy very who's very busy or anything. So
Speaker 1: I'm sure it was very easy for you to reschedule
Speaker 1: your time just come back in for a second day,
Speaker 1: very easy, indeed. But no, seriously, thank you very much
Speaker 1: for coming back in and recreating last last week. The
Speaker 1: way we started our conversation last week, and really the
Speaker 1: first thing we discussed is that your firm, to Sigma,
Speaker 1: it's a very well known Quantitative Hedge Fund is known
Speaker 1: for having a game. You've created a video game and
Speaker 1: created a competition for people all around the world to
Speaker 1: come and design programs to master the game. So tell
Speaker 1: us what is this game that you have people do
Speaker 1: and why do you have people tried to upbeat it?
Speaker 1: So a couple of years ago we introduced a game,
Speaker 1: a programming competition game where first we within the company
Speaker 1: and then eventually members of the general public got a
Speaker 1: chance to write computer programs that would try to win
Speaker 1: some strategy game. So in fact, it isn't really a
Speaker 1: game of people, but it's a game of programming where
Speaker 1: you program something to try to win. The game was
Speaker 1: really successful internally and excited our engineers and got them
Speaker 1: to think really deeply about algorithms and about how to
Speaker 1: structure situations and game theoretic ways. And we decided to
Speaker 1: launch it thinking that it would attract many programmers that
Speaker 1: would then hear about two Sigma. Some of them might
Speaker 1: actually decide they want to work with us. It would
Speaker 1: also educate people because it requires very sophisticated and clever
Speaker 1: programming to win these games, and we're really interested in
Speaker 1: educating more and more people in tech it was sufficiently
Speaker 1: successful the first year that we did it again, and
Speaker 1: this year there were about six thousand players that wrote
Speaker 1: bots as we call them, to play from about a
Speaker 1: thousand organizations a hundred countries. In the top ten, there
Speaker 1: were six nations represented, and in the top ten winners
Speaker 1: of this two of them were high school students, amazingly enough,
Speaker 1: one of them from Brooklyn and one from Argentina. So um,
Speaker 1: I'm trying to rethink all my questions from last week. No,
Speaker 1: no new questions, okay, fresh questions. We we hear a
Speaker 1: lot about the competition for talent in technology. You obviously
Speaker 1: have all these financial firms that want programmers, um coders,
Speaker 1: people like that, and they're competing with tech firms in
Speaker 1: Silicon Valley. How intense is that competition And what's the
Speaker 1: benefit of trying to attract competition through something like this
Speaker 1: game versus more traditional enticements to the financial industry, like
Speaker 1: just offering people say a lot of money. Well, I think,
Speaker 1: first and foremost, what we're seeing is technology playing a
Speaker 1: bigger and bigger role in almost every industry. I refer
Speaker 1: to that as CS plus X for all X. So
Speaker 1: the innovation is occurring at that intersection of computing and X.
Speaker 1: It's certainly happening now in finance, but I think what
Speaker 1: comes first is technological excellence. So we see ourselves as
Speaker 1: having to play in exactly the same markets for talent
Speaker 1: then tech companies in many domains, and I think that
Speaker 1: will occur even beyond finance and healthcare and education, etcetera
Speaker 1: in the future, this kind of a global technology community.
Speaker 1: We try to appeal to that in having a culture
Speaker 1: internally that values technology, that values algorithms, and values careful thinking,
Speaker 1: values terrific engineering, and we try to portray that externally
Speaker 1: so that people know that's the kind of firm they're joining.
Speaker 1: So tell us about the game specifically, what kind of
Speaker 1: game is it? So the game is a turn based
Speaker 1: strategy game. So there this year somewhere either two or
Speaker 1: four players on the game. When the game starts, the
Speaker 1: players have three ships each and outer space, and the
Speaker 1: goal is to have the ships take over a large
Speaker 1: number of planets and basically take over the galaxy that
Speaker 1: they're part of. Uh. It's really simple in a way
Speaker 1: that the ships can really do only three things. They
Speaker 1: can move a certain number of positions, they can land
Speaker 1: on a planet, and they can take off from the planet,
Speaker 1: and there's some things that happen when they encounter other ships,
Speaker 1: and when they get on the planet, how they gain strength,
Speaker 1: and when more ships are created. But there are only
Speaker 1: three commands to do it. On the other hand, there
Speaker 1: are many, many possible positions in the galaxy, and that's
Speaker 1: what makes the game interesting. There is a huge combinatorial explosion,
Speaker 1: as we say, of moves that you can make at
Speaker 1: any given time. So, but it's extremely challenging to write
Speaker 1: a program to win in this galaxy. Compare the complexity
Speaker 1: of this game to a sort of move based game
Speaker 1: like we would like chess, for example. So in chess,
Speaker 1: the thing we think about, despite all the complexity of
Speaker 1: doing it, is that there's only one piece you move
Speaker 1: at a time, and that piece, depending upon the piece,
Speaker 1: can do different kinds of things. But we call it
Speaker 1: a branching factor of thirty five. At each move in
Speaker 1: the game, you can do about thirty five things, and Haylight,
Speaker 1: the branching factor is ten, followed by zeros, so a
Speaker 1: very very large number of moves. So it's essentially impossible
Speaker 1: for human to play, but a bot can play it
Speaker 1: really well because computers, as we know, are pretty fast.
Speaker 1: So people are playing this game, which bots have been
Speaker 1: most successful and what types of strategies have they been pursuing.
Speaker 1: This is a really interesting question. In the game. You
Speaker 1: might think that the approach should be that people should
Speaker 1: sit down. Players should sit down and think hard about
Speaker 1: should they go to a near planet that's very large,
Speaker 1: should they go to a distant planet that's smaller. Should
Speaker 1: they hide out in a corner and wait for other
Speaker 1: players to interfere with each other and the like. That's
Speaker 1: an algorithmic approach to the game. Or there's the question
Speaker 1: of should we be doing what say the deep mind
Speaker 1: people in that Google subsidiary in London are doing and
Speaker 1: building AI programs that play the game against each other
Speaker 1: and learn the right approaches by essentially trial and error
Speaker 1: and by seeing which wins both approaches are used in
Speaker 1: the game. The top players, the top say thirty or
Speaker 1: forty players used algorithmic approaches where they really thought things through. However,
Speaker 1: now this year some of the top players in the
Speaker 1: top fifty or sixty actually built very simple bots with
Speaker 1: very small amounts of code that actually learned by playing
Speaker 1: the game. Millions and millions of times, and it's quite
Speaker 1: interesting that that actually is working in a world which
Speaker 1: is this difficult. And of course you mentioned the Google
Speaker 1: deep Mind endeavor. It's important in the history of chess computers.
Speaker 1: This is the two different approaches. So back in the
Speaker 1: nineties when we think of Cass pro Verse deep Blue
Speaker 1: Deep Blue at the whole library of games and all
Speaker 1: these grand masters training it, and the new generation just
Speaker 1: learns chess from day one and it teaches itself without
Speaker 1: any GMS or anything. And these days that new approach
Speaker 1: is what works. But well, you're saying in this game,
Speaker 1: you've seen some success from both approaches. That's right. In
Speaker 1: the recent Alpha Go program that that deep Mind did,
Speaker 1: they learned to be a world champion in chess in
Speaker 1: four hours of play without much background. Really remarkable. This
Speaker 1: game is considerably harder. So if we think about artificial intelligence,
Speaker 1: some artificial intelligence is just to try to duplicate what
Speaker 1: people do. So like an early problem in AI was
Speaker 1: digit recognition. Could you read, say, the numbers on a
Speaker 1: check automatically? That was AI just a few years back.
Speaker 1: That was a very hard problem. Now then another problem
Speaker 1: in AI is to do something that humans do, but
Speaker 1: do it better. So that's like self driving cars. You
Speaker 1: can easily imagine that it should be possible. Maybe it's
Speaker 1: hard to build a self driving car because we can
Speaker 1: do it pretty well. Then there are these questions of
Speaker 1: things which we can't even do, and that's a game
Speaker 1: like hay Light. Can we get a I s to
Speaker 1: do that? And there are implications of course in financial
Speaker 1: markets were all kind of challenged by predictions and optimization
Speaker 1: and financial markets. Maybe it's very much the case that
Speaker 1: these AI systems, in the fullness of time, will do
Speaker 1: things we ourselves can't even think of doing today, and
Speaker 1: in making a better economic system. So I'm always curious
Speaker 1: when it comes to these bots that are essentially self
Speaker 1: learning the game, how good are they dealing with spontaneity
Speaker 1: or the unpredictability of other people's decisions or say, you know,
Speaker 1: just a human playing the game who might make a mistake.
Speaker 1: Do they always assume that the other players are a
Speaker 1: rational or can they react in some some way to
Speaker 1: the unexpected. I guess I think it's a really good question.
Speaker 1: I don't know the answer, and I think it's a
Speaker 1: subject of research now. To understand that. Two things come
Speaker 1: to mind. One is I saw some of the early
Speaker 1: newscasts on the early go playing programs, and people thought
Speaker 1: they were really creative and doing things that hadn't been
Speaker 1: seen before. I'm not a go aficionado, but I believe
Speaker 1: that to be true. The second is, it's certainly the
Speaker 1: case that many think that great creativity is kind of
Speaker 1: serendipity or almost a kind of randomness that happens. And
Speaker 1: of course, if we think that, and we think that
Speaker 1: great creativity comes of kind of the random ideas that
Speaker 1: maybe one of our strange colleagues might have some days
Speaker 1: that can be programmed. So obviously, humans can't play this game, heylite,
Speaker 1: It's way too complicated. Can humans appreciate the game like
Speaker 1: in the same way like if you watching two bods
Speaker 1: play against each other? Is it understandable enough so that
Speaker 1: someone could look at the game and sort of grasp
Speaker 1: what they're doing? Absolutely. A couple of things about that.
Speaker 1: Number one is that if humans are going to want
Speaker 1: to program bots for the game, they have to find
Speaker 1: it entertaining. So it has to be an interesting objective
Speaker 1: that they're trying to achieve, and they have to be
Speaker 1: able to watch and understand what their body is doing,
Speaker 1: and it's quite exciting, so you need that for the game.
Speaker 1: And then secondly, we in fact saw that in reality,
Speaker 1: many people have put up plays of the game on
Speaker 1: YouTube and other places where you can watch really interesting
Speaker 1: games and how they unfold, and you get to see
Speaker 1: the strategy. For example, what may happen is a player
Speaker 1: a players bought have to be careful. How I say this,
Speaker 1: A players bought may realize that it has very little
Speaker 1: chance of winning, but perhaps if it goes hides in
Speaker 1: a corner, the other players may defeed each other and
Speaker 1: it might come in second. And that's a strategy that
Speaker 1: happened in the first round of the game. That bot Yeah, well,
Speaker 1: the in fact you do. We do tend to personify
Speaker 1: these things over time, which is another interesting aspect of
Speaker 1: how humans deal with computers. But we we didn't see
Speaker 1: this behavior until the last week or so of Halight
Speaker 1: version one, and then all of a sudden, we call
Speaker 1: it an emergent behavior. It emerged from the game. We
Speaker 1: never anticipated that that happened. And there are other other
Speaker 1: kinds of strategies that also occur as well in the game.
Speaker 1: So you've been running, uh, two rounds of this game
Speaker 1: now Halight right, Um, you're in your second iteration. Have
Speaker 1: you ever recruited anyone that was playing the game? Has
Speaker 1: it actually translated into tangible recruitment benefits for you? Yes,
Speaker 1: happy to say that we have a tremendous employee that
Speaker 1: came out of Halight, one who's working with us on
Speaker 1: our London office, and we have many more people that
Speaker 1: remind us that they know about two Sigma because of Halight.
Speaker 1: So it's valuable from a marketing perspective as well. So
Speaker 1: I think it's something that will be around and helping
Speaker 1: us for a long term. In fact, I met a
Speaker 1: college intern who did Halight as a high school student
Speaker 1: and said that she knew about two Sigma because she
Speaker 1: did it as a high school student. I want to
Speaker 1: turn to more just the you know, talk about UH
Speaker 1: quantitative finance and some of the lessons you've learned before
Speaker 1: we do though, and before we move off the game.
Speaker 1: When we in our first attempt at recording this episode,
Speaker 1: at the end, you said, oh, you wanted to talk
Speaker 1: a little bit more about some of the high school
Speaker 1: students who had done so well in the game, and
Speaker 1: so I don't want to forget to do that this time.
Speaker 1: Tell us a little bit more out how high school
Speaker 1: students are able to who they are, how can they
Speaker 1: compete with the top computer scientists in programming at So
Speaker 1: let's just start with one thing first. So you have
Speaker 1: to design a game so that it's easy to get
Speaker 1: started with. Right. That's a nice thing about Checkers, right
Speaker 1: for little kids. You can learn the rules quickly, and
Speaker 1: yet it's pretty sophisticated to play it. Well, the same
Speaker 1: thing happens here. You want to build a game that's
Speaker 1: easy to get started with, but that has a really
Speaker 1: really long path, maybe in essentially an infinite path towards perfection,
Speaker 1: so maybe there can be no absolute perfection. You can
Speaker 1: play a very very long time, then it's a much
Speaker 1: better game. So we even wrote a paper about how
Speaker 1: to design these games, called the Design and Implementation of
Speaker 1: Modern Online Programming Competitions. So, again going back to the
Speaker 1: ease of starting, we realized that since they're easy to
Speaker 1: start playing, they're accessible to high school students. So we
Speaker 1: went out and did a bunch of hackathons around the
Speaker 1: New York City area and some other places and had
Speaker 1: quite a bit of acceptance. We had almost a thousand
Speaker 1: high school students doing this worldwide, and we learned about
Speaker 1: it because a teacher in Texas initially wrote to us
Speaker 1: and said that it was a great opportunity for members
Speaker 1: of his class to start programming. And we think that
Speaker 1: early outreach is very important. It's also a core value
Speaker 1: of the firm because the co chairs of the firm
Speaker 1: are very involved in mathematics education for young kids and
Speaker 1: also for programming educations via the M I T. Scratch
Speaker 1: initiative for middle schoolers and uh and high school students.
Speaker 1: So just one last thing on that one. I gotta
Speaker 1: just mentioned. So this, this this kid in Brooklyn actually
Speaker 1: had an article written about him in the Brooklyn newspaper.
Speaker 1: So that was very exciting. I was called Brooklyn High
Speaker 1: Schooler takes on the World. We'll have to check that
Speaker 1: one up well, link to it when we post this. Yeah.
Speaker 1: Uh So, widening the conversation out to finance and tech,
Speaker 1: we were referring to Two Sigma earlier as a very
Speaker 1: all known quant fund. I'm wondering what makes a quant
Speaker 1: fund a quant fund, given that nowadays it feels like
Speaker 1: pretty much every fund has some sort of systematic or
Speaker 1: programmatic trading actually happening. Right, So two SIGMAS a tech
Speaker 1: firm that looks at many places where we can apply
Speaker 1: technology to optimize outcomes and finance. So we're also in insurance,
Speaker 1: and we're in venture capital, etcetera. But certainly one of
Speaker 1: the things we do is investment management. As you mentioned,
Speaker 1: I think what differentiates us is number one, the deep
Speaker 1: and long term technical talent that we've had. After all,
Speaker 1: we were started by an m I T pH D
Speaker 1: and AI about fifteen or more years ago. David Siegel
Speaker 1: and John Overdeck, the other co chair, is a real
Speaker 1: expert mathematician, silver math OLYMPIAD and a statistician. So the
Speaker 1: two of them really brought this to the firm quite
Speaker 1: a while back and it's everywhere in the firm. Second
Speaker 1: is we do have scale in this. We've been doing
Speaker 1: it a long time, and I think that scale is
Speaker 1: really something that differentiates us from many of our competitors,
Speaker 1: right because, as we know, we've all heard every bank
Speaker 1: CEO these days or at times they say, oh, we're
Speaker 1: really a software company that does banking, or really a
Speaker 1: tech company. But you have a long experience with companies
Speaker 1: that are undisputably tech companies. Google and IBM, one of
Speaker 1: the biggest differences in terms of culture that you see
Speaker 1: at a place like two Sigma versus your experience at Google,
Speaker 1: I think probably if you could name one, it's that
Speaker 1: technology is viewed at least as the equal, if not
Speaker 1: the driver, of the core business. So at our firm,
Speaker 1: there's no question that those of us that do computer science,
Speaker 1: mathematics and statistics are viewed by almost everyone as the
Speaker 1: basis of the firm's success. Now, of course we need
Speaker 1: and we're very happy to have the folks that do
Speaker 1: compliance and legal and all the other activities that are
Speaker 1: needed in the firm, but it's really a technology and
Speaker 1: math and statistics first operation. I think the same thing
Speaker 1: is true at the really successful tech companies as well,
Speaker 1: and became frankly less true at the tech companies that
Speaker 1: didn't do so well. It is kind of interesting that
Speaker 1: if you think at places where algorithms and programmatic strategies
Speaker 1: might be really interesting to do, uh, the finance companies
Speaker 1: should theoretically be really really intriguing, because banks and insurers
Speaker 1: have these realms and reams of data that should be
Speaker 1: interesting for anyone with the technology background. But it almost
Speaker 1: feels like it's taken a little bit of time for
Speaker 1: people to catch on to that, and it's only now
Speaker 1: that a lot of the financial firms are making this
Speaker 1: really big push. Why do you think it's taken a
Speaker 1: bit of time. So one is, of course, finance use
Speaker 1: technology very early on, right, It was among the earliest
Speaker 1: users just to computerize account records and transfers and such.
Speaker 1: So perhaps it's the case that because finance used a
Speaker 1: lot of technology, there became kind of a installed base
Speaker 1: of old technology that actually acted as an impediment to modernization.
Speaker 1: So I think that is one fact. So those of
Speaker 1: us that are newer in the business have an advantage.
Speaker 1: An example, of course, if you look at say online advertising,
Speaker 1: it didn't exist more than a couple of decades ago.
Speaker 1: That's when it all began, so there can't be an
Speaker 1: installed base from the nineteen sixties. So I think we
Speaker 1: didn't have, if you will, negative inertia in new fields,
Speaker 1: and we we did have some of that in finance.
Speaker 1: The second is, I think it's important to understand what
Speaker 1: we should be doing in finance, and that's making financial
Speaker 1: systems economic systems work better. So all of us like capitalism,
Speaker 1: we like decentralization, and we like optimization of the firm,
Speaker 1: and we all hope that it will lead to pray,
Speaker 1: to optimality and an efficient operation of society that produces
Speaker 1: lots of goods and services for all. But we all
Speaker 1: know that if we're not careful, inventories build up, or
Speaker 1: prices get out of whack, or people have irrational exuberance
Speaker 1: and the like. I believe with the proper application of data,
Speaker 1: the proper application of mathematics and statistics, we can do
Speaker 1: a better job of running these economic systems. It's not easy,
Speaker 1: but I think that's really exciting. And I have a
Speaker 1: lot of success in attracting technical people to the firm
Speaker 1: because that's what I think we're doing. Do you proactively
Speaker 1: think about exactly what you said about building up some
Speaker 1: sort of legacy code base or some sort of legacy
Speaker 1: set of systems that ten years from now you'll still
Speaker 1: be hewing too, even if it's not the state of
Speaker 1: the art. I worry about it all the time. All
Speaker 1: of us in technology worry or should be worrying about
Speaker 1: the legacy that we will create. And it's a very
Speaker 1: difficult problem. If you think in the United States, they're literally,
Speaker 1: you know, millions and millions of programmers writing computer code
Speaker 1: all the time. All of that code will someday get old,
Speaker 1: and I'm afraid it will look like the substructure underneath
Speaker 1: Lexington Avenue out here sometime and make it very difficult
Speaker 1: to build the next subway. But in banking, you still
Speaker 1: hear stories about some of the banks having, um, how
Speaker 1: do you say coble or cobble This programming language from
Speaker 1: that stemmed from I think it was World War two,
Speaker 1: basically invented in the nineteen forties and nineteen fifties. And
Speaker 1: if you're one of the programmers who can still actually
Speaker 1: code in this ancient, ancient software language, apparently you can
Speaker 1: earn big money. So it does seem to be something
Speaker 1: of an issue. So common business oriented language COBAL. Yeah,
Speaker 1: I think it comes probably from late fifties and sixties. Uh,
Speaker 1: not World War two, but you're on the right track there.
Speaker 1: And yeah, there's a lot of cobaal code around and
Speaker 1: some of it was written by employees who retired, maintained
Speaker 1: by the employees they trained who have now retired, and
Speaker 1: the next generation is maintaining that. And you can just
Speaker 1: think of the engineering challenge do you rewrite it all?
Speaker 1: But do you even know what it does. It's a
Speaker 1: real challenge for organizations to deal with that. I don't
Speaker 1: believe we have any coball. In fact, I'm certain we
Speaker 1: have no coball co ball free. One of the things
Speaker 1: you hear a lot uh Silicon Valley people talk about
Speaker 1: is the importance of culture as the enduring mode or
Speaker 1: the enduring sustainable advantage, and that with whatever else that
Speaker 1: goes on, as long as they have a superior culture,
Speaker 1: that that allows them to beat the competition. How do
Speaker 1: you guarantee that that's in place at two sigma? And
Speaker 1: when you think about all of these new funds or
Speaker 1: legacy funds that sort of want the new quant unit,
Speaker 1: or banks trying to get into quant stuff, how much
Speaker 1: do you see that as an advantage towards competitors who
Speaker 1: would otherwise want to modify what you're doing. I think
Speaker 1: in all of our organizations, talent is the first and
Speaker 1: most important thing. So the talent today is possessing of
Speaker 1: many opportunities because there's so many applications of advanced computer
Speaker 1: science and machine learning and AI and the like. So
Speaker 1: we really feel that that that culture is really important,
Speaker 1: and the culture is it's hard to pin down exactly
Speaker 1: what it is. Certainly, it's clear objectives for the business.
Speaker 1: Certainly it's clear understanding of what we do for our
Speaker 1: clients and we have to understand what to do and
Speaker 1: feel good about doing that really well. But it's also
Speaker 1: soft and other things. Just if you think about the
Speaker 1: boards where people were talking about Haylight, um, you know
Speaker 1: you read them if you're an employee and you feel
Speaker 1: good about working at the firm. One of them said,
Speaker 1: it's an absolute blast discussing strategy, sharing replays and getting
Speaker 1: excited about the games with friends. That's a great place
Speaker 1: to work when you're doing that for the world. Last question,
Speaker 1: unless Joe has more, what's your taught up tip when
Speaker 1: it comes to avoiding technological errors such as the one
Speaker 1: we experienced last week. Well, my original career as a
Speaker 1: professor at Carnegie Mellon was in reliable distributed systems, and
Speaker 1: that means that you have to have duplication at many
Speaker 1: levels of the system. So how do you make sure
Speaker 1: you have two of everything in the chain. That's important
Speaker 1: in financial markets, so that we have capacity to keep operating.
Speaker 1: That's probably important in games. We had many servers that
Speaker 1: could run Haylight, so if one of them, god forbid,
Speaker 1: had a problem, another one would keep running. In fact,
Speaker 1: we ran maybe tens or hundreds of servers simultaneously to
Speaker 1: deal with the load. It's probably important in radio and
Speaker 1: podcast two. On that note, a perfect tip for all
Speaker 1: of us to remember in all endeavors of our lives.
Speaker 1: Alfred Spector, thank you very much for joining us. It's
Speaker 1: my pleasure. I enjoyed doing it again, so I really
Speaker 1: hope we don't have to bring Alfred in for a
Speaker 1: third time. But it was really I like, I disagree.
Speaker 1: I really I was gonna say, wait, wait, wait, I
Speaker 1: was gonna say, it was really enjoyable speaking to him
Speaker 1: for another thirty minutes. Agree, And if we have to
Speaker 1: do it a third time, I'm looking forward to that
Speaker 1: as well. But in all seriousness, I think we did
Speaker 1: a pretty good job sort of recreating the magic of
Speaker 1: that first one. No, I really I love that, and
Speaker 1: I love like you know, we talk a lot about
Speaker 1: quantitative finance in our work, and we'll talk about various
Speaker 1: well known strategies, momentum strategies and other uses of alternative data.
Speaker 1: We talked about that all the time and in our reporting,
Speaker 1: but we don't talk about the sort of what it
Speaker 1: what needs to happen for people to come up with
Speaker 1: that stuff, and the idea of that this stuff has
Speaker 1: to happen through recruitment and culture and academic study. So
Speaker 1: I feel like this is a interesting, unexplored facet of
Speaker 1: all this. Yeah, I absolutely agree. And I have to
Speaker 1: say some of the machine learning that we were talking
Speaker 1: about this notion that bots, once they realized that they
Speaker 1: were probably not going to win, or they didn't have
Speaker 1: a good chance of winning, they went and they hid
Speaker 1: in some obscure corner of the haylight galaxy. That kind
Speaker 1: of strategy is just really fascinating, and it's amazing to
Speaker 1: think that high schoolers potentially are coding that kind of
Speaker 1: learning into the system. And the fact that in the
Speaker 1: early rounds of the game they weren't doing that and
Speaker 1: that they learned that sort of adaptive approach over time
Speaker 1: is really fascinating. Does it make you think of Skynet?
Speaker 1: Makes me think of skynet a little bit, definitely. Okay,
Speaker 1: all right, this has been another edition of the Odd
Speaker 1: Lots Podcast. I'm Tracy Alloway. You can follow me on
Speaker 1: Twitter at Tracy Alloway, and I'm Joe Why isn't all.
Speaker 1: You can follow me on Twitter at the Stalwart and
Speaker 1: be sure to follow our hard working producer Topur Foreheads
Speaker 1: at foreheads T, as well as the head of podcast
Speaker 1: at Bloomberg, princesco be at Francesca today. Thanks for listening.