Speaker 1: Pushkin. Welcome back to Risky Business, a show about making
Speaker 1: better decisions. I'm Maria Kanakova.
Speaker 2: And I'm Nate Silver.
Speaker 3: Today on the show, we're gonna be talking about how
Speaker 3: chat GPT plays poker, i e.
Speaker 2: Poorly.
Speaker 3: If chat gipt is getting ready for the World Series
Speaker 3: of Poker like us, then it's got a lot of
Speaker 3: homework to do.
Speaker 1: Yes. And after we talk about that, we'll talk about
Speaker 1: Harvard two point zero aka the fact that foreign students
Speaker 1: are now being potentially barred from attending Harvard University. Harvard
Speaker 1: has sued the administration. Anyway, we'll be talking about what's
Speaker 1: going on with that and what the implications are for
Speaker 1: the future of the United States and kind of research development,
Speaker 1: brain power and the US competitive edge. So for people
Speaker 1: who don't subscribe to Silver Bulletin, which you absolutely should,
Speaker 1: Nate and I both had poker posts this last week.
Speaker 1: Mine was about cheating, was about chat GPT, but his
Speaker 1: post this week was one of the funniest things I've
Speaker 1: read in a while about his attempts to get chat
Speaker 1: GPT to simulate a hand of cash game poker. And
Speaker 1: one of the reasons I mean, I found this amusing
Speaker 1: because I mean, first of all, it fused cards together
Speaker 1: for an image. From the beginning to the end, it
Speaker 1: was pretty spot on in terms of being spot on wrong.
Speaker 1: But Nay, You're also someone who is constantly writing about
Speaker 1: how good AI is at so many things, and so
Speaker 1: it was funny to me to see it fall so
Speaker 1: short of something that actually tests intelligence as opposed to
Speaker 1: being able to kind of pull together a lot of
Speaker 1: things and spit out an answer.
Speaker 3: There are some reasons why I'm just sid in testing
Speaker 3: chatchipt on poker, but they kind of fall into two
Speaker 3: big buckets. Right, What is it like poker played in
Speaker 3: the context of a real hand or in this case,
Speaker 3: a real text based simulation I guess of a hand.
Speaker 3: It really does require quite a few skills, right. There
Speaker 3: is the pure math part of it. What is the equilibrium,
Speaker 3: the Nash equilibrium, the gto strategy that you're solving for.
Speaker 3: There's also making adjustments for how other players play. There's
Speaker 3: the conversation you're having, the physical reads that you're getting
Speaker 3: their stuff, like knowing what the rules are right, which
Speaker 3: might seem trivial, but we've yeah, chatchpt also.
Speaker 1: Does not know.
Speaker 2: It seems to be no, it is fun to look.
Speaker 3: I mean, I'm sure we've all had hands where we
Speaker 3: like misread a board or I you know, I had
Speaker 3: a big hand in the main event at the when
Speaker 3: I play back in Florida back in April or whatever,
Speaker 3: right where like a guy had like a twenty five
Speaker 3: thousand dollars chip that was like almost the same color
Speaker 3: as the felt, which is not good chipkit maintenance, by
Speaker 3: the way, Seminal hard Rock. I like the WPT, but
Speaker 3: that chip kit it's got to be improved, right, But yeah,
Speaker 3: you're tired, you make mistakes like that. But and you
Speaker 3: also have to have like a lot of short term memory,
Speaker 3: which sounds trivial, but you you know, you want to
Speaker 3: remember how you arrived at this current spot in the hand, right,
Speaker 3: you have to keep crack of all these stack sizes,
Speaker 3: which again seems trivial, but like it's a good test
Speaker 3: of general intelligence of a certain type, especially a live
Speaker 3: poker hand, like I'm asking.
Speaker 2: Chat ept to simulate.
Speaker 3: Right. The other reason is that like I don't think
Speaker 3: engineers in Silicon Valley are trying to optimize it for poker.
Speaker 3: And the reason that's important is because like, look, there
Speaker 3: are benchmarks like math Olympiad problems that these LM's large
Speaker 3: language models like CHATCHPT, Claude, et cetera compete on, right,
Speaker 3: And they're like, well, we be bragging that we can now,
Speaker 3: you know, beat all but the Nobel Prize mathematicians on
Speaker 3: x percentage of math problems and things like that. And
Speaker 3: there are a couple of issues with that, right. Like
Speaker 3: one is that, like, clearly, if you train a transformer model,
Speaker 3: a machine learning model on a particular type of problem,
Speaker 3: like it can do fairly well or very well often, right,
Speaker 3: Like poker is to a first approximation, and it's an
Speaker 3: important approximation. Right, But if you have poker dedicated tools,
Speaker 3: I wouldn't consider a solver and AI that's a technical
Speaker 3: distinction I think might not be that important for our listeners.
Speaker 3: But like, you know, if you want to use computers
Speaker 3: to play very good poker, then they can play very
Speaker 3: very good poker. Right. The question is like, can you
Speaker 3: take a text based model and have this organic property
Speaker 3: where intelligence emerges and converts from the data set towards
Speaker 3: superintelligence without training it on poker specifically. And the answer,
Speaker 3: at least as of last week when I did this,
Speaker 3: is no, it fails miserably.
Speaker 1: Yeah, and I think it's actually important. One of the
Speaker 1: things that came to my mind when I was reading
Speaker 1: your piece was that poker has been a benchmark for
Speaker 1: AI development well before LMS, Right, It has been kind
Speaker 1: of the gold standard that teams all over the world
Speaker 1: have been working on for decades because it is a
Speaker 1: much more complex game in many senses than chess, then
Speaker 1: even go, and it's a game where, you know, if
Speaker 1: computers can actually manage to outthink human players on a
Speaker 1: broader scale, that would mean something in terms of kind
Speaker 1: of broader intelligence capabilities. And before LMS were developed, that
Speaker 1: had not happened. Right, There had been computer programs, kind
Speaker 1: of AI algorithms that have been able to beat heads
Speaker 1: up one on one opponents in poker. But when it
Speaker 1: came to full ring games, So for people who don't
Speaker 1: know poker, that means basically, when you put it in
Speaker 1: a rich environment with you know, six players, eight players
Speaker 1: and you have a computer there, it was still not
Speaker 1: quite there. And one of the you know, one of
Speaker 1: the reasons why poker so interesting is it's not just math, right,
Speaker 1: especially when you have multiple players. There's so many dynamics
Speaker 1: and there's actually a very thorny problems for AI, which
Speaker 1: is that if you program something to follow an algorithm
Speaker 1: right to be GTO game theory optimal, then it can
Speaker 1: be exploited by a human if the human figures out
Speaker 1: the GTO strategy. Now, if it were another human, then
Speaker 1: you'd adjust immediately right the moment the human adjusts, the
Speaker 1: other human would adjust and kind of figure it out.
Speaker 1: But if you're an a if you're a computer, and
Speaker 1: you build in that adjustment parameter, what ends up happening
Speaker 1: is that the model adjusts too much and too quickly,
Speaker 1: and so that lacks kind of some of the nuance
Speaker 1: and flexibility that marks that's kind of the hallmark of
Speaker 1: the great poker players, right, kind of that ability to
Speaker 1: into it when it's time to slightly deviate. And I mean,
Speaker 1: it would be terrifying but amazing if a model could spontaneously,
Speaker 1: like an LLM, could actually spontaneously figure that out and
Speaker 1: so far, I mean, instead instead of doing that, Nate,
Speaker 1: you know, your model decided that the person who won
Speaker 1: the hand actually lost the hand, right, It just got
Speaker 1: basic things completely wrong, and you just you know, obviously
Speaker 1: we've talked about pe doom a lot on the show.
Speaker 1: I think you and I are both worried about that.
Speaker 1: But when I see this poker stuff and like, oh man.
Speaker 3: Yeah, I don't know how much to update. But look
Speaker 3: my say in the post, I mean, like my appreciation
Speaker 3: and expectations for large language models have increased a lot
Speaker 3: over the past year. I had a pretty good experience
Speaker 3: working with them when I was building the NCAA tournament
Speaker 3: model that we host silver bulletin and just helpful kind
Speaker 3: of Swiss army knives or like annoying data related tasks.
Speaker 3: It's late at night, you forget the Stata command for something,
Speaker 3: You're like, right, give me the right command and write
Speaker 3: five lines of code, and like usually it works, and
Speaker 3: it kind of now shows you like the chain of
Speaker 3: reasoning and like what it's thinking. Right, And but you know,
Speaker 3: people are saying that we're going to have like AI
Speaker 3: revolutionizing all programming or you know, well, first of all,
Speaker 3: the big claim is going to have AGI, meaning that
Speaker 3: a machine. It's a little ambiguous when people say, does
Speaker 3: this mean like chat GPT itself is AGI? Right, Well,
Speaker 3: clearly not. It's very far from human being in some things, right,
Speaker 3: Does that mean that a combination of transformer or machine
Speaker 3: learning based technologies is or zooming out the definition of AI.
Speaker 3: It's really AI still, right, so that you have chechiptiza
Speaker 3: seventy percent of things well, and then you specialized applications
Speaker 3: for twenty percent, and then there are ten percent where
Speaker 3: you're not doing it very well. Like that would still
Speaker 3: be a big achievement, very disruptive achievement. I'm not sure
Speaker 3: it would kind of quite count as AGI. Like one
Speaker 3: thing you could do is, like some of these new
Speaker 3: models will say, okay, I can detect that you ask
Speaker 3: for something that requires mathematical precisions. So let's take a
Speaker 3: simpler example. Right, Let's say I want to take the
Speaker 3: distance between pick two cities, Boise, Idaho and Tampa, Florida. Right,
Speaker 3: that's pretty easy to calculate mathematically if you look up
Speaker 3: the latlong coordinates for Boise and Tampa, and then there
Speaker 3: are various formulas that.
Speaker 2: You can use.
Speaker 3: Right, So a pure data set where it's just crunching
Speaker 3: texts might not find that answer because there's not necessarily
Speaker 3: a lot of examples of like what's the distance between
Speaker 3: Boise and Tampa? Right, Whereas you would have that for
Speaker 3: like La and new York or something. However, you can
Speaker 3: set up kind of agentic meaning agent ais where they're like, okay,
Speaker 3: well this person asked a question that triggered a routine
Speaker 3: of mine. Where now I'm going to go and I
Speaker 3: know to search the internet for the coordinates or maybe
Speaker 3: it's stored somewhere right of Boise and Tampa. And then
Speaker 3: the formula. I know the formula and I can apply it, right,
Speaker 3: so like you have the scaffolding. It's sometimes called a
Speaker 3: different processes on top of one another. And I guess
Speaker 3: that kind of I guess that counts as I mean,
Speaker 3: it's kind of how humans solve problems, right, They're like, oh,
Speaker 3: and now I have to go look something up, right,
Speaker 3: It's still I think subtly different. So one implication I
Speaker 3: think this has is artificial general intelligence versus super intelligence ASI,
Speaker 3: sometimes called where there's like an explosion of intelligence just
Speaker 3: from kind of mining text or other data sources. I
Speaker 3: have actually become a little bit more skeptical of that,
Speaker 3: or I think it's presumptive to assume it can find that.
Speaker 3: I mean, like, let me give you another example, right,
Speaker 3: like one I had an idea for either a silver
Speaker 3: bulletin post or maybe even a risky business segment, right
Speaker 3: where like I asked, chatchipt deep research, design a meal
Speaker 3: with foods that have no clear precedent in other countries, right, huh?
Speaker 3: And I thought, and the idea was that, like, you know,
Speaker 3: at some point somebody invented pizza or spaghetti or whatever else, right,
Speaker 3: or or I.
Speaker 1: Mean at some point somebody invented bread and figured out
Speaker 1: that you can take flour and like bake shit with it.
Speaker 1: It's amazing.
Speaker 3: Yes, you have to be able to buy these ingredientstead
Speaker 3: of Trader Joe's or a Whole Foods. Right. I was
Speaker 3: just going to like hire professional chef and have a
Speaker 3: dinner part and ask how these creations went, right, And
Speaker 3: it's like and all they recipes were like here's salmon
Speaker 3: with miso paste, right, really creative and things like that, right,
Speaker 3: and like it didn't have any ability to like extrapolate
Speaker 3: beyond the data set. I'm sure these preparations are fine.
Speaker 3: It was like their little chefy, meaning like things that
Speaker 3: like have one too many ingredients, right, and like and
Speaker 3: I don't, yeah, look so bad chefe.
Speaker 2: Yeah.
Speaker 3: And meanwhile, I've been like I've been interviewing candidates for
Speaker 3: like a sports assistant position at Silver Bulletin, and one
Speaker 3: of the questions is, how are you using AI? These
Speaker 3: are very bright I want to say kids. A lot
Speaker 3: of them are are recent college grads, you know, your
Speaker 3: early twenties to mid thirties, basically right, and very technically
Speaker 3: proficient people. And asked them how they're using a GI
Speaker 3: and their experiences like kind of similar to mine. They're like, Yeah,
Speaker 3: for a certain task, it's very helpful. For certain task,
Speaker 3: it's somewhat helpful for certain tasks, not helpful at all.
Speaker 3: I can get you in trouble, right, which again contrast
Speaker 3: with the experience of people at the AI l apps
Speaker 3: to say, yeah, it's going to like displace all programming
Speaker 3: within a year or two.
Speaker 2: I mean, I know you should weigh in here.
Speaker 1: So I have an interesting kind of anecdote to add
Speaker 1: to this that's not poker related, but that talks about
Speaker 1: kind of some of this nuance and complexity and the
Speaker 1: fact that you know that all of these lms still
Speaker 1: are wanting in certain major respects. So I had the
Speaker 1: chance last week to speak to the CEO of one
Speaker 1: of the major AI companies. I won't name which company
Speaker 1: it was, but one of the big ones. And someone
Speaker 1: had asked him earlier, what was something that was surprising
Speaker 1: about the way that this AI functioned, and he said,
Speaker 1: you know, one of the mistakes that people is kind
Speaker 1: of they trust it, And what we know is that
Speaker 1: it's remarkably accurate ninety percent of the time, right, and
Speaker 1: then ten percent of the time it isn't. But the
Speaker 1: ten percent is getting harder and harder for humans to
Speaker 1: spot because it doesn't make errors in a way that's
Speaker 1: intuitive for people, right. It makes errors in different ways.
Speaker 1: It's like computer errors AI errs, that's not the way
Speaker 1: that the human mind makes errors. He's like, but like,
Speaker 1: it's fine because we have really smart people who can
Speaker 1: figure out where it's making errors and can work with
Speaker 1: it and can kind of calibrate it and help. And
Speaker 1: so my question, my follow up question, was, Okay, you
Speaker 1: know what happens when the new generation that's being brought
Speaker 1: up with this AI comes up and they've been educated
Speaker 1: with it and they've been using it the whole time,
Speaker 1: and they didn't necessarily get the same education that we got, right,
Speaker 1: because they aren't the incentives are different, and they're not
Speaker 1: getting the deep knowledge. They're not actually able to figure
Speaker 1: out what are the programming errors because this person was
Speaker 1: talking that, you know, some of the biggest potential of
Speaker 1: AIS and like programming and biology and kind of those
Speaker 1: those types of things, and I said, well, what if
Speaker 1: that person doesn't have the neuroscience and the biology background
Speaker 1: or the programming background. And what the CEO said was,
Speaker 1: this is a major problem, and what we're hoping is
Speaker 1: that we can develop even more advanced AI to help
Speaker 1: and to help fix the problems that AI is causing.
Speaker 1: Because he was like, yeah, this is a big problem
Speaker 1: and we're not quite sure, like we're hoping for the best,
Speaker 1: but we don't know. And the fact that this was
Speaker 1: our conversation didn't exactly inspire me to heights of thinking
Speaker 1: this is going to replace intelligence or become AGI or
Speaker 1: what did you call, Nate the other ASI artificial super intelligent? Yeah,
Speaker 1: artificial superintelligence. So it didn't inspire me that that was
Speaker 1: going to happen, and instead it was like an uh
Speaker 1: oh moment where like, what happened when the humans who
Speaker 1: are kind of helping push it along and make it better?
Speaker 1: When that generation, like when they age out and they're
Speaker 1: new people out there who who lack that sort of
Speaker 1: skill and expertise and who grew up with AI that
Speaker 1: is ninety percent accurate or even ninety five percent accurate,
Speaker 1: but they can't. It's harder and harder to spot that
Speaker 1: five and ten percent. And that to me was actually, like,
Speaker 1: it's a worrisome thing, and it's clearly worrisome to the
Speaker 1: people who are developing these technologies as well. And so
Speaker 1: that I think is something that dovetails with what you
Speaker 1: were talking about here. What happens Nate if all of
Speaker 1: a sudden, all poker players are training with chatchipt right,
Speaker 1: and that's that's actually great for us, but not great
Speaker 1: for the future of poker.
Speaker 3: And we'll be right back after this break. I use
Speaker 3: large language models for tasks that I have strong domain
Speaker 3: knowledge over and could do myself. But they're a labor
Speaker 3: saving device. Often they say substantial labor, right, Like I
Speaker 3: use them to copy edit articles I posted the newsletter,
Speaker 3: and I you know, I'm a fairly proficient user of
Speaker 3: the English language and like for programming, right Like you know,
Speaker 3: if you're like using AI to like design a website
Speaker 3: and some of the functionality breaks, right, that might be okay.
Speaker 2: You can patch it and fix it. Right, if I'm
Speaker 2: doing things.
Speaker 3: Involving modeling, I'm trying to project the value of basketball
Speaker 3: teams or football teams, or basketball players or football players. Right,
Speaker 3: and there's a bug where all of a sudden, like
Speaker 3: one of the worst players in the league is rated
Speaker 3: as being very good. Right. I mean, and you see
Speaker 3: this in poker, where like you know, AI is relying.
Speaker 3: You get punished in poker for sloppy application of imprecise
Speaker 3: application of heuristics. Like one of the things like the
Speaker 3: AI did so at first, I had it simulate just
Speaker 3: one hand, and it had like a backstore for each player.
Speaker 3: It fucked that up, right, then like Oka, I'm gonna
Speaker 3: use deep research now and have it simulate like an
Speaker 3: orbit of eight hands.
Speaker 2: So that was a little better.
Speaker 3: It took more compute time, right, Some of the hands
Speaker 3: were decent, but it you know, it didn't know how
Speaker 3: to like read aboard correctly and keep track of stacks.
Speaker 3: But like, but when it was over able to overcome
Speaker 3: those errors, it also had poor strategy and it gave
Speaker 3: various excuses. One thing it said is like, well, the
Speaker 3: quality of text based content on poker on the internet
Speaker 3: really sucks, Right, it's aware of that is it says
Speaker 3: it's worse than go or chess, although when I talk
Speaker 3: to people who know how AI's played chess, it's also
Speaker 3: a disaster. Even something like wordle I heard from a user.
Speaker 3: You know you think wordle is a word game and
Speaker 3: it does very well, but like it can't quite figure
Speaker 3: out the strike sure of the problem, right, it kind
Speaker 3: of was learning a little bit more by by a
Speaker 3: rote And to be clear, like this is a very
Speaker 3: good strategy for like many types of problems. And also
Speaker 3: if you were to say, okay, here's a bunch of
Speaker 3: we bought data from PIO solver or gto wizard. These
Speaker 3: are solvers if you don't know listeners or a database
Speaker 3: of high stakes hands. Like if you trained it on
Speaker 3: that and then had GPTs say okay, poker question, I'm
Speaker 3: going to call them especially trained database, then it would
Speaker 3: do well. And maybe if people are criticizing its for
Speaker 3: on poker, then the labs will start to do that. Right,
Speaker 3: but it's still kind of like slightly cheating from the
Speaker 3: standpoint of super intelligence. Also, it says like when you
Speaker 3: asked me to do a whole bunch of things at once, right,
Speaker 3: So I told it, give me characters, give me eight
Speaker 3: characters who have like they were all kind of like
Speaker 3: ethnic stereotypes, right, it was.
Speaker 1: They were very funny.
Speaker 3: It was kind of yeah and great. It was kind
Speaker 3: of at the one hand, so half the players were women.
Speaker 3: That was very feminist of it, right, But then they're
Speaker 3: all ethnic stereotypes of different kinds, right, and so it
Speaker 3: was kind of both woken and unwoke, right the opposite. Yeah,
Speaker 3: it was kind of good about like matching players playing
Speaker 3: styles with with their actions, but like, but it couldn't
Speaker 3: keep track of stack size. It's like, yeah, keep track
Speaker 3: of stacks. I have to store a bunch of stuff
Speaker 3: in memory, and then when you ask me to do
Speaker 3: a whole bunch of things at once, right, and like
Speaker 3: you know, and even including like so in the hand
Speaker 3: I show a sylver bulletin it like it misreads the board,
Speaker 3: doesn't recognize that a pair of nines has a higher
Speaker 3: two pair. If you ask it that out right, then
Speaker 3: it thinks about it more and gets the question right, right,
Speaker 3: But like it loses track of things in the context
Speaker 3: of these scaffolding situations where it has to keep track
Speaker 3: of a bunch of things at once and it doesn't
Speaker 3: know it doesn't know what to prioritize right. Like you know,
Speaker 3: you can make a lot of mistakes in poker if
Speaker 3: you don't know which hand be twitch hand. That's a
Speaker 3: more elementary mistake than anything else.
Speaker 1: Absolutely, So there are a few things that stand out
Speaker 1: to me here.
Speaker 3: One.
Speaker 1: I mean, it's a computer, right, like we humans have
Speaker 1: working memory capacity problems. It should be able to keep
Speaker 1: all these things like this is what it's good at.
Speaker 1: But the problem that you're kind of that you're illustrating
Speaker 1: is it doesn't know how to prioritize right, and that
Speaker 1: that is actually that is a major problem. And also
Speaker 1: when we keep saying thinking. But one of the first things,
Speaker 1: so when I was just learning poker, one of the
Speaker 1: first lessons I had with Phil Galfon, who is one
Speaker 1: of the people who kind of I worked with and
Speaker 1: who taught me a lot. I remember very early on
Speaker 1: he said, you know, I can give you a bunch
Speaker 1: of charts and a bunch of outputs, and you can
Speaker 1: memorize them and you'll be a very decent player very quickly.
Speaker 1: Like I know, I can give you this shit, you
Speaker 1: can memorize it, you can spit it back at me.
Speaker 1: You'll be fine, He's like, but I don't want you
Speaker 1: to do that, because that's going to make you a
Speaker 1: fine poker player, and you'll do fine in the short run,
Speaker 1: but you'll never be a great poker player because you
Speaker 1: have no idea why you're doing it. You're not actually thinking,
Speaker 1: you don't understand. You've just done a rote memorization, which
Speaker 1: is might make you more money in the short term,
Speaker 1: but in the long term is actually going to be
Speaker 1: detrimental to you because you're going to do stuff unthinkingly
Speaker 1: because you've memorized it.
Speaker 3: He said.
Speaker 1: What I want you to do is instead think through
Speaker 1: things and figure out, Okay, why right? For every single play,
Speaker 1: why am I doing this? Why would I play this
Speaker 1: hand and not this hand? Why would I raise this
Speaker 1: type of hand and not this type of hand? Why?
Speaker 3: Why? Why? Why? Why?
Speaker 1: And that's something that to this day has stayed with
Speaker 1: me because it's such an important thing to remember when
Speaker 1: you're making a decision. Right, And this doesn't even have
Speaker 1: to be poker, It can be any sort of decision.
Speaker 1: Why am I choosing this action? Why is it better
Speaker 1: than all of the other actions? Right? And if you
Speaker 1: even if you feed you know, piosalvereign gto Wizard outputs
Speaker 1: two lllms, they'll be doing some rote of memorization. You know,
Speaker 1: at this point they're not understanding the why, and so
Speaker 1: that will lead them still to make big mistakes, which
Speaker 1: happens when you just learn solver outputs, no matter how
Speaker 1: sophisticated those outputs might be and how correct they might
Speaker 1: be in one specific hand, in one specific spot, if
Speaker 1: you don't understand the why, you're either going to overgeneralize
Speaker 1: it right or misapply it like you're going to screw
Speaker 1: it up because you don't understand the underlying reasoning. And
Speaker 1: there's the difference between looking like you're thinking and actually thinking.
Speaker 1: And yeah, you know, as a human you have other pitfalls,
Speaker 1: and even if you're you know, thinking through things, you
Speaker 1: can mess up, So you mess up in different ways.
Speaker 1: But I think that's a really important distinction and probably
Speaker 1: one of the reasons why AGI is not as close
Speaker 1: like poker. Actually illustrates in a very practical way, why
Speaker 1: the notion that you can just have a lot of
Speaker 1: data and all of a sudden have this you know,
Speaker 1: flurry of insight might not be as I guess, as
Speaker 1: intuitive as one might think.
Speaker 3: When one thing, Chatchip you told me when I asked
Speaker 3: to like audit itself, is like, well, poker's very difficult
Speaker 3: because it's adversarial. You know, understand that there's some game
Speaker 3: theory there, but it's adversarial, and like there's no one
Speaker 3: I mean, there is like I guess some superstructure of
Speaker 3: like a solution for all poker hands if you get
Speaker 3: very zoomed out, right, but like but like subtle things
Speaker 3: when you have to calculate like an adversarial equilibrium on
Speaker 3: the fly basically, and it's that's very difficult.
Speaker 1: Yeah, I mean think about not even poker, but think
Speaker 1: about trying to do a game theory solution for multiple players.
Speaker 3: Right.
Speaker 1: It's hard enough trying to do a payoff matrix for
Speaker 1: two players when you're really trying to think through it
Speaker 1: and you're trying to think of all of the different
Speaker 1: payoffs and figuring out, you know, how to exactly do
Speaker 1: you wait them, how exactly do you structure that. Now
Speaker 1: when it's three players, four players, I mean, it's incredibly
Speaker 1: difficult to do that accurately. And so even if we
Speaker 1: just zoom out from poker, like this is just an
Speaker 1: incredible a very tough problem, even if you understand game theory.
Speaker 1: Plus I mean, I think that anyone who has used solvers,
Speaker 1: and anyone who's talked about this understands anyone who doesn't
Speaker 1: even play poker but has worked with algorithms, the common
Speaker 1: saying garbage in, garbage out is absolutely accurate.
Speaker 3: Right.
Speaker 1: The solver works based on what ranges of hands you
Speaker 1: as a human put in there, right, and what responses
Speaker 1: you allow or don't allow, right, And yes, it will
Speaker 1: come up with an equilibrium strategy. But if you were wrong, right,
Speaker 1: if there's a player who's playing a totally different range,
Speaker 1: if there's a player who's playing a totally different strategy,
Speaker 1: all of a sudden, your solution means nothing. And as
Speaker 1: a human you can figure that out, and you can
Speaker 1: kind of make adjustments from baseline, right, if you understand
Speaker 1: what the baseline strategy as you can adjust as an LM,
Speaker 1: as an AI who is learning that but doesn't kind
Speaker 1: of have that nuanced experience at least at this point.
Speaker 1: I don't pretend to know what AI is going to
Speaker 1: be capable of in you know, in five years and
Speaker 1: ten years, but at least at this point they're not
Speaker 1: capable of making those sorts of nuanced extrapolations and figuring out, Okay,
Speaker 1: what were my inputs accurate, right, or were my inputs
Speaker 1: not quite accurate.
Speaker 3: Look, one thing human beings are good at is that
Speaker 3: human beings are relatively good estimators, and chatchipt can be
Speaker 3: sometimes if it's like, Okay, take all this text on
Speaker 3: the web and kind of give me the average of that,
Speaker 3: Like there are some applications where it's pretty good. But like,
Speaker 3: you know, the other day, I was getting getting a
Speaker 3: drink with my partner and a guy who looked like
Speaker 3: he was homeless comes in and like hands like the
Speaker 3: hosts slash barked like a note saying call nine to
Speaker 3: one one or something like that, right, and you kind
Speaker 3: of have to make this judgment call about like is
Speaker 3: this like a paranoid schizophrenic or is someone actually in danger?
Speaker 3: And like I think was pretty clear. I mean they're
Speaker 3: probably you know, two bartenders and eight customers clear to everybody.
Speaker 3: Like this guy I think is not like acutely in
Speaker 3: danger or anything. But I'd never quite experienced situation like
Speaker 3: that before. And like the fact that we can use
Speaker 3: like our general intelligence about like human behavior Yeah, Like
Speaker 3: I think everyone made the right decision that we're not
Speaker 3: going to call the police either to rat on him
Speaker 3: or to say he was in danger. And like you know,
Speaker 3: and that kind of thing is is is hard for
Speaker 3: language yells to do, you know.
Speaker 1: No, I mean I think in general, like this just
Speaker 1: illustrates a really good point, which is that humans, even
Speaker 1: not the smartest humans, are much smarter and much more
Speaker 1: capable in very basic ways that we take for granted
Speaker 1: than a lot of kind of super intelligences. Right, Like
Speaker 1: it took forever for a robot to be able to
Speaker 1: pick up an egg, right, It's something that we don't
Speaker 1: even think about, but this was like a robotics problem
Speaker 1: that was absolutely unsolvable, Like how do you get a
Speaker 1: robot to pick up an egg without without breaking it?
Speaker 1: And we do it without just without thinking about it.
Speaker 1: And we make judgments all the time, just silly judgments
Speaker 1: that we don't think twice about that are so easy
Speaker 1: for the human mind, but that are that we don't
Speaker 1: even realize we're making right things about safety, things about
Speaker 1: just perceptions of the world. So I think that you know,
Speaker 1: humans are much smarter than than we think in like
Speaker 1: in dumb ways, if that makes sense, Like in waste
Speaker 1: that seemed like they're not hard problems, but those are
Speaker 1: actually some of the hardest problems to solve.
Speaker 2: I just think of.
Speaker 3: There being four levels, right, Like one more AI performance
Speaker 3: better than any human, two where AI performs better than
Speaker 3: like all but expert level humans, Three where AI gives
Speaker 3: kind of a passable substitute performance but like you know,
Speaker 3: not quite professional high grade level, and four where it's
Speaker 3: just inept right to the point of being comically inept.
Speaker 3: And like, you know, my heuristic is that like within
Speaker 3: a few years we might have roughly an even divide
Speaker 3: between those four and I'm counting, by the way, tasks
Speaker 3: that involve manipulting the physical environment, which I think AI
Speaker 3: will mostly be pretty bad at. Right, I don't know
Speaker 3: how much I should extrapolate from the poker example. It
Speaker 3: was just so incongruent with these predictions of like imminent AGI, right,
Speaker 3: where a year ago would have said, okay, yeah, of
Speaker 3: course suck at poker. Right, it's not really trained on
Speaker 3: this and it's a hard problem, and ha ha ha, Right,
Speaker 3: But like if you want to have these complicated, like structured,
Speaker 3: nested tasks, like I'm much less worried now in periods
Speaker 3: of oh, let's say five years of AI like being
Speaker 3: able to build like atiscal model to like forecast elections
Speaker 3: or the NFL or whatever else, because that like just
Speaker 3: requires like a superstructure of lots of little tasks, all
Speaker 3: of which are fuck upable, And if you get the
Speaker 3: superstructure wrong too, then you're kind of just drawing dead.
Speaker 3: To use a poker term, right if any if you
Speaker 3: have to chain together twenty steps and any step, there's
Speaker 3: a ninety percent chance you get it right. Well, point
Speaker 3: nine to the twentieth power means you're almost.
Speaker 2: Sure to fuck something up.
Speaker 1: Yeah, fuck upable is a really great word to describe this.
Speaker 1: Nate to kind of to sum this up, you did
Speaker 1: ask chat GBT why it was bad at poker? What
Speaker 1: did it tell you?
Speaker 3: Yeah? No, I mean it's said various things. It said, Look,
Speaker 3: you stressed me out by having me having me have
Speaker 3: to make up these players and things like that.
Speaker 1: Trust it out, You trusted.
Speaker 3: It said the data that it trained on on the
Speaker 3: internet is which seems realistic.
Speaker 1: Yeah, it's a data problem. It's a yeah, it's not me.
Speaker 2: It's you. It's it's complicated.
Speaker 3: Inform about like how like it doesn't realize how important
Speaker 3: stack sizes are because it's just a bunch of tokens.
Speaker 3: Like all the input you give it is generated into tokens, right,
Speaker 3: and like the word the is not very important. The
Speaker 3: fact that Maria has fifty two thousand dollars in chips
Speaker 3: is very important, right, And it doesn't know how to
Speaker 3: distinguish those from its transformer architecture. So like, look, it
Speaker 3: is good at like it is good at verbal reasoning.
Speaker 3: Like again, I I, you know we are talking about
Speaker 3: we don't other segments where we're like amazed by AI progress.
Speaker 3: Like it's very good at verbal reasoning, or at least
Speaker 3: faking that. But in ways I if it's faking, it's
Speaker 3: doing a pretty good job. Right, Like it's very good
Speaker 3: at verbal stuff for the most part. Right, And AI
Speaker 3: is traded on poker, are very good at mimicking Selver solutions,
Speaker 3: right and like and so like and machine learning can
Speaker 3: get you a lot of ways when you have good data,
Speaker 3: but like when you don't have the data in the
Speaker 3: training set and it's not seeming to extrapolate very well,
Speaker 3: and then these complex tasks that require more and more
Speaker 3: compute like, I have not been impressed by deep research,
Speaker 3: which is where it goes away and says I'm going
Speaker 3: to perform several tasks for you that require deeper thought, right,
Speaker 3: and then it takes fifteen minutes you come back and like,
Speaker 3: are you fuck this up? Right? And like, anyway, so
Speaker 3: I think it has affected my priors a little bit.
Speaker 1: Yeah, that's that's really interesting. And I love, by the way, Nate,
Speaker 1: that you know, even though that this is an AI
Speaker 1: which is supposed to kind of be better than humans
Speaker 1: in so many ways, that use such human excuses for
Speaker 1: why it fucked up. You know, you stressed me out,
Speaker 1: you gave me that data. It's not my fault.
Speaker 2: This is hard.
Speaker 1: And on that note, let's let's take a break and
Speaker 1: talk a little bit about Harvard and the other type
Speaker 1: of intelligence, human intelligence. Nay, We've talked on the show
Speaker 1: a few times now about kind of the crusade that
Speaker 1: Donald Trump seems to have against higher education, specifically the
Speaker 1: Ivy League and even more specifically Harvard University. Last time
Speaker 1: we spoke was when basically the administration had threatened to
Speaker 1: pull funding if Harvard didn't comply with a certain set
Speaker 1: of demands. Harvard said fuck you and sued and yeah,
Speaker 1: and tried to go that route, and the funding was frozen.
Speaker 1: And then the administration last week, so we're taping this
Speaker 1: on Wednesday, the twenty eighth of May. So last week,
Speaker 1: the administration decided that it wanted to punish Harvard even
Speaker 1: more because you know, they didn't like the fact that
Speaker 1: Harvard wasn't complying, that it was being defiant, that it
Speaker 1: was taking them to court, and so said, hey, Harvard,
Speaker 1: you can no longer have foreign students. Anyone who has
Speaker 1: visa's effective immediately. Those are going to be revoked. So
Speaker 1: Harvard's class of twenty twenty five that's graduating next week
Speaker 1: is going to be your last basically foreign student class,
Speaker 1: and we're not going to be granting visas anymore to
Speaker 1: those students. Harvard has sued again, so now we have
Speaker 1: a second set of lawsuits. The initial court cases have
Speaker 1: frozen that. But then after that happened in the last
Speaker 1: few days, the administration said Marco Rubio actually said that
Speaker 1: we are going to freeze interviews for all foreign student visas,
Speaker 1: not just Harvard. So they not only you know, went
Speaker 1: after Harvard, but doubled down and said, you know what,
Speaker 1: you can sue us and you can freeze this. But
Speaker 1: if we don't grant the interviews, last laugh is with us.
Speaker 3: Yeah.
Speaker 2: Were they likely to win the lawsuit or are the
Speaker 2: likely to?
Speaker 3: Yeah?
Speaker 1: I think so. I think that Harvard is likely to
Speaker 1: win the lawsuit. Yes, However, from the lee I'm not
Speaker 1: a lawyer, from the legal analysis that I have seen,
Speaker 1: they're likely to win the lawsuit. But Nate, let's remember
Speaker 1: that that requires first of all, a lot of times, right,
Speaker 1: because how many times can we appeal this? And ultimately, well,
Speaker 1: if it goes to the Supreme Court, it ends up
Speaker 1: being these days less of a legal issue and more
Speaker 1: of a political issue.
Speaker 3: Look, I don't think the Supreme Court is likely to
Speaker 3: be very sympathetic to Trump on this type of issue,
Speaker 3: in part because, like you know, clearly they are somewhat
Speaker 3: transparent about like they are not doing it for like
Speaker 3: some security concerning and they're doing it because they don't
Speaker 3: like some of the speech that Harvard is making. But
Speaker 3: it's almost certainly First Amendment protect.
Speaker 1: Yeah, exactly, and I actually think that the So I
Speaker 1: don't know if you read Stephen Pinker's op ed in
Speaker 1: the New York Times, I don't remember what the op
Speaker 1: ed was called, but he termed this Harvard derangement syndrome, right,
Speaker 1: kind of playing off of Trump arrangement syndrome and full disclosure.
Speaker 1: Steve Pinker was my undergrad advisor, so someone I know, well,
Speaker 1: someone i'm you know, I'm still close with, so I'm
Speaker 1: very sympathetic. But I thought it was an excellent way
Speaker 1: of framing what's going on. And he is someone who
Speaker 1: has attacked Harvard. He's a tenured professor at Harvard, but
Speaker 1: he's attacked Harvard many, many times for not standing up
Speaker 1: for free speech, for not protecting conservative viewpoints, for being
Speaker 1: a little bit too woke. Right. He has been one
Speaker 1: of the main people who said, hey, like, we have
Speaker 1: an issue here. But this his op ed was, this
Speaker 1: ain't the way, right, this is not the way to
Speaker 1: solve this problem. And you're actually attacking the students who
Speaker 1: are some of my most critical, thinking, least woke people
Speaker 1: who are on campus.
Speaker 3: But yeah, look, I mean I kind of want to
Speaker 3: zoom out and say, like, what is the equilibrium here,
Speaker 3: Like what is a Trump administration trying to achieve? What
Speaker 3: is Harvard trying to achieve? Like I support maybe some
Speaker 3: listeners will get pissed off, right, you know, I think
Speaker 3: clearly some of these colleges are not complying with like
Speaker 3: the spirit and maybe the letter of the Supreme Court's
Speaker 3: affirmative Action decisions, right, and so like, I wouldn't mind
Speaker 3: if like the Trump administration's like aggressively enforcing the law
Speaker 3: on those or things like diversity statements, which are enforced
Speaker 3: political speech. Although Harvard's a private organization, so it's a
Speaker 3: little bit more complicated, right, But like I you know,
Speaker 3: I don't it's such a blunt tool to like target
Speaker 3: foreign students. And even if you hate Harvard, you know,
Speaker 3: as a patriotic American, I want the best and brightest
Speaker 3: from all around the world to come here and contribute
Speaker 3: to our economy and pay our taxes and everything else, right,
Speaker 3: And like it's just like purely destroying the value of
Speaker 3: these very bright students from helping our economy.
Speaker 1: Yes, it's destroying our intellectual capital. So, by the way,
Speaker 1: when we're taping this right now, I'm actually in Hongo,
Speaker 1: and I just found out today that several universities in
Speaker 1: Hong Kong have offered blanket admissions to all students who
Speaker 1: can show that they were admitted to Harvard and can
Speaker 1: no longer go because they are a foreign student. They said,
Speaker 1: you don't even have to apply, you can just come here,
Speaker 1: and these are some of the best universities in Hong Kong.
Speaker 1: And that's super smart, right, Like, universities all over the
Speaker 1: world should be doing stuff like that right now, because
Speaker 1: what the administration is doing is kind of bankrupting at
Speaker 1: the future of kind of intellectual development in the country
Speaker 1: by saying, oh, foreign students, you know, we've we've just
Speaker 1: frozen all these applications. So to me, like that's super smart, right,
Speaker 1: Like that is the we've we talked about this several
Speaker 1: months ago, Nate, Right, what can China do to capitalize
Speaker 1: on the moment it's doing it. It's being like, come here,
Speaker 1: like you don't even have to apply, you don't have
Speaker 1: to do anything to show us the letter that showed
Speaker 1: you were accepted and welcome. Come.
Speaker 3: Yeah. I went to London School of Economics for my
Speaker 3: junior year and if I were them, i'd be doing
Speaker 3: cart wheels. And they always got a lot of foreign students.
Speaker 1: Right absolutely, I mean I think everyone should be doing
Speaker 1: this right now, and I just do not see I
Speaker 1: mean a lot of these things. At this point, it
Speaker 1: just derangement syndrome seems right, because there does not seem
Speaker 1: to be any strategic value to this. There's not any
Speaker 1: any value in terms of trying to get at the
Speaker 1: types of problems that the Trump administration says it wants
Speaker 1: to get at, because it's not even addressing that right,
Speaker 1: Like at the originally anti Semitism was the pretext for this,
Speaker 1: and this is just like so far divorced from it.
Speaker 1: Everyone is having their visas revoked or not granted, or
Speaker 1: their interview is not even granted. So it's just, you know,
Speaker 1: the pretense is now crumbling. We always knew it was
Speaker 1: just a pretense, but at this point, it's just not
Speaker 1: accomplishing anything other than to further could of create this
Speaker 1: absolute chasm in higher education and future brain power, future
Speaker 1: research ability, future creativity of the United States basically mortgaging
Speaker 1: its future right saying that we were going to give
Speaker 1: up one of the biggest, if not the biggest edge
Speaker 1: that the United States has had historically, which is kind
Speaker 1: of this brain power creativity place where people can can
Speaker 1: develop and find support for for for their ideas, and
Speaker 1: now that is not possible and if this actually stands, right,
Speaker 1: if foreign students are not allowed into US universities, because
Speaker 1: right now, like I said, this interview process being paused
Speaker 1: for everyone, not just Harvard. I mean, that is going
Speaker 1: to be just detrimental in so many respects to what
Speaker 1: happens here.
Speaker 3: Yeah, the next So Harvard is not a very sympathetic
Speaker 3: case for reasons that are partly they're full right, but
Speaker 3: like you know, so far, the Trump admin has been
Speaker 3: pretty smart about kind of which schools they they pick on.
Speaker 3: I mean, I don't know what their in game is
Speaker 3: here or what Harvard's is really you know, JD. Vans
Speaker 3: had it tweet. I don't know where he was like,
Speaker 3: he's oddly transparent sometimes about his thinking, and he's like,
Speaker 3: well look just like, look, these colleges aren't serious about
Speaker 3: complying with the law citing like the students for fair admissions.
Speaker 3: I think it's called which was the affirmative action decision,
Speaker 3: which I do think they've kind of not been serious
Speaker 3: about complying with in some cases case by case basis,
Speaker 3: and so like, unless we kind of show real muscle,
Speaker 3: then what can we do. We have to fight them
Speaker 3: because they're in the wrong, and so we're overreaching. It
Speaker 3: is a subtext of that, right, which is like maybe
Speaker 3: not crazy and they have a real leader because Harvard
Speaker 3: isn't that sympathetic perceptions of higher education that's gone way down,
Speaker 3: so it's like harder to marshall public opinion on the
Speaker 3: side of this, right, and like and like, you know,
Speaker 3: I saw some Harvard professors on Twitter. Ever we can
Speaker 3: be like this is the greatest threat to American I'm like,
Speaker 3: please don't say that, because it's it's it's maybe more
Speaker 3: of a threat to American competitoris and not sure it's
Speaker 3: a threat to democracy per se.
Speaker 1: Right, No, I think there are bigger threats to democracy.
Speaker 3: But like, but what's Harvard's end game? Like I think, ok,
Speaker 3: if they could go back and tone down some of
Speaker 3: the things say down for the past ten years, and
Speaker 3: I think they maybe would, but like now, I mean,
Speaker 3: if they back down, then they look weak and that
Speaker 3: also might hurt their recruitment own nothing, they'd have real problems.
Speaker 3: But like, what if you're the president of Heart What's
Speaker 3: name Alan Garber? Is that right? Yep? If you're Alan Garber, Maria,
Speaker 3: what do you do right now? And you know Harvard?
Speaker 1: Yeah, yeah, I mean, you're in a horrible position. I
Speaker 1: think you're doing what you can, which is trying to
Speaker 1: stand up to the Trump administration. So Alan Garber is
Speaker 1: trying to actually walk a very fine line. He's trying
Speaker 1: to stand up to Trump, and he's you know, spearheading
Speaker 1: these lawsuits, and he is leading committees into the claims
Speaker 1: of anti Semitism and kind of and discrimination on campus,
Speaker 1: the anti wokeness. So he is actually trying to address
Speaker 1: all of these things in different ways. And I have
Speaker 1: no idea, you know, how successful that will end up being,
Speaker 1: but it is. It's a it's a tough you know,
Speaker 1: he's been put in a really tough spot because obviously
Speaker 1: he hasn't been president for long. The last president departed
Speaker 1: on lessons tellar circumstances, as did a number of presidents
Speaker 1: of Ivy League institutions, and you know, you're picking up
Speaker 1: the presidency at a very difficult point in time where, yeah,
Speaker 1: you have to acknowledge that Harvard has made mistakes, right,
Speaker 1: not just Harvard, a lot of universities I think were
Speaker 1: trying to be I don't even want to use the
Speaker 1: word woke, because I don't think that's accurate. Here but
Speaker 1: we're becoming, you know, places where people were afraid of
Speaker 1: voicing their minds. And I wrote, I think almost a
Speaker 1: decade ago, I wrote a piece for The New Yorker
Speaker 1: about basically, you know, the problems in academia that come
Speaker 1: from not having enough conservatives right that it actually has
Speaker 1: trickle down effects in research and the types of things
Speaker 1: that are being researched in the findings that are allowed
Speaker 1: to be voiced, and that it can be incredibly problematic.
Speaker 1: And so I think you need to acknowledge that, and
Speaker 1: those are things that need to be rectified. At the
Speaker 1: same time, you are currently fighting this battle against you know,
Speaker 1: Donald Trump, who's trying to destroy higher education in the
Speaker 1: United States, and so that is a you know, overriding priority,
Speaker 1: which doesn't dismiss all of the things that have gone
Speaker 1: wrong and all of the things that are wrong with academia,
Speaker 1: but it's just a it's a kind of this fine
Speaker 1: balance where you're like, yeah, I know I'm not sympathetic
Speaker 1: because I've fucked up and I've done all of these things.
Speaker 1: And yet right now I'm one of the people best
Speaker 1: positioned to fight this, and so let's just try to
Speaker 1: figure out how to prioritize this and how to not
Speaker 1: spread ourselves tooth in so that we can actually protect
Speaker 1: higher education and then start to remedy the problems that
Speaker 1: we have internally, because I do think those are problems
Speaker 1: that need to be remedied, right, Like, you can't just
Speaker 1: be like, well, now we're all united against Trump, so
Speaker 1: let's forget we ever did anything wrong. I think that
Speaker 1: all of these things you remember, Nate, like less than
Speaker 1: you know. Like six months ago, I was railing against
Speaker 1: Harvard because I was really pissed at the way they
Speaker 1: were reacting to a lot of things. Now I think
Speaker 1: now I'm supporting it because they're being attacked. It's the
Speaker 1: classic psychology thing, right where you can actually unite a
Speaker 1: lot of people who who criticize you when you attack, right,
Speaker 1: all of a sudden, these various factions, when they feel
Speaker 1: these life threatening attacks from the outside, all of a sudden,
Speaker 1: these factions start uniting. And I think that's what's happening
Speaker 1: at Harvard right now, as it should be. But we
Speaker 1: still have to realize that, you know, there are all
Speaker 1: of these different issues and it's a really I mean,
Speaker 1: it's a shit show. That's a technical term.
Speaker 2: Nate yeah, Look, I'm not afraid to use a term woke.
Speaker 3: I mean, you know, critical race theory and intersectionality, all
Speaker 3: these things kind of come from an extremely academic context, right,
Speaker 3: And Look, the thing I'm probably most concerned about is
Speaker 3: the fact that I think the quality of academic research
Speaker 3: is often has lots of problems, but like political bias
Speaker 3: in predictable directions is among those problems. I'm trying to
Speaker 3: think of a politically experienced solution that the Trump administration
Speaker 3: might like and that people would find tolerable. On the
Speaker 3: Harvard side, Right, we'll collect data on the political affiliation
Speaker 3: of our students and faculty. We will make concerted efforts
Speaker 3: to I'm not going to try to frame this right
Speaker 3: conservati efforts like higher conservative scholars. Right, maybe you have
Speaker 3: like you know, if you have an ethnic studies department,
Speaker 3: you have a conservative stay.
Speaker 1: No, I mean that isn't that you know exactly what
Speaker 1: people have been trying to reverse by kind of with
Speaker 1: the affirmative action rulings, right, that you have quotos for
Speaker 1: certain types of people. So I think that the better
Speaker 1: way to do it is basically having blind hiring, just
Speaker 1: like you have blind admissions. You are willing. You just
Speaker 1: don't you don't ask any of that, and you don't
Speaker 1: disqualify people because they have conservative leanings, right, and that
Speaker 1: it's not even it's not a thing because you don't
Speaker 1: really care, Like I don't care if my uh, you know,
Speaker 1: neuroscience professor, Well maybe not neuroscience as insofar as I
Speaker 1: study psychology, but if my astrophysics professor is, you know,
Speaker 1: what their political views are, as long as they correctly,
Speaker 1: you know, as long as they're one of the best
Speaker 1: astrophysicists in the world. So so I don't think that
Speaker 1: that's you know, that what you're proposing is affirmative action
Speaker 1: for politics.
Speaker 3: I'm just saying, like it's slightly stupid, but like maybe
Speaker 3: maybe jd.
Speaker 2: Vance would like it.
Speaker 1: But but you're saying, like do something that he would like.
Speaker 1: Columbia tried to do everything and had all of their
Speaker 1: funding frozen anyway, right, So like this is the problem
Speaker 1: that like actually doing trying to appease is not the
Speaker 1: correct play here. I think that's actually the completely wrong
Speaker 1: game theoretical way to respond here. That's because it's not working.
Speaker 3: And that's a meta thing across everything that the White
Speaker 3: House does. You see it playing out Ontari's too, where
Speaker 3: on the one hand, they reverse course and undercut themselves
Speaker 3: all the time, right, you know, and they're sloppy enough
Speaker 3: where they probably maybe if they're doing this right, they
Speaker 3: could have had more likelihood of success in the courts
Speaker 3: than they have. Right, on either hand, they don't necessarily
Speaker 3: hold up their quid pro quote. This is kind of
Speaker 3: very transactional and explicit, right, So they're not even like
Speaker 3: presenting clear enough information to make themselves easy to bargain with,
Speaker 3: even if it were some theoretical optimal solution or win win.
Speaker 1: Yeah. So I mean, so I guess, you know, we're
Speaker 1: just in a very sticky spot and we'll have to
Speaker 1: see how it plays out. I think Harvard is doing
Speaker 1: its best, and you know, I wish that I could
Speaker 1: give it, you know, a playbook, Harvard, this is what
Speaker 1: you should be doing, but but I can't. So let's
Speaker 1: just let's hold out hope. I think you are.
Speaker 3: I mean, there are real changes that like, yeah, Harvard should, and.
Speaker 1: I think Stephen and I think Stephen Pinker has advocated
Speaker 1: for a lot of them. Like, as I said, I'm biased,
Speaker 1: but I think that Harvard and Harvard should listen to
Speaker 1: Steve Pinker. That's my advice. I think he's one of
Speaker 1: the smartest people I know who actually knows a lot
Speaker 1: about this and has very rational views and can advise
Speaker 1: on this. It is almost eleven pm for me in
Speaker 1: Hong Kong, so I think we're gonna rap it for
Speaker 1: this week. The World to Years of Poker has already started.
Speaker 1: You and I are not going to be out there
Speaker 1: for another It's another basically two weeks, I think for
Speaker 1: us week and a half. So let's wish all of
Speaker 1: our Risky Business listeners who are already in Vegas for
Speaker 1: the World Series of Poker good luck. We hope that
Speaker 1: you all crush it at the tables and we're looking
Speaker 1: forward to joining you soon. Let us know what you
Speaker 1: think of the show. Reach out to us at Risky
Speaker 1: Business at Pushkin dot fm. And by the way, if
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Speaker 1: Risky Business is hosted by me Maria Kannakova.
Speaker 3: And by me Nate Silver. The show is a co
Speaker 3: production of Pushkin Industries and iHeartMedia. This episode was produced
Speaker 3: by Isabelle Carter. Our associate producer is Sonia Gerwin. Sally
Speaker 3: Helm is our editor, and our executive producer is Jacob Bolstein.
Speaker 2: Mixing by Sarah Bruger.
Speaker 1: Thanks so much for tuning in.