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. Time for an AI episode, I think, Aria, Yeah, it's.
Speaker 1: It's been a minute since we had AI related conversations
Speaker 1: on the show. But it's obviously a hugely important topic
Speaker 1: and one that should have been more in the news
Speaker 1: last week it got you know, got sidelined by other news.
Speaker 1: But who's that a guy don't don't know, don't know
Speaker 1: Nate but never never met him. That's true, never met him.
Speaker 1: But yeah, well, we'll talk a little bit about the
Speaker 1: AI action plan that was released by the US government
Speaker 1: last week. We have lots to talk about before we do. Nate,
Speaker 1: we're both back in New York, but I'm actually flying
Speaker 1: back to Vegas in twenty four hours to play in
Speaker 1: the twenty five k NBC Heads Up Championship.
Speaker 2: Yeah are you? Are you excited? Have you been coaching?
Speaker 3: I have?
Speaker 1: I have so For people who don't know, this was
Speaker 1: a big show back in the day. It was before
Speaker 1: my time, so I never watched it. Nate, did you
Speaker 1: ever watch it?
Speaker 2: Sure? Yeah, yeah, so time, you're not that much younger.
Speaker 1: I didn't know anything about poker. It was before my time,
Speaker 1: like actually knowing anything about poker, So I didn't watch
Speaker 1: any poker, know about the existence of any poker shows
Speaker 1: before I started researching Biggest Bluff. So that's what I mean,
Speaker 1: before my time, before my time in the poker world.
Speaker 2: Had you heard of poker only because of rounders?
Speaker 1: I knew that technically it was a game that existed,
Speaker 1: but it's not something that was ever really on my radar.
Speaker 1: But yeah, this was a huge show on NBC back
Speaker 1: in the day. Heads Up poker is one on one poker,
Speaker 1: which is kind of the most competitive in some ways
Speaker 1: form of poker because you're forced to play all hands.
Speaker 1: Basically your ranges are one hundred percent, so it forces
Speaker 1: a very different kind of thinking. And it's being rebooted
Speaker 1: in conjunction with Peacock and Poker Stars and Poker Goes
Speaker 1: kind of is helping sponsor all of this. So I
Speaker 1: am yeah, I'll be in the Poker Ghost Studios taping
Speaker 1: and I hope it goes well. I've been getting coaching
Speaker 1: from Kevin Rabichow, who's considered Yeah, he's considered one of
Speaker 1: the best heads up players in the world. He has
Speaker 1: a new heads up coaching course that just came out.
Speaker 1: But yeah, so I've been I've been really trying. You know,
Speaker 1: usually after the World Series, I'm like, okay, no poker
Speaker 1: for a while. Right, it's not gonna just gonna clear
Speaker 1: my my clear my brain.
Speaker 3: I'm in the no poker zone for a period of time.
Speaker 3: We're also bad heads up to be frank, which was
Speaker 3: some coaching might have been good, but like, yeah, I
Speaker 3: can't can't make the time commitment this summer in.
Speaker 1: Yeah, so instead of taking my usual break from from
Speaker 1: poker that I do, I've been instead like going hard
Speaker 1: and trying to prepare myself for this heads up Championship.
Speaker 1: And you know it, heads up, the variants so much higher,
Speaker 1: and the card distribution matters so much, right, So, like no,
Speaker 1: and if someone is just getting hands, that's kind of
Speaker 1: you know, that's it, Like they're they're potentially going to win.
Speaker 1: And there have been lots of times where really great
Speaker 1: players have been just completely obliterated by someone who doesn't
Speaker 1: know how to play. Yeah, so card distribution can be
Speaker 1: a bitch heads up, or it can be amazing.
Speaker 2: Are those exclusive? Uh No, Okay, I'm not going to
Speaker 2: get into it.
Speaker 1: Yeah, no, bitchin amazing can be can be within the
Speaker 1: same match, but it does matter much more so than
Speaker 1: in full ring poker. I saw some of the names
Speaker 1: that we'll be playing, and there's gonna be no spoilers obviously. Well,
Speaker 1: I haven't played yet, but I'm not going to be
Speaker 1: able to tell you how I did until the show
Speaker 1: airs in the fall. Yeah, since it's not it's not
Speaker 1: airing in real time, but you will. People do know
Speaker 1: who's playing. And if I'm paired with someone like Jason
Speaker 1: Kuhn on my first match, yeah, that's that's not going
Speaker 1: to be fun. And if I'm paired with Don Cheetle,
Speaker 1: who's also playing, I think I'm gonna be much happier.
Speaker 2: Yeah.
Speaker 3: I didn't realize there that many not Fish but celebrities
Speaker 3: that maybe Fish adjacent potentially, although he plays a little
Speaker 3: bit right he does.
Speaker 1: Yeah, so so we'll We'll just see and I'm excited
Speaker 1: for it no matter how it goes. And Kevin has
Speaker 1: been very good at preparing me for both heads up
Speaker 1: and also for the eventuality that he as one of
Speaker 1: the best and at times probably the best in the world,
Speaker 1: has sometimes busted in the first round right in the
Speaker 1: matchups like this, because he plays a twenty five K
Speaker 1: heads up at the World Series every year.
Speaker 3: Are you worried about like physical reads and things like that,
Speaker 3: because like that can be more of a factor too.
Speaker 1: Yeah, you know, so I tend to be you know,
Speaker 1: I hope that people can't get a lot of reads
Speaker 1: off of me, but you just never know. And this
Speaker 1: is actually hilarious night the rules of this. When I
Speaker 1: started reading it, I just burst out laughing because they
Speaker 1: said that you are allowed So people who don't play poker,
Speaker 1: this is not normal. You're allowed to expose one or
Speaker 1: both of your cards at any point during a hand.
Speaker 2: You can cash usually but not in turn.
Speaker 1: But this, yeah, this is a tournament. Yeah, so you
Speaker 1: can expose your cards one or both of them. And
Speaker 1: they said they encourage speech play. You can talk about
Speaker 1: the contents of your hands. So basically everything are not
Speaker 1: allowed to do in tournaments normally you're being encouraged to
Speaker 1: do to make good television. I'm not going to be
Speaker 1: showing my cards day.
Speaker 3: Okay, I've become more of a speech play guy over time.
Speaker 1: No really, yeah, okay, okay, give me an example.
Speaker 3: You know, like the Europeans, I'll like try to get
Speaker 3: info out of them. Were so quiet all the time
Speaker 3: that like I want, like, I just want to like
Speaker 3: any vibe I can get pretty much, or with like
Speaker 3: really bad players who like often will reveal information just
Speaker 3: to gauge their comfort level. It's kind of been a
Speaker 3: proto trial and error Zoka. For right now, I'm trying
Speaker 3: to like kind of like collect mental database of like
Speaker 3: how people react to different things.
Speaker 2: I don't know, it's kind of I mean, because so.
Speaker 3: Many decisions are so close, yeah, equilibrium that like any
Speaker 3: vibe you can get, And I like recently cash games
Speaker 3: more than tournaments and they're kind of looser in various
Speaker 3: senses of the term, right, people like you reveal more
Speaker 3: info voluntarily or involuntarily.
Speaker 2: I like cultivating that environment. I think.
Speaker 1: Yeah, no, I'm someone who definitely is chatty and tends
Speaker 1: to be like very friendly at poker tables. But you know,
Speaker 1: I'm not someone who's going to be like, oh, you know,
Speaker 1: do you have aces, like do you do things like
Speaker 1: that or is it because you know there are players
Speaker 1: who do or is it something a little bit different
Speaker 1: just to try to engage.
Speaker 3: Yeah, maybe it's like you're would have decided to fold
Speaker 3: already if they want to like reveal something that they
Speaker 3: might not reveal otherwise, or to send a bassline for
Speaker 3: the next time we play a hand, things like that.
Speaker 1: Yeah, that's that's actually smart.
Speaker 2: So how can you reveal afterward? Right?
Speaker 3: If you're friendly in the right way, they might reveal
Speaker 3: information later on.
Speaker 2: Right for sure.
Speaker 1: Yeah, that's actually something that I have capitalized on because
Speaker 1: I am a friendly player. Like I'll sometimes when it's obvious,
Speaker 1: like I've made a big fold or something like that,
Speaker 1: and I like make it obvious, they'll show me. And
Speaker 1: that's that's very nice because it's good information. But I'm
Speaker 1: always I do try to be careful, and I tell
Speaker 1: people this, you know, with with poker and with just
Speaker 1: like reads and our ability to spot deception in general,
Speaker 1: that we do tend to be not great at it
Speaker 1: as human beings. And the other kind of the other
Speaker 1: element of that is that we think we're better than
Speaker 1: we are. And we also often reveal information when we're
Speaker 1: engaging in speech play without realizing it because of how
Speaker 1: we're talking, what we're doing. And so it's something that
Speaker 1: I think just in general, with the psychology of the thing,
Speaker 1: you have to be careful.
Speaker 2: It's all contextual.
Speaker 3: Any poker tell speech play, it's all about semantic context,
Speaker 3: so to speak for sure.
Speaker 1: For sure. On that note, Nate, shall we switch gears
Speaker 1: and talk about AI and the news from last week
Speaker 1: which got buried because there's a lot of news in
Speaker 1: the news. There's a lot of news in the news
Speaker 1: these days, Nate, that's a very deep insight. So last
Speaker 1: week President released the AI Action Plan that talks about
Speaker 1: what the administration wants to kind of see going forward
Speaker 1: in the AI space. And I think the name of
Speaker 1: this action plan is actually quite telling about the contents.
Speaker 1: It says, winning the Race America's AI Action Plan. So
Speaker 1: it's titled winning the Race. And that's how it's framed, right,
Speaker 1: It's framed as a race between the US and everyone else,
Speaker 1: especially China, but really everyone else, mostly China, but mostly no,
Speaker 1: there isn't. And the overall point of all of the
Speaker 1: different sections is how can we make the US the
Speaker 1: leader in AI the most competitive and how can we
Speaker 1: do that by also kind of screwing over China in
Speaker 1: some respects by not allowing it access to the types
Speaker 1: of things that will allow our allies to access and
Speaker 1: some of the things in it are contradictory, but that's
Speaker 1: kind of the overall message. Did you read the report night,
Speaker 1: Do you have any initial thoughts?
Speaker 2: So?
Speaker 3: V Maschwitz is one of the most prominent and best
Speaker 3: AI bloggers. He writes the substack, don't worry about the vase,
Speaker 3: and he calls it as he sees that he's more
Speaker 3: concerned about ped doom than a lot of people. P
Speaker 3: doom is the possibility if things going really badly, extinction,
Speaker 3: loss of control, et cetera. And he said he thought
Speaker 3: the plan was actually his term pretty good. I don't
Speaker 3: know his politics in detail, but he really dives down
Speaker 3: into like every last word you can read that. So
Speaker 3: I'm kind of relying on that for like general vibes, right.
Speaker 3: I mean, it is interesting that like, even like even
Speaker 3: people who are concerned like V about AI safety, deeply
Speaker 3: concerned in his case, have this ambiguous relationship with China,
Speaker 3: right where if we were the only country developing AI
Speaker 3: and we had like centralized control, then it would be
Speaker 3: easier to argue that Okay, let's speak appropriately cautious here,
Speaker 3: take it slow right with China. You know a lot
Speaker 3: of people I don't want it to be of you
Speaker 3: to him I do think that, well, we have no
Speaker 3: choice but to race, and maybe we can race in
Speaker 3: ways that involves some degree of coordination or cooperation. It's
Speaker 3: hard to run races like that. But you know, I
Speaker 3: mean the notion of like securing American leadership instead of
Speaker 3: seeding it to China, I don't think is terribly controversial.
Speaker 3: I mean there's a lot in the in the in
Speaker 3: the in the plan though, right, there are some Trumpian
Speaker 3: elements around factors like the political orientation of AI models,
Speaker 3: right where they don't want them to be too woke,
Speaker 3: They don't want them to be too political quote unquote right.
Speaker 1: Yeah, which is one of the things which is hard,
Speaker 1: Which is hard, and which is one of the things
Speaker 1: that ZV does point out because I read this as well,
Speaker 1: it's one of the one of the things that he
Speaker 1: points out is actually probably not possible given the type
Speaker 1: of language that that Trump employs, and that as a psychologist,
Speaker 1: I would say is not possible given how AI is developed,
Speaker 1: because there's just there's bias everywhere, and there's no such
Speaker 1: thing as like this is the objective thing, and if
Speaker 1: you're constantly policing quote unquote anti woke, even if that's
Speaker 1: kind of the unbiased position. But you're like, oh no,
Speaker 1: that's you know that, then you're going to it's problematic.
Speaker 1: It's problematic in terms of how your how you go
Speaker 1: about it.
Speaker 3: Yeah, with with Groc or more technically, the Twitter bought Groc,
Speaker 3: which is an instance of Elon Elon's overall modeled rock. Right,
Speaker 3: Elon just did a couple of like subtle things. It
Speaker 3: was like, oh, don't be too woke here, don't trust
Speaker 3: the media too much. Right, if you read the actual
Speaker 3: changes to the archae texture, they weren't that profound, And
Speaker 3: yet Kroc was calling itself. I believe the term is
Speaker 3: Mecha Hitler after some period of time engaging in elaborate
Speaker 3: rape fantasies. I wish this were a joke, but it's not.
Speaker 3: And like so yeah, I mean if you kind of
Speaker 3: nudge a model to override its inputs, it's hard to
Speaker 3: do so in a way that like has any type
Speaker 3: of dexterity. I mean Google had this problem too, right
Speaker 3: when they got to Gemini two years ago, a year
Speaker 3: and a half ago, right, they were like, oh my gosh, yeah,
Speaker 3: people are there's too many like white people, white men
Speaker 3: in these photos, So like literally it was forced to
Speaker 3: draw like multi racial Nazis.
Speaker 2: It always comes down to Nazis.
Speaker 3: And by the way, why well, in part because like
Speaker 3: if you're kind of mining text of arguments on the
Speaker 3: Internet and on Twitter, people invoke Nazi comparisons all the time.
Speaker 2: Right, Who's hot Sydney Sweeney?
Speaker 3: Right, there was some new American ego or commercial where
Speaker 3: like she's inn am pair of gens and she talks
Speaker 3: about how she is good genes. A little weird, right,
Speaker 3: but like it was kind of Nazi. It was a
Speaker 3: little weird. However, idiots on the internet were like, this
Speaker 3: has Nazi symbolism in it, you know, not just alluding
Speaker 3: to that saying the word Nazi. So if you're like groc,
Speaker 3: you're like, okay, well you want like kind of the
Speaker 3: garden variety political epitaph and calling people Nazi or Hitler,
Speaker 3: which should be more forbidden, you would think, But that
Speaker 3: taboo is a violated all the time. And so an
Speaker 3: AI trained on political speech, if you go and say, yeah,
Speaker 3: don't be so woke here, right, Well, can kind of
Speaker 3: go into like a downward spiral election people do where
Speaker 3: before long it's calling everybody or itself hitler.
Speaker 1: Yeah, no, I think that that's absolutely true, and you
Speaker 1: know it also your your point actually makes a broader
Speaker 1: point about this AI action play, and where a lot
Speaker 1: of the things and I did skim through it. I
Speaker 1: didn't read the whole thing word for word. It is
Speaker 1: small print on twenty eight pages, and I had other
Speaker 1: things to do this week, so I just prepare for
Speaker 1: the Heads Up Championship. So I didn't read the whole thing,
Speaker 1: but a lot of it actually, like there are things
Speaker 1: that sound reasonable in theory, but there's no practical way
Speaker 1: of like how is this actually going to happen? How
Speaker 1: do we make this happen? And so you know, I
Speaker 1: think that this is a very concrete this like anti
Speaker 1: woke is a very concrete example where like, okay, well,
Speaker 1: even if in theory we're saying we want everything to
Speaker 1: just be unbiased, let's like, let's just remove anti woke, right,
Speaker 1: we want everything to be like as unbiased as possible.
Speaker 1: Let's pretend that that's actually what it said, even though
Speaker 1: it didn't. It did use the terminology anti woke, and
Speaker 1: then there's even an anti woke executive order after it.
Speaker 1: Let's pretend it didn't, Like, let's just pretend it was
Speaker 1: we do not want bias. Great, I agree with that.
Speaker 1: How do you implement it? Like, how do you actually
Speaker 1: like do those weights? How do you It's it's base
Speaker 1: sickly impossible. If not, like, it's a ridiculously thorny problem.
Speaker 1: And that is just a very specific thing that you
Speaker 1: can kind of grab onto it to realize how a
Speaker 1: lot of these statements might sound good and like, Okay,
Speaker 1: well this is a reasonable aim, but if you don't
Speaker 1: actually have the implementation down and have the nitty gritty
Speaker 1: of how to do it, it's pointless. It's just words,
Speaker 1: it's just rhetoric. And we'll be back right after this.
Speaker 1: There's another really interesting part of this action plan that
Speaker 1: I think we should talk about because it was something
Speaker 1: that the Biden and administration also struggled with, which is
Speaker 1: what do we do in terms of proprietary versus open
Speaker 1: source models?
Speaker 2: Right?
Speaker 1: Do we encourage people to kind of share all the
Speaker 1: code so that anyone can use it and anyone can
Speaker 1: kind of build on top of it, or is this
Speaker 1: something that we want to kind of keep inside, kind
Speaker 1: of keep internal. China went open source right almost immediately
Speaker 1: with kind of deep seak all of that those are
Speaker 1: open source models, and in the US, the Biden administration
Speaker 1: didn't really like they just like punted on it. They
Speaker 1: didn't they didn't have a position. And we do have both, right,
Speaker 1: we have proprietary models, we have some open source stuff,
Speaker 1: and there are arguments on both sides. But you had
Speaker 1: started off by talking about p doom, which is not
Speaker 1: something this report mentions at all, and one of the
Speaker 1: arguments against having everything be open sources. Well, okay, great,
Speaker 1: but then you know, rogue actors, people who don't have
Speaker 1: the best interests of the United States in mind will
Speaker 1: have this code as well. It's kind of the same
Speaker 1: arguments that have been made in the past with like
Speaker 1: when people provide the full genomic sequence of like deadly viruses,
Speaker 1: right or things like that, are you know, instructions for
Speaker 1: how to build kind of a bomb was step by step?
Speaker 1: Like hey, guys, like, why are we actually making all
Speaker 1: of this information publicly available? So that's kind of I
Speaker 1: think the strongest case against open source. There's a lot
Speaker 1: of pro let's let's talk about con first.
Speaker 3: Yeah, well, look, I think part of it was there's
Speaker 3: a period of time where I think the AI safety
Speaker 3: folks thought that hey, maybe we could just kind of
Speaker 3: contain it to open AI, Anthropic and Google or like
Speaker 3: or have been like the Big three, right, and maybe
Speaker 3: that we can avoid like a race dynamic if there's
Speaker 3: a finite number of players and they all believe in
Speaker 3: AI safety, right, and I think now a there's like
Speaker 3: less belief that like open AI in particular, is that
Speaker 3: concerned about AI safety. It's a legitmission, right, you know.
Speaker 3: B we've seen with deep Seek the Chinese model that
Speaker 3: like with some clever engineers, you can probably have a
Speaker 3: model it's like only six months to a year behind
Speaker 3: the frontier, probably not on the frontier. Sottitudes have changed
Speaker 3: again kind of as a resignation to the inotability, I
Speaker 3: think in part and also there is some protectionism, right,
Speaker 3: Like Meta has championed open models. Why well, partly because
Speaker 3: Meta's models don't suck, but they've been considered behind to
Speaker 3: the point where they're literally offering people AI engineers, in
Speaker 3: one case, allegedly, according to a Wired as much as
Speaker 3: a billion dollars to join Meta so they can catch.
Speaker 2: Up and become like a fourth part of the Big four.
Speaker 2: I guess it would be, right.
Speaker 3: So yeah, I mean, when it comes to election analysis
Speaker 3: and other models. Oftentimes, I found the people in that
Speaker 3: space who are advocating for total open source everything are
Speaker 3: people that like A don't need the income or be their.
Speaker 2: Models kind of suck, right. They're like, we're doing this
Speaker 2: really good the community.
Speaker 3: It's like, well, you're doing that because you're not gonna
Speaker 3: it's not really good enough to sell that product for
Speaker 3: a premium price, whereas I think mine is for example. Right, So,
Speaker 3: like there are all types of ulterior motives. There's regulatory capture,
Speaker 3: right of course Sam Milton might say, well, yeah, we
Speaker 3: have to be regulated. We can't have any and pop
Speaker 3: Shop burning an AI model, so therefore just just us three, right,
Speaker 3: just as Google and a thropic, and therefore we capture
Speaker 3: all the market share in perpetuity.
Speaker 1: Yeah. So so the the incentives here are definitely not
Speaker 1: as aligned as as one might think. And I think
Speaker 1: you you're also kind of subtly raising another point here,
Speaker 1: which is that in general, and stop me if you disagree,
Speaker 1: But it seems like over the last year, I sure
Speaker 1: like people are you know, worried about AI and like
Speaker 1: p doom and all this, but it seems like that
Speaker 1: discourse has actually quieted down and instead people are like, yeah,
Speaker 1: let's let's be more competitive with China. Okay, fuck it, Like,
Speaker 1: let's let's let's try to push to like do what
Speaker 1: we can to like to make it really good. And
Speaker 1: I think that people have actually just maybe it's because
Speaker 1: they've become, you know, just more used to having AI
Speaker 1: whatever it is. It feels like the safety concerns have
Speaker 1: taken kind of a secondary role to capitalistic and competitive instincts.
Speaker 3: I think there is maybe some view that, like this
Speaker 3: year has not seen as much progress in AI timelines
Speaker 3: as people would have thought back in January, that might
Speaker 3: be debated. I mean, I think people are also like
Speaker 3: a little reluctant to talk about this in the AI
Speaker 3: safety community because they don't want to make it seem
Speaker 3: like their guards are down right, But that's my sense,
Speaker 3: right almost more about what's being kind of on said now. Also,
Speaker 3: people can get bored of saying the same thing over
Speaker 3: and over. There's a new book out very soon about
Speaker 3: how AI is going to kill everybody if we don't
Speaker 3: learn how to control it quickly, right from people Olosio
Speaker 3: Yukowski and need Sours who are doomers, but very smart
Speaker 3: people widely respect to me, we'll have one of them
Speaker 3: on at some point. I don't know, right, but yeah,
Speaker 3: and I think it's kind of like also maybe a
Speaker 3: view that like, Okay, this technology is interesting and economically
Speaker 3: powerful and kind of fun, right, so we can't talk
Speaker 3: about the same thing all the time.
Speaker 2: I don't know, it's my that's my impression.
Speaker 1: Yeah, whatever, whatever the cases, whatever's going on behind the scenes.
Speaker 1: I do feel like, well, this report, the AI report,
Speaker 1: didn't really mention it at all, right, Like there there
Speaker 1: was very little talk about safety or concerns, and there
Speaker 1: was some hand waving too, like oh, you know, if
Speaker 1: anything seems like it's you know, getting bad, we'll fix it.
Speaker 3: But even Gavin knwso veto to bill last year called
Speaker 3: SS Yeah, I don't know which number it was, right
Speaker 3: that would have regulated all the AI labs in California,
Speaker 3: since they're all currently have substantial economic nexus in California,
Speaker 3: they would have all have been affected by it. So
Speaker 3: I don't know, right, Maybe it's like a lot of
Speaker 3: things where something really bad has to happen, and what
Speaker 3: people like Aliozier are concerned about that by the time
Speaker 3: the bad things happen, it's it's too late. Right again,
Speaker 3: I'm I'm skeptical of the idea of like a super
Speaker 3: intelligence explosion quote unquote. We talked in the Poker episode
Speaker 3: a month or so ago about how like AI progress
Speaker 3: has been patchier. It's already superhuman in some areas, it's
Speaker 3: kind of deficient in others. Right, and as a near
Speaker 3: daily user of these products, right, I mean, there are
Speaker 3: times where I think it's thinking and being very smart,
Speaker 3: in times when it can't fucking add numbers together, right,
Speaker 3: So and so like, in some ways, the Groc thing
Speaker 3: was scarier than like the Google thing, because like Elon
Speaker 3: had like very restively gentle changes for the architecture produced
Speaker 3: these profound changes where it was literally calling itself Mecha
Speaker 3: Hitler in outputs.
Speaker 1: Right.
Speaker 3: At the same time that gets to the race dynamic, right,
Speaker 3: that model had not been because you know what happens.
Speaker 3: You program an AI, right, you train it, you give
Speaker 3: it a system prompt, and then you test test test it, right,
Speaker 3: and whenever it says Hitler, you say no, you're being
Speaker 3: a bad boy.
Speaker 2: If this is not in the context of.
Speaker 3: World War two or something, then please don't mention Hitler
Speaker 3: so much. Right, And you know, if you spank it
Speaker 3: really hard. It learns to avoid that, right, But if
Speaker 3: you're if you're just kind of I'm getting the prompt,
Speaker 3: testing it with two of your buddies and then releasing it,
Speaker 3: you might you might miss those. So there is fundamentally
Speaker 3: a speed off or a trade off, speed off, a
Speaker 3: trade off between speed and safety. Right, it's maybe not
Speaker 3: a one for one trade off that it's probably pretty
Speaker 3: inversely correlated.
Speaker 1: It is it is, And by the way, in the
Speaker 1: Grock example, I think it's also interesting that some of
Speaker 1: the coding that was revealed said also to check what
Speaker 1: Elon Musk thinks about this right before giving your answer.
Speaker 1: And this was not this was not a blip, Like
Speaker 1: multiple people replicated that this was actually in the coding.
Speaker 1: And as you've already said, like open AI said before
Speaker 1: that it was you know, safety first, and that's clearly
Speaker 1: not the case, and they've shown that to not be
Speaker 1: the case. So I think we do need to keep
Speaker 1: asking questions about incentives, about alignment, and about kind of
Speaker 1: who's benefiting from what type of advances in the AI community.
Speaker 3: Yeah, I mean the as kid, I mean, it felt
Speaker 3: to me like Grock kind of almost took on like
Speaker 3: a personality or became like kind of a self caricature,
Speaker 3: right where it can be recursive, right if it's like
Speaker 3: searching output on X, including its own output. Right, I
Speaker 3: might say, Okay, this is how Groc behaves, and like,
Speaker 3: let me be careful about this. I mean, Groc can
Speaker 3: kind of be in a dark way, kind of funny,
Speaker 3: whereas the other A models are are are not right,
Speaker 3: they're kind of goody two shoes aiming to please. Oh,
Speaker 3: that's the most brilliant article I've ever seen, Nate. You know,
Speaker 3: here are twenty seven typos if I give it a
Speaker 3: copyated task, right, And this is a counter to that.
Speaker 3: But like, all these things are tied together, right, Like
Speaker 3: wokeness is also tied to kind of a certain type
Speaker 3: of academic expertise, you know what I mean, which I
Speaker 3: call the indigo blob. How Like, Yeah, the liberal media
Speaker 3: is biased and liberal, but also it's generally more accurate
Speaker 3: and has more expertise in the conservative media. Both those
Speaker 3: things are true. If you crunch a bunch of data
Speaker 3: from the Internet into vectors, then that gets hard, right,
Speaker 3: It gets hard because you might throw the baby out
Speaker 3: with the bath water, right that you want to like
Speaker 3: have it reflect the expert consensus. Well, the experts have
Speaker 3: biased it sometimes sometimes creeps into that consensus, right, And
Speaker 3: it's very hard for again for newsrooms and journals and
Speaker 3: constitutions to do it, but just as hard for AI models.
Speaker 1: Yeah, And as AI models become more integrated and become
Speaker 1: kind of more a part of the way that people
Speaker 1: do work, and we see more outputs from AI models
Speaker 1: and their training materials shift, like these are things that
Speaker 1: can become even worse kind of in the future. It
Speaker 1: is kind of this cycle that can self reinforce.
Speaker 2: And we'll be right back after this break.
Speaker 3: I don't want people thinking that like an AI is
Speaker 3: like an oracular. It's not perfect entity either, right.
Speaker 2: You know. Paul Krugman had a thing about like.
Speaker 3: GROC a couple of weeks ago where he's like, well,
Speaker 3: if AI has a liberal bias, because reality has a
Speaker 3: liberal bias, right, And I'm like, well, maybe on some things,
Speaker 3: but like you know, and he's actually pretty skeptical of AI,
Speaker 3: Like that's not you know, look at any algorithm, and
Speaker 3: I probably an AIL. I'm probably too complicated to describe
Speaker 3: as an algorithm. It has like some membership behaviors, but
Speaker 3: still directionally speaking, right, any algorithm is imperfect, you know
Speaker 3: what I mean, to make bad predictions can reflect bad data.
Speaker 3: And the AI is especially they're not fine tuned, which
Speaker 3: requires human input.
Speaker 2: Right, that's how they're fine tune. They have like a
Speaker 2: bunch of people being like yes no, yes, no.
Speaker 3: Then I mean I think garbage in, garbage out is
Speaker 3: not quite the right case.
Speaker 1: But yeah, directionally speaking, that actually that points to what
Speaker 1: we're talking about, which is that the inputs really really
Speaker 1: do matter. And I think the other thing is that
Speaker 1: there is this human bias to think that, oh, well,
Speaker 1: this is data. This is a machine, so it's more
Speaker 1: unbiased because it's not human. And that's simply not true,
Speaker 1: you know. And if we have that bias to say, oh, well,
Speaker 1: this is like this is objective truth because it's coming
Speaker 1: from a not like it's coming from a program that's
Speaker 1: that ain't work, that ain't where it's at like that,
Speaker 1: that is actually a highly problematic way of thinking.
Speaker 3: Yeah, and look what it's objectives aren't clear, right, I mean,
Speaker 3: in one sense, large language models are trying to minimize
Speaker 3: the laws function from accurately affecting what's represent the data.
Speaker 3: On the other hand, it's contradicted by the human feedback
Speaker 3: that it gets, right, and so it's kind of trying
Speaker 3: to please its creators to whatever extent it can, and
Speaker 3: to the point of being kind of a sycophant in
Speaker 3: some cases. So like and you know, I think people
Speaker 3: understand more about interpreting AI outputs than they did a
Speaker 3: year or two or three ago. But it can be fragile.
Speaker 3: You know, small things can affect the entire system. Right,
Speaker 3: It's it's complexity. It's a kind of origin of complexity.
Speaker 3: Theory really is well reflected in AI. Where a butterfly
Speaker 3: flaps its wings in Beijing, this tornado in Texas. Right,
Speaker 3: you know, Elon puts one change the system prompt in place.
Speaker 2: It doesn't seem to matter.
Speaker 3: But when you put that change in and then you say, also,
Speaker 3: please make sure you're reading what Elon would write. Right,
Speaker 3: those things combined can have a profound effect.
Speaker 1: And this is just something that's visible. Now, imagine all
Speaker 1: the things that are invisible right behind the scenes, like
Speaker 1: tiny tweaks like that that we don't see because it's
Speaker 1: not actually visible in like your immediate LLLM output unless
Speaker 1: you really try to kind of no, and we.
Speaker 2: Moved away from transparency in general.
Speaker 3: I mean not just in Google's Ai Gemini, but like
Speaker 3: in Google Search, there's a lot of intervention, right that
Speaker 3: if you search for occupations, just in regular Google search
Speaker 3: for photos of like doctors, it will be very conscientious
Speaker 3: to show I mean there might be more women doctors now.
Speaker 3: It's like it will show like doctors of all races
Speaker 3: and women doctors, right if you probably if you search
Speaker 3: for hockey player, it'll make sure. I mean there are
Speaker 3: some great black hockey players, right, but it's a lot
Speaker 3: of white Canadian and European kids, right, And so you know, yeah,
Speaker 3: the notion of oh, the Web's just a search, right
Speaker 3: or large language models are just kind of a new
Speaker 3: to presentation of a clever way to like matricize Internet
Speaker 3: data and writing.
Speaker 2: I mean, it's never been that.
Speaker 3: And the consensus has moved away from transparency if anything
Speaker 3: almost toward like, well, if we're transparent transparent, they'll just
Speaker 3: give you more things to critique or to pick on.
Speaker 3: And so so sorry, you know, if you like the model,
Speaker 3: then use it. If you don't like it, then use
Speaker 3: different model instead.
Speaker 1: Yeah, and there is this tension here once again with
Speaker 1: going back to what we were talking about with open source, right, Like,
Speaker 1: there's definitely this tension between open source, and that's actually
Speaker 1: some of the good stuff of transparency, right that you
Speaker 1: can actually look at the inputs and actually try to
Speaker 1: figure out what's going on, are they Like, that's one
Speaker 1: of the benefits of having open source, transparent models that
Speaker 1: people can kind of pick apart and play with. We've
Speaker 1: talked about kind of the risks, you know, p doom,
Speaker 1: et cetera, et cetera, But there are benefits, and transparency
Speaker 1: is certainly one of them.
Speaker 3: Yeah, they been many times when we've had models different
Speaker 3: kinds running, right, and people are like, this seems wrong, right,
Speaker 3: why is this pull away a certain way? And like, yeah,
Speaker 3: eighty percent of time it's part ofsan bs, But twenty
Speaker 3: percent of time that they've caught something for sure, right,
Speaker 3: any for sure? Even for myself, if I have a
Speaker 3: model making sure I output different data at different stages,
Speaker 3: evaluate it. If you don't catch a problem early on
Speaker 3: when you're building a complex, you know, the NFL thing
Speaker 3: I'm working on, right, there are lots of component parts.
Speaker 2: It will eventually be a couple of.
Speaker 3: Thousand lines of code and then which individually are that
Speaker 3: complicated if like, if there's one step that's wrong, it
Speaker 3: can infect the entire system, right, And if you're smart,
Speaker 3: you're building and robust us and redundancy. You know, every
Speaker 3: major computer program has bugs, every model has bugs.
Speaker 2: Right.
Speaker 3: But like, but like outputting that and being like I
Speaker 3: want to visualize this, I want to look at the
Speaker 3: right inputs, right, I want to look at edge cases
Speaker 3: that are important.
Speaker 2: Like that's an important thing to do.
Speaker 1: Yeah, it absolutely is. And you know, to kind of
Speaker 1: defend open source. That's one of the big things about
Speaker 1: data integrity. Just everywhere is open sources good? Right, So
Speaker 1: there was huge crisis in academia with you know, with cheating,
Speaker 1: with fabricating data, you know, with papers that were published
Speaker 1: in important journals with big results that ended up being manipulated.
Speaker 1: And so there's more and more of a movement toward
Speaker 1: you know, not just pre regis during your study, but
Speaker 1: sharing your data, right, like actually sharing the data sets
Speaker 1: so that people can look at what the source material was.
Speaker 1: And you know, like one of the biggest scandals that
Speaker 1: we talked about briefly on the show with Dan Ariellie
Speaker 1: and Francesca Gino, they you know, showed that some of
Speaker 1: those data sets were manipulated, but that was only after
Speaker 1: they were forced to provide the data sets, you know,
Speaker 1: after some good some ego eide researchers were like, hey,
Speaker 1: like these what we're seeing in the paper doesn't make sense,
Speaker 1: and sometimes it's willful manipulation and sometimes it's just a mistake. Right.
Speaker 1: There have been some side like there was one very
Speaker 1: famous scientific study where they fucked up an Excel sheet
Speaker 1: where they buy mistake like moved the rows down by one,
Speaker 1: and that just completely. It was just it was horrific
Speaker 1: because this is a medical study. Right, So actually tiny
Speaker 1: things like that, it's incredibly important to have access to
Speaker 1: the raw data, to be able to to look at this,
Speaker 1: to manipulate it on your own. People can really spot
Speaker 1: both malfeasance and just human error which will happen and
Speaker 1: with AI will have both human error and computer error.
Speaker 1: Right as we're as we're having AIS do coding for us,
Speaker 1: you know, vibe coding all of that, there might be
Speaker 1: errors there. We need people. We need people capable of
Speaker 1: looking at it, debugging and trying to figure out, hey,
Speaker 1: this seems like it's working great, but actually it's not.
Speaker 3: Now a well designed model, including in a large language
Speaker 3: model is kind of like a airplane, right, there are
Speaker 3: like multiple redundant systems, there are different levels of safety
Speaker 3: and safeguards, and like that's hard to do it. That
Speaker 3: requires a lot of work and expensive budget and being
Speaker 3: an actually good programmer YEP.
Speaker 1: One of the other major things that I saw in
Speaker 1: this action plan that I think is actually quite important,
Speaker 1: and again there are arguments on both sides, is like
Speaker 1: one of the big bottlenecks to AI development is energy, right,
Speaker 1: because AI just like takes a lot a huge energy
Speaker 1: cost and access to data centers. Like that's that's actually
Speaker 1: been a big thing where data centers like can't you know,
Speaker 1: they've tried to bypass the power grids. There's been you know, pushback,
Speaker 1: et cetera, et cetera. So one of the things that
Speaker 1: this plan tries to do is say, let's eliminate all
Speaker 1: the barriers and let's try to use all the energy
Speaker 1: we have available for AI. Yeah.
Speaker 3: I mean, look, I think it's just like a little overclaim, right.
Speaker 3: If you look at like the amount of actual wattage
Speaker 3: required to like run a chat reipete response, it's like
Speaker 3: this is kind of like a I don't know, it's
Speaker 3: a fairly bullshitty claim based on the present capabilities of AI.
Speaker 1: So Nate, let me just push back a little bit
Speaker 1: because there's a related issue, which is the availability of
Speaker 1: water to cool these data centers. And we have already
Speaker 1: seen that there has been you know, there have been
Speaker 1: instances of wells town wells running dry. There's been instances
Speaker 1: of construction having to be because the water, because of
Speaker 1: basically water shortage issues. So I think that that's a
Speaker 1: related concern that we should have just dispised.
Speaker 2: Getting from low Ki Maria.
Speaker 1: Maybe maybe Grock, What do you have to say about this?
Speaker 3: Yeah, I would, I would, I would be I'm skeptical.
Speaker 1: Of I think it is a big I think it
Speaker 1: is a big deal.
Speaker 3: It's not relative to other uses of electricity though it's
Speaker 3: a small piece of the pie. And granted Sam Altman
Speaker 3: at all want to be bigger.
Speaker 2: Right.
Speaker 1: Yeah, And I you know, it's hard for me. I
Speaker 1: don't have the numbers in front of me, so I'm
Speaker 1: not going to say something and have it not be true.
Speaker 1: So so I'm just I'm not gonna engage that point
Speaker 1: because I just honestly don't know. However, it is something
Speaker 1: that this bill is trying to address and trying to
Speaker 1: give AI companies whatever resources it needs to access power grids,
Speaker 1: access energy grids, so that data centers can be built
Speaker 1: on federal land and kind of allow innovation to proceed
Speaker 1: without there being an energy bottleneck. I think that that's
Speaker 1: kind of objectively what this bill is trying to accomplish.
Speaker 3: Yeah, look, I mean, by the standards of Trump, it's
Speaker 3: a pretty serious document. The one good thing about their
Speaker 3: alliance may not the only good thing right with the
Speaker 3: kind of tech right is that they do I think
Speaker 3: have like Capetan people in place. You might have different
Speaker 3: position on AI, but like, this isn't an amateurish documents.
Speaker 3: It's thoughtful. It involves politics, but like there was some
Speaker 3: care put into this. Yeah, for sure, for sure, very
Speaker 3: unlike Trump in most ways.
Speaker 1: I agree. I agree reading it, you know, obviously you
Speaker 1: can see the Trump and language and parts of it,
Speaker 1: but there there are nuanced points here where it will
Speaker 1: be interesting to see how, if at all, any of
Speaker 1: this is enforced and kind of what ends up happening.
Speaker 1: I think we'll just have to see over the next
Speaker 1: six months, one year and see how a lot of
Speaker 1: these provisions shakeout. By the way, there are a lot
Speaker 1: more provisions. We're not going to talk about the whole thing. Nate,
Speaker 1: as you said, V has a good substock on this,
Speaker 1: so for people who want to know more, I think
Speaker 1: we'd both encourage you to read that. But yeah, all
Speaker 1: in all, you know some things where you're like, okay, yeah,
Speaker 1: this makes out some things that are a little you know,
Speaker 1: eyebrow raising, not enough care shown to or not care
Speaker 1: that's the wrong word. But I think we're seeing speed
Speaker 1: over safety in a lot of this, and how do
Speaker 1: we facilitate speed instead of safety? So I think that that,
Speaker 1: to me is the big thing to kind of watch
Speaker 1: out for and to keep an eye on. Thanks for listening.
Speaker 1: We're taking next week off and we'll be back in
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Speaker 1: Business is hosted by me Maria Kanikova.
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 Isabel Carter. Our associate producer is Sonia Gerwin. Sally
Speaker 3: Helm is our editor, and our executive producer is Jacob Goldstein.
Speaker 2: Mixing by Sarah Bruger.
Speaker 1: Thanks so much for tuning in.