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 today on the show, Maria, have
Speaker 2: you ever taken a way Moo or another autonomous vehicle?
Speaker 1: I have, not, Nate, but I know, I know, I know,
Speaker 1: I know that you have. We've talked about that before.
Speaker 1: But yeah, we'll be talking more in depth about kind
Speaker 1: of the whole autonomous vehicle future, the risks, the rewards,
Speaker 1: what's good, what's bad, how we should think about it,
Speaker 1: how we should evaluate it. I think there are lots
Speaker 1: of questions here and lots of things to think about
Speaker 1: as we kind of figure out the future of driving.
Speaker 2: We got a little risk, we got a little psychology,
Speaker 2: we got a little algorithm TLIC.
Speaker 3: It's gonna be a good episode, Maria.
Speaker 1: So, Nate, you told me about some amazing experiences that
Speaker 1: you've had using waymos in San Francisco.
Speaker 3: Yeah. No, I uh.
Speaker 2: The first time I took a WEEIMO, I was actually
Speaker 2: at a meeting with Manifold, which is a prediction market company,
Speaker 2: just shooting the shit. I'm friendly with those guys, and
Speaker 2: they're like, we're gonna hail you Awaimo back to your
Speaker 2: hotel in San Francisco, and I'm like, okay, this seems
Speaker 2: kind of stupid, right, I don't know, we're afraid, and
Speaker 2: like it was fucking it felt like the fucking future.
Speaker 3: Right.
Speaker 2: You get in there, there's no driver, there's like space
Speaker 2: age music playing, and like San Francisco is like not
Speaker 2: a particularly easy city to navigate. There's a lot of traffic,
Speaker 2: there's lots of hills. I wouldn't call SF the most.
Speaker 3: Traffic obedient place. Probably better than like Boston or something. Right, Oh,
Speaker 3: I don't think.
Speaker 1: Anything is worse than Boston when it comes to.
Speaker 3: You know, my dad had like it.
Speaker 2: We lived briefly in Boston for a year, and like
Speaker 2: he got a falsely accused and like being in a
Speaker 2: car accident. There's a Brian Q Silver and he was
Speaker 2: Brian D Silver. Anyway, we'll leave it aside family lore.
Speaker 3: Brian Q Silver, Fuck you, Brian Q Silver. He fucking
Speaker 3: my dad's credit reading.
Speaker 1: Oh my god, No, that's I mean, this is a
Speaker 1: fun like this is a hilarious side of family lore,
Speaker 1: but like this is actually a consideration when it comes
Speaker 1: to self driving cars versus human driving cars.
Speaker 2: No, it felt like the fucking future, right, and like it,
Speaker 2: like I would say, and by the way, there's also
Speaker 2: like lots of let's be honest, especially a couple of
Speaker 2: years ago, and this happened a lot of vagrancy, homeless people,
Speaker 2: sketchy people in downtown San Francisco, right, So like you're
Speaker 2: dealing with an unpredictable element that might not be programmed
Speaker 2: into some baseline model of like how driving is supposed
Speaker 2: to work. But now the WAIMA did like a very
Speaker 2: very good job. It's very smooth with the acceleration and
Speaker 2: the deceleration. It's a little bit a little bit nitty
Speaker 2: about following traffic signals and things like that. I was
Speaker 2: you would expect, but it did like lots of I
Speaker 2: think fairly saying in rational things, right, like I was
Speaker 2: staying at some slightly funky hotel and like it pulls
Speaker 2: up on the opposite side of the hotel where it's
Speaker 2: like safer to get out and not the officially recommended spot,
Speaker 2: and like it just you know, it would be like
Speaker 2: in the ninety seventh or ninety eighth percentile of Uber drivers,
Speaker 2: I would say, you know, whether or.
Speaker 3: Not you like the conversation, Yeah, go ahead.
Speaker 1: I was gonna say, it's funny that you say they're nitty.
Speaker 1: So I'm in Las Vegas right now, and Vegas is
Speaker 1: rolling out a lot of autonomous vehicles, not just way Mos,
Speaker 1: but what's the what's the other one called zekes There
Speaker 1: we go zu zukes all right, So Vegas is rolling
Speaker 1: out a lot of autonomous vehicles, including zekes here. So
Speaker 1: when I came back for the NAPT the North American
Speaker 1: Poker to Our last month, was the first time that
Speaker 1: they had done really the full rollout already where you
Speaker 1: can hail them, you know as Uber, Left, et cetera. Well,
Speaker 1: actually I don't know if it's both Uber and Left. Anyway,
Speaker 1: you can use ride sharing services with them, and they're
Speaker 1: all over the streets. And I was trying to drive,
Speaker 1: and I realized very early on, and this says something
Speaker 1: about how close I like to you know, I sometimes
Speaker 1: cut things a little bit close when it comes to
Speaker 1: making it to places on time. I realized right away
Speaker 1: that I did not want to be behind them. Basically,
Speaker 1: if I saw them in my lane, I tried to
Speaker 1: like get into a different lane because they would not
Speaker 1: go a single mile above the speed limit. They would
Speaker 1: stop like and sometimes get a little bit, you know, confused.
Speaker 1: They were very nitty, very cautious drivers, and I was like,
Speaker 1: come on, guys, you can go thirty seven in the
Speaker 1: thirty five zone.
Speaker 3: It's not going to kill you.
Speaker 1: It's gonna be okay. But they wouldn't, and which was
Speaker 1: really interesting. And I don't know if it's you know,
Speaker 1: if it's this particular company, but they were definitely very
Speaker 1: nitty drivers, which, by the way, is not necessarily a
Speaker 1: bad thing, right, Like they were being they were being cautious,
Speaker 1: they were being safe. But for me, I was like, okay,
Speaker 1: I want to go slightly faster. Sorry, Las Vegas Police.
Speaker 1: I never go more than a few miles above the
Speaker 1: speed limit, I promise, But like I got stuck behind
Speaker 1: them a few times, and like they wouldn't take some
Speaker 1: turns at intersections which I think a human driver might
Speaker 1: have taken. Probably it's a little bit safer, Like they
Speaker 1: just really play it safe in those scenarios. But I
Speaker 1: just want it to be like, Okay, you can go,
Speaker 1: you have room, let's go, let's make that turn.
Speaker 2: I mean, there are there are times when it's probably
Speaker 2: I don't drive, actually I do bike sometimes, right, like
Speaker 2: on the city bikes or whatever. Right, there are times
Speaker 2: when violating traffic laws is probably safer. Yeah, you know
Speaker 2: what I mean, like mile violations where or just on
Speaker 2: the common sentence, so you're trying to yeah, I don't know.
Speaker 3: I think that's really and that's more true in New
Speaker 3: York than probably other locations. I would think.
Speaker 1: Now, sometimes you do have to make these judgment calls
Speaker 1: because sometimes like you will see something and in order
Speaker 1: to be safer, like you will have to violate a
Speaker 1: traffic rule. And that's I think one of these broader
Speaker 1: you know, as we as we start talking about kind
Speaker 1: of the broader risk reward and kind of how this
Speaker 1: future looks, and you know what we think about autonomous vehicles,
Speaker 1: I think that a lot of the questions do revolve around,
Speaker 1: you know, how do you program the cars to actually
Speaker 1: make these decisions, because you know, it's not It would
Speaker 1: be one thing if we had this immediate transition right
Speaker 1: where like you can just go like this snap of
Speaker 1: the fingers and all of a sudden, all the cars
Speaker 1: on the road are autonomous vehicles. That's very different from
Speaker 1: having kind of multiple years of in between where you
Speaker 1: have human drivers and autonomous vehicles and you know, more
Speaker 1: human drivers than autonomous vehicles in all of these kind
Speaker 1: of the transitional period, I think from a risk safety
Speaker 1: standpoint and from making those types of trade offs is
Speaker 1: probably going to be the most difficult to navigate because
Speaker 1: you have to account not just for ideal driving and
Speaker 1: what you're supposed to be doing, but what humans actually
Speaker 1: do right, and the mistakes that humans make, and how
Speaker 1: you share the road with humans, some of whom are
Speaker 1: bad drivers, some of whom are impaired drivers, right, because
Speaker 1: those risks don't suddenly magically go away. Now, it's kind
Speaker 1: of you know, it's funny. We talk a lot about
Speaker 1: poker and kind of ideal poker on this show. It's
Speaker 1: kind of like, you know, if you have the GTO
Speaker 1: cars right, but then you have all of these crazy
Speaker 1: and different types of players on the road, and you
Speaker 1: have to try to figure out like, sure, you have
Speaker 1: to drive in a GTO way, but you also have
Speaker 1: to make sure that you are optimizing for the fact
Speaker 1: that you have, you know, these loose cannons and all
Speaker 1: of these different elements that are still going to be
Speaker 1: sharing the roads with you. And unless there's like this
Speaker 1: huge thing where all of a sudden, the government's like,
Speaker 1: we're going to pay you, you know, tons of money
Speaker 1: to give up your car and use autonomous vehicles. Like
Speaker 1: that transition, to me is going to be one of
Speaker 1: the riskier parts of this whole equation.
Speaker 3: Well, I assume that these.
Speaker 2: Cars are programmed in like find if you call it,
Speaker 2: like an exploitive strategy in poker, right, but as soon
Speaker 2: they are at least doing some defensive driving, right. I
Speaker 2: mean again, San Francisco not a place which is ideal
Speaker 2: for traffic laws or anything else, really right, and they're
Speaker 2: kind of training these real world environments.
Speaker 3: I mean, look, I mean to back up a second, right, Like,
Speaker 3: in general.
Speaker 2: There's this critique that like AI driven systems are good
Speaker 2: with simple manipulation tasks, so language, certain types of math, right,
Speaker 2: certain types.
Speaker 3: Of games when they're well trained.
Speaker 2: I know we've talked in this program about how poker
Speaker 2: if you're not training on poker specifically, might be an exception.
Speaker 2: So it is kind of amazing that like that they're
Speaker 2: as good as they are, right, But like, yeah, the
Speaker 2: thing people are recognize is like the human drivers are
Speaker 2: I have lots of problems, you know what I mean,
Speaker 2: They're distracted I mean, I was just in where was
Speaker 2: I was just in Florida, right, And like.
Speaker 3: In Florida you can talk on your cell phone when
Speaker 3: you're driving.
Speaker 1: And like, oh my god, is that true night?
Speaker 3: Oh my god, you do neither you nor there?
Speaker 1: No, no, no, that's actually that's actually both here and there.
Speaker 1: I don't remember if I've ever told our listeners, but
Speaker 1: the one of the scariest experiences I've had in an
Speaker 1: uber was in California. It was in LA. We were
Speaker 1: stuck in traffic in the middle of you know, one
Speaker 1: of these horrible LA freeways, and my driver was already
Speaker 1: like exhibiting signs of strange behavior, and then he just
Speaker 1: like stares intently at me in the rear view mirror
Speaker 1: and says, do you believe in God?
Speaker 3: That I was like, oh no, oh.
Speaker 2: God, no, oh no.
Speaker 1: Anyway, this is actually very Germane because that is one
Speaker 1: of the things that I think self driving, the autonomous
Speaker 1: vehicles definitely have a leg up on. You are not
Speaker 1: going to have one of those do you believe in God?
Speaker 1: Experiences that are going to make you want to immediately
Speaker 1: get out of the car and we'll be back right
Speaker 1: after this. So I think that one of to me,
Speaker 1: one of the big good things about the future of
Speaker 1: autonomous vehicles is the fact that you kind of sometimes
Speaker 1: like this is evidence for when taking the human equation
Speaker 1: out of it can be good. Right, You don't have
Speaker 1: to like every time that I'm coming home, you know,
Speaker 1: every time I'm going back to Brooklyn from JFK. When
Speaker 1: I get back to New York, I end up taking
Speaker 1: the taxi, but I get into arguments with the driver
Speaker 1: every single time because inevitably they want to take the
Speaker 1: Belt Parkway, which is never the best way to go.
Speaker 1: You always get stuck in traffic, it's always horrible, but
Speaker 1: they always want to do that, and we just get
Speaker 1: into a back and forth. I'm like, no, I want
Speaker 1: you to do this, and they're like, no, I'm gonna
Speaker 1: do that. And with presumably with autonomous vehicles, you know,
Speaker 1: a lot of this won't come up, right, they have
Speaker 1: the roots and they will kind of they will, they will.
Speaker 2: Do Yeah, I'm a little more sympathetic empirically, and I
Speaker 2: don't drive, but my partner does and we drive decently
Speaker 2: off the bike. Empirically, I think like when the GPS
Speaker 2: says something that like you as an experienced driver in
Speaker 2: that area.
Speaker 3: Disagree with I think it's kind of fifty to fifty.
Speaker 3: Who's right? Yeah and who isn't? You know what I mean?
Speaker 2: From like little things like knowing, oh, this street is
Speaker 2: closed when we used to live near Penn Station. Right,
Speaker 2: there's a nixt game starting. You don't want to fucking
Speaker 2: drive anywhere near Massion Square Garden when there's a next
Speaker 2: game starting. Or I mean, look, obviously, if you're in
Speaker 2: a new or a lift, then the estimate of when
Speaker 2: you'll arrive at your destination is strongly biased toward optimism.
Speaker 3: You know, this is why I want, like, I want
Speaker 3: prediction Mark. We need polymarket nation, we can bet.
Speaker 1: Yeah, we need psychology actually in this because I think
Speaker 1: that I actually think that cars get this absolutely backwards,
Speaker 1: because it's incredibly frustrating when you see, like you go
Speaker 1: to order an uber a lift and it's like two
Speaker 1: minutes and then it ends up being eight minutes because
Speaker 1: they basically have lied to you and they know that
Speaker 1: right away, and then they're like, oh, it's going to
Speaker 1: take you, you know, ten minutes to get here, or this
Speaker 1: traffic slow down is a fifteen minute slow down, whatever
Speaker 1: it is, it's always optimistic right, And the way you
Speaker 1: need to do it psychologically speaking is you want to
Speaker 1: do the old underpromise over deliver, right. They should actually
Speaker 1: know that this is statistically speaking, you're probably going to
Speaker 1: be at the other side of the distribution. So instead,
Speaker 1: let me say you know that your uber is coming
Speaker 1: in eight minutes, and then when it arrives in four minutes,
Speaker 1: you'll be happy instead of frustrating. Let me tell you
Speaker 1: that the slowdown is going to be twenty five minutes,
Speaker 1: and then when it's over in twenty minutes, you're gonna
Speaker 1: be like, oh my god, it was much shorter.
Speaker 3: This is great.
Speaker 1: So I actually think that psychologically they get this totally wrong.
Speaker 1: We're talking about self driving cars in your.
Speaker 2: Room at your at Bellagio and I'm gonna, I'm gonna,
Speaker 2: I'm gonna order a lift in six minutes, right, and
Speaker 2: it's like they're in two minutes. You have to get
Speaker 2: on the fucking ride share thing. You don't take these
Speaker 2: zubers in Vegas, I guess very much, right.
Speaker 1: No, that's it's yeah, No, that's a different thing. But
Speaker 1: I think you just right now, there's no reliable gauge
Speaker 1: for how quickly or not a car will actually come.
Speaker 1: But I wonder if autonomous vehicles will actually solve this
Speaker 1: problem or if they're just gonna lie the exact same way,
Speaker 1: right that this is a problem with the underlying algorithm
Speaker 1: as opposed to I think anything else. The other thing
Speaker 1: that with autonomous vehicle is actually where you might need
Speaker 1: a human. So talking about GPS and all of these things,
Speaker 1: for whatever reason, where I live in Vegas for a
Speaker 1: very long time was for some reason misrepresented on the GPS,
Speaker 1: and drivers would actually like go to the totally wrong place,
Speaker 1: and it was an absolute nightmare wanting you needed to
Speaker 1: get picked up because somehow the GPS just completely fucked up, right,
Speaker 1: and so people would just not be able to find it.
Speaker 1: And so if there's a human there, you can talk
Speaker 1: to them and be like, hey, okay, no, you need
Speaker 1: to do this, you need to take a turn here,
Speaker 1: you need to do this to get me. What do
Speaker 1: I do if it's a weimo that's coming to pick
Speaker 1: me up? How do I make sure that when you
Speaker 1: need kind of that human intervention because the technology is
Speaker 1: fucking up. Like I said, I've never taken weimos, so
Speaker 1: I don't know if you can interact with them in
Speaker 1: the same way. Maybe this isn't a problem, but it
Speaker 1: seems like it might be a point of friction.
Speaker 2: I mean, look, one problem with algorithms, really with algorithms there,
Speaker 2: with people who designed the algorithms, right, is like there's
Speaker 2: like a lot of over literalness.
Speaker 3: You know what I mean. Like I used to live near.
Speaker 2: A corner near Penn Station, right, and this is a
Speaker 2: very busy all times of days intersection, right, But there
Speaker 2: are lots of times when if not makes sense to
Speaker 2: loot all the way around the whole avenue when you
Speaker 2: can just drop me on like one block north or
Speaker 2: one block south right, and like so like that logic
Speaker 2: that like a human driver, I'm just gonna drop you
Speaker 2: twenty ninth and seventh and not loop.
Speaker 1: Around because otherwise it's gonna add five minutes to fifteen
Speaker 1: fucking minutes, yeah, fifteen minutes whatever it or like hey, pick.
Speaker 2: I'm gonna pick you up one seventh and not on
Speaker 2: on thirtieth. Like that kind of thing has never been
Speaker 2: a strength of any of these, right, because you're like
Speaker 2: overly literal about like okay, get me the general area,
Speaker 2: and like this is you know a lot of the
Speaker 2: pickoff and drop off locations can be overly literal at
Speaker 2: some properties and things like that.
Speaker 3: I mean, I don't know.
Speaker 1: Yeah, presumably algorithms will get better, and they'll they'll get
Speaker 1: better at things like that, but there are other safety
Speaker 1: issues that I do wonder about. And by the way,
Speaker 1: a few weeks back, there was a big op ed
Speaker 1: from a surgeon in the New York Times about autonomous
Speaker 1: vehicles and you know, accident rates and not just fatalities,
Speaker 1: but the types of injuries that the physician you know
Speaker 1: has seen in different situations. And it was arguing for
Speaker 1: more autonomous vehicles because you know of how these insane
Speaker 1: situations that human drivers get into, and that their safety
Speaker 1: record is much better. Obviously this is a tiny fraction,
Speaker 1: you know, et cetera, et cetera, et cetera. But that
Speaker 1: made me much more kind of pro autonomous vehicles than
Speaker 1: I might otherwise have been, because I think that this
Speaker 1: is this is a big, a big deal, and we
Speaker 1: do want to be thinking about, you know, about those
Speaker 1: types of questions. But some of the other kind of
Speaker 1: algorithmic questions are you know, how do you and this
Speaker 1: is you know, I don't want to get all like
Speaker 1: philosophical here, But it does like get into kind of
Speaker 1: these morality problems, types of situations where like whose life
Speaker 1: do you prioritize? Right? Like how do you make those
Speaker 1: types of judgment calls? What is the algorithm going to
Speaker 1: do in different situations where there's no good answer right
Speaker 1: where you might endanger your passenger or engager someone crossing
Speaker 1: the street, or do this or do that, especially as
Speaker 1: we started off the show talking about in this transitional
Speaker 1: period where you're not dealing with other autonomous vehicles, right,
Speaker 1: you're dealing with a lot of humans on the road.
Speaker 1: So how do you make those trade offs? How do
Speaker 1: you program that? You know, who's responsible for programming those algorithms?
Speaker 1: How are they thinking about it? I think these are
Speaker 1: all really important questions where it's not a question of
Speaker 1: tweaking the algorithm and saying, oh, like let the person
Speaker 1: tell them to like pick me up at this corner instead.
Speaker 1: These are much deeper questions.
Speaker 2: Yeah, I mean I some of our listeners are probably
Speaker 2: familiar with with the trolley problem.
Speaker 1: Yeah. Yeah, the trolley problem is your is your gold
Speaker 1: standard here and all the iterations of.
Speaker 2: It, which is a philosophical dilemma. I guess where there
Speaker 2: is a trolley on a busy track. It's going to
Speaker 2: kill I guess some people who are tied to the track.
Speaker 2: You can also divert it and instead of killing these
Speaker 2: five people, you kill like two workers.
Speaker 3: I mean, there are there are lots.
Speaker 1: Of infinite variations of it.
Speaker 2: I think it seems pretty us like diverted kill the
Speaker 2: two and not the five. Right, But Like the point is,
Speaker 2: if you're kind of like intervening and making a decision,
Speaker 2: then it begins to feel like I feel like, yeah,
Speaker 2: you've made a moral choice, right, And.
Speaker 1: And what if you know that one of these is
Speaker 1: an infant one of these is a Nobel Prize winning set, Right,
Speaker 1: what if you actually know something about the identities? Like
Speaker 1: you said, there are infinite variations of this, of the
Speaker 1: trolley problem, and there's no definitive answer. Like philosophers disagree
Speaker 1: on the answers to this to this day. There are
Speaker 1: different philosophical schools that have that advocate different approaches. And
Speaker 1: this is I mean, autonomous vehicles have to deal with
Speaker 1: one big trolley problem, right in some sense.
Speaker 2: Yeah, when and when you have to quantify things, it's
Speaker 2: one of the things that I will defend some of
Speaker 2: the effective altruists and like rationalists about right is Thelle
Speaker 2: will be like, well, what's what's the value of a
Speaker 2: ship's life compared to like a human life? Yep, And
Speaker 2: it can be a little off putting to think about.
Speaker 2: On the other hand, we make the trade offs all
Speaker 2: the time, right. I talk about an example in my
Speaker 2: book from a few years ago where like the entire
Speaker 2: New York City, not the entire New York City subway system,
Speaker 2: several lines, like the f whatever lines were shut down
Speaker 2: because of a loose dog that I had in somewhere
Speaker 2: in Brooklyn Heights. I think, right, And like, you know,
Speaker 2: I mean, there is a big cost to shutting down
Speaker 2: the subway.
Speaker 3: It makes people late.
Speaker 2: It means that can include like emergency workers and things
Speaker 2: like that, because often in New York the fastest way
Speaker 2: to get around is with the subway, no matter, you know,
Speaker 2: unless you have an ambulance with the right of way
Speaker 2: and things like that. Right, and people are comfortable talking
Speaker 2: about like what is the cost of a lost hour
Speaker 2: of economic productivity? And how's that way against a dog's
Speaker 2: life and things like that.
Speaker 3: But the point is that like that.
Speaker 2: When you kind of force people to make decisions that
Speaker 2: are explicit and if playing their logic then then that
Speaker 2: becomes problematic in some ways.
Speaker 1: Right, Yeah, And you know when you are talking about
Speaker 1: all of these autonomous vehicles and all of these different companies,
Speaker 1: you have to realize. I mean we've talked about this before,
Speaker 1: but I think it's a really important point that algorithms
Speaker 1: do not exist in a vacuum.
Speaker 3: Right.
Speaker 1: They are based on they're humans who are programming them.
Speaker 1: There are people who are waiting different inputs. Those are
Speaker 1: all judgment choices. Even when you're looking at you know
Speaker 1: AI and like lms, that's also trained on some set
Speaker 1: of data. Right, which data is it trained on? Who
Speaker 1: created that data? And like those weights really matter, right,
Speaker 1: they can make a really big difference. And like all
Speaker 1: of a sudden, something's going to go off the rails
Speaker 1: because of a tiny, tiny tweak and there's no such
Speaker 1: it's there's no such thing as objectivity, and because it's
Speaker 1: being made by an algorithm, it's objective and it's perfect.
Speaker 1: Like that just doesn't that's that's r I don't know.
Speaker 1: I just think that human beings are absolutely I just
Speaker 1: mean in algorithms like it's this, it's this myth and
Speaker 1: this you know, misc not misconception, because I think a
Speaker 1: lot of people agree with me. But it's this just
Speaker 1: thinking that, oh, like, because it's like an algorithm and
Speaker 1: a computer, it's free of human bias. And that's just
Speaker 1: not true. It's not free of human bias. It's not
Speaker 1: free of human error. And so you have to think about,
Speaker 1: like who are the people in charge, what kinds of
Speaker 1: choices are they making, what weights are they putting on?
Speaker 1: You know, I'm going to value the life of this
Speaker 1: person versus that person, Nate, if I know that the
Speaker 1: passenger of a weim is a Democrat or a Republican,
Speaker 1: you know, am I going to Am I going to
Speaker 1: make different choices based off the value of their life?
Speaker 1: That sounds I mean, I'm being kind of silly on
Speaker 1: purpose because like, you know, I'm trying to make a
Speaker 1: point where like you understand that these are all objective
Speaker 1: judgments and who knows right in the black box of
Speaker 1: the algorithm how people end up coming up with them,
Speaker 1: And and now imagine that, Okay, fine, like we've we've
Speaker 1: tried to do the best we can we being the
Speaker 1: engineers and the people putting this together. I think we've
Speaker 1: got it, you know, down, and then what happens if
Speaker 1: there's like a camera malfunction or that something is misperceived
Speaker 1: as something else, and you know, car does something horrible
Speaker 1: because it thinks that it's about to hit a dog,
Speaker 1: but it's really not about to hit a dog, right,
Speaker 1: Someone just like left a scooter or something like that.
Speaker 1: Those types of things happen as well, and so you
Speaker 1: have just whenever you a lot of technology, there are
Speaker 1: lots of parts where it can malfunction, it can go wrong.
Speaker 1: So last week, please don't judge me, Nate, I rewatched
Speaker 1: the movie Speed. It didn't hold up very well. I
Speaker 1: have to say I was very disappointed. I'm sorry if
Speaker 1: anyone in the audience is a huge fan of Speed.
Speaker 1: Just it did not hold up very well. The acting
Speaker 1: was horrible. Just everyone was a little bit off. Anyway,
Speaker 1: there's a scene this is relevant. There's a scene in
Speaker 1: Speed and the conceit of the movie. This is not
Speaker 1: a spoiler for anyone who hasn't seen it, because you'll
Speaker 1: figure it out within the first ten minutes. Literally, the
Speaker 1: only conceit of the movie. There's a bomb on the
Speaker 1: bus and the bus cannot go below fifty miles an
Speaker 1: hour if it goes below fifty miles an hour, the
Speaker 1: bus will blow up with everyone on it. That's the
Speaker 1: entire movie. That's the plot. So the bus is going
Speaker 1: and they're all in city streets in La, which obviously
Speaker 1: is not very conducive to going fifty miles an hour.
Speaker 1: By the way night the only implausable point, well, there
Speaker 1: are lots of implausible points of that plot, but somehow,
Speaker 1: on a highway in La it managed to go faster
Speaker 1: than fifty miles an hour. I was like, guys, that's
Speaker 1: just never happening. That is never happening. There's way too
Speaker 1: much traffic in La. Anyway, we're on surface streets, we're
Speaker 1: going over fifty miles an hour, and there's a woman
Speaker 1: with a baby carriage and she is going to get hit.
Speaker 1: The baby carriage is going to get hit by the bus,
Speaker 1: and what we can see as humans, which autonomous vehicle
Speaker 1: might not realize, is that there's no baby in the
Speaker 1: carriage is filled with bottles. So this is, you know,
Speaker 1: someone who was collecting bottles from the street putting them
Speaker 1: in a baby carriage. So the carriage goes flying and
Speaker 1: the bottles shatter, but no baby is actually hurt. But
Speaker 1: I was actually just thinking of this just now when
Speaker 1: we're discussing this. You know, what if that happens and
Speaker 1: you're in an autonomous vehicle and the autonomous vehicle doesn't realize,
Speaker 1: you know, baby carriage doesn't necessarily mean baby. You have
Speaker 1: to kind of figure it out and like does something
Speaker 1: that will endanger all the people in the car on
Speaker 1: the bus because they don't want to hit the baby
Speaker 1: because priority says save baby. It's not even a baby, right,
Speaker 1: but it's in a baby carriage. So you know, all
Speaker 1: of these things. This goes back to what we talk
Speaker 1: about often that a lot of the AI can be
Speaker 1: and all of these things can be really really smart,
Speaker 1: but there are certain things that are second nature to humans.
Speaker 1: We don't even realize we're making these judgments that are
Speaker 1: really really difficult to replicate. Right, there's something that we
Speaker 1: just do unthinkingly, but that it's very hard to program
Speaker 1: into an algorithm, into a computer or teach them how
Speaker 1: to do that correctly.
Speaker 3: Yeah.
Speaker 2: On the flip side, when algorithms make counterintuitive quote unquote
Speaker 2: decisions even when they're correct. And believe me, I'm not
Speaker 2: the world's most unskeptical admirer of algorithms. I think there
Speaker 2: are lots of bad algorithms and bad models out there.
Speaker 2: I've built lots of good models and some bad models myself,
Speaker 2: probably over the years and seeing and seeing them from others.
Speaker 3: Right.
Speaker 2: But you know, I was in Florida, like I said,
Speaker 2: and my friend was driving me back to my hotel
Speaker 2: from dinner. Nice of him, but we'd had a little
Speaker 2: wine at dinner, and he's like, I have a test.
Speaker 2: I'm just gonna turn automated mode on and not touch
Speaker 2: the steering wheel, and like it did pretty well, but
Speaker 2: like it took a route that he would never have
Speaker 2: taken back to the hotel, right, And like he's.
Speaker 3: Like, oh, actually this makes sense.
Speaker 2: I understand why it would do it, and so and so,
Speaker 2: like you know, and that's a very low stakes decision, right,
Speaker 2: But like you know, often with these directions, it's like, Okay,
Speaker 2: if people have this intuition that you want to take
Speaker 2: the most direct route, whereas oftentimes going out of your
Speaker 2: way is in principle faster, you could probably test that
Speaker 2: robustly or not or things like that, right, or maybe
Speaker 2: there's a safety thing that seemed like it's dangerous, Like,
Speaker 2: but if you deviate.
Speaker 3: From like the norm.
Speaker 2: Yea, then then you get punished for it, right, and
Speaker 2: so like it might be that like, obviously these vehicle
Speaker 2: should be regular. It might be that regulations that require
Speaker 2: more like explicit and transparent thinking, yeah, are actually less safe.
Speaker 2: I want to be very careful with that. I don't
Speaker 2: think that's the case for like really large language models,
Speaker 2: for example. I think we need far more transparency with those.
Speaker 2: I think they fail in ways that.
Speaker 3: Are a little unpredictable.
Speaker 2: But the problem is, like, yeah, people will blame these
Speaker 2: cars even if they have like a you know whatever
Speaker 2: twenty or twenty five percent the accident rates that humans do,
Speaker 2: and and and plus you have vested interest, right. You know,
Speaker 2: in New York it's a city with a lot of
Speaker 2: union activity. You know, New York also has these you know,
Speaker 2: amazingly expensive frankly ubers and lifts and things like that.
Speaker 2: It's amazing when you go to like a city like
Speaker 2: like in Florida and you're like, oh, man, eight bucks,
Speaker 2: you get across town. That's amazing, right, Not in the
Speaker 2: case in Manhattan and Brooklyn.
Speaker 1: No, certainly not.
Speaker 3: And we'll be right back after this break.
Speaker 1: Even simple algorithms like you know, what's the best route
Speaker 1: to take. I've been completely screwed over in recent months
Speaker 1: by Google, which will default instead of the fastest it
Speaker 1: will go to the eco friendly way to drive. And
Speaker 1: I'm like, how are you even defining eco friendly? Like
Speaker 1: are you is it because I'm saving gas? Because I'm
Speaker 1: not breaking as much? Am I saving the environment? What
Speaker 1: exactly am I doing? And first of all, I didn't
Speaker 1: say I wanted to do this. Secondly, I don't know
Speaker 1: what you mean by eco friendly, Like third of all,
Speaker 1: just tell me that I'm not going the fastest way
Speaker 1: instead of defaulting to it. So I think that in
Speaker 1: that sense, we want to be explicit in the algorithms.
Speaker 1: And I think you need to make think there. Does
Speaker 1: I see a future where like there's human input into
Speaker 1: it where you're like, I want to take the way mo,
Speaker 1: and I like this is my priority, right, like get
Speaker 1: me here fastest or I don't care. I want to
Speaker 1: see neck drive like whatever it is, Like that would
Speaker 1: be that would be an interesting way of doing it,
Speaker 1: as long as you know the model doesn't lie to
Speaker 1: you and it's not like, oh, this is the fastest way,
Speaker 1: right because I'm programmed to take these other things into consideration,
Speaker 1: but I don't want to piss you off.
Speaker 2: Yeah, look, I mean part of the question is like
Speaker 2: what is the algorithm solving for? Right, So, like I
Speaker 2: now live in the East Village, which is very dependent
Speaker 2: on a couple of crosstown subway lines, like the L,
Speaker 2: and so it's actually pretty good overall for subway service,
Speaker 2: but you're almost always making transfers to get most places,
Speaker 2: and like you know, there is a lot of redundancy
Speaker 2: typically in which transfer or you make.
Speaker 3: And like I don't think.
Speaker 2: The Google subway directions are often very good in terms
Speaker 2: of matching my intuitions about like where's the right place
Speaker 2: to make it? Yeah, a transfer, right, Like you know,
Speaker 2: just the kind of combination of the fact that, like
Speaker 2: some lines are more reliable than others, some some actual transitions, right,
Speaker 2: Like you know Union Square, if you ever in a
Speaker 2: Union Square in Manhattan, all those you know, it's very
Speaker 2: crowd of those platforms, right, And like there are some
Speaker 2: other stops where just like you know, I don't want
Speaker 2: to have to change there if there's another alternative, and
Speaker 2: like I don't think it quite understands that adequately, And
Speaker 2: like the fuzziness around, like I don't actually have to
Speaker 2: get this exact location, just somewhere kind of like near there, right,
Speaker 2: I don't think it's fantastic, but like, what are are
Speaker 2: you trying to sell it for?
Speaker 3: Like time or like ease of use?
Speaker 2: You know, in theory, it's like, Okay, you can get
Speaker 2: there two minutes faster if you transfer twice instead of once. Okay,
Speaker 2: Well a I don't really buy I think there's too
Speaker 2: much risk in each transfer, right, that something's wrong, right,
Speaker 2: take a wrong turn.
Speaker 1: Right.
Speaker 3: Depends on a lot of things.
Speaker 2: But yeah, if you don't know what the algorithm is
Speaker 2: solving for exactly, then it's not garbage in garbage out,
Speaker 2: but it's inherently inadequate to the problem unless you're kind
Speaker 2: of like and probably implicitly it's like some notion of
Speaker 2: like utility or satisfaction or whatever else.
Speaker 1: Yeah, No, I mean I think that and a subway
Speaker 1: problem is much simpler than all the different things that
Speaker 1: a self driving car is going to need to solve
Speaker 1: for on any given basis and at any given point
Speaker 1: in time. So I think that these are just illustrations
Speaker 1: of a lot of the things, a lot of the
Speaker 1: challenges that remain a lot of the value judgments that
Speaker 1: have to be made as this technology gets off the ground. Now,
Speaker 1: I'm also worried, Nate, Let's just imagine, for the sake
Speaker 1: of argument, that everything, like all these algorithms are evolved perfectly,
Speaker 1: right that, And like I said, for the sake of argument,
Speaker 1: let's just say that like they've solved the trolley problem
Speaker 1: and they are optimized, like they're doing everything that we
Speaker 1: think they should be doing, and like the technology is
Speaker 1: now like beautiful and the cars can be driving beautifully.
Speaker 1: There's one other element that I worry about, which is
Speaker 1: security of the actual systems, in terms of hacking and
Speaker 1: in terms of bad actors being able to get into
Speaker 1: these autonomous vehicles, take them over, sabotage them, whatever it is.
Speaker 1: This is not me being conspiracy theory minded. Basically every
Speaker 1: single smart car system has been hacked into at some point,
Speaker 1: including things that seem ridiculous, and not just smart cars,
Speaker 1: smart homes. Right, Like, there are people who like hack
Speaker 1: into homes and like turn your heat to one hundred
Speaker 1: or make it freezing, and do things that seem like
Speaker 1: silly pranks, but they're not right, Like they're doing this
Speaker 1: with a very malicious intent, Like people's toasters, Like anything
Speaker 1: that's smart that's connected to the grid, like has been
Speaker 1: actually taken over at some point, whether to prove a
Speaker 1: point or sometimes for a malicious purpose. And cars, like
Speaker 1: if you think about an autonomous system that can be
Speaker 1: taken over and whether it's cars or something else, like
Speaker 1: this isn't the I mean, it could be the plot
Speaker 1: of a movie, but like this is something that's actually
Speaker 1: not It's quite reasonable to assume that there are bad
Speaker 1: actors who will try to break into any system, just
Speaker 1: like energy system, powagrids, et cetera. Like that's that's something
Speaker 1: that has happened. It will continue to happen, and people,
Speaker 1: you know, in their quest to kind of evolve and
Speaker 1: move forward, safety in that sense is not often the
Speaker 1: number one concern, and so they're end up being you know,
Speaker 1: there end up being vulnerabilities that are only discovered after
Speaker 1: the fact. And if you think about what might happen,
Speaker 1: like it's a constant arms race, you know, between bad
Speaker 1: actor hackers and people trying to you know, the white
Speaker 1: hackers like good actors who are trying to prevent vulnerabilities,
Speaker 1: prevent security technology breaches, and it's this constant war and
Speaker 1: if you think about now a future where you know,
Speaker 1: we have lots of fleets of autonomous vehicles, I think
Speaker 1: that's something that we do need to think about and
Speaker 1: worry about as well. And like I said, like I
Speaker 1: don't think that this is being conspiracy theorist or just
Speaker 1: are very alarmist. I actually think that this is quite real.
Speaker 1: It happens all the time, you know, people things are
Speaker 1: hacked all the time, and as you know, as we
Speaker 1: are more and more reliant on it, you realize just
Speaker 1: how big of an impact hacks can have. They If
Speaker 1: you remember, like six months ago, Las Vegas kind of
Speaker 1: came the strip came to a standstill when someone hacked
Speaker 1: into all the MGM systems. Right, no one could check
Speaker 1: in to their rooms. People couldn't even get into their
Speaker 1: rooms like people could. It was just absolutely a shit
Speaker 1: shown that happened to the Caesar's properties. Turns out, by
Speaker 1: the way, I was a teenager, but like it was
Speaker 1: just an absolute nightmare. And that's just like that's a
Speaker 1: tiny thing.
Speaker 2: Yeah, not fully automating things with AI, because when AI
Speaker 2: systems fail, they don't fail particularly robustly necessarily.
Speaker 3: I mean, there's also just some shit like.
Speaker 2: You know, probably if humans are picking out driving routes
Speaker 2: like from JFK back to Brooklyn or Manhattan, right, probably
Speaker 2: you want some degree your randomization. You know, you probably
Speaker 2: have a mixed strategy in game theory terms, where like
Speaker 2: if everybody takes the most optimal route, then it's still
Speaker 2: longer the most optmal route because it gets more crowded.
Speaker 3: Right, you know, I don't know if there's some of that.
Speaker 3: I was just in.
Speaker 2: Miami estate that can have horrible traffic problems, so they're
Speaker 2: also very like patchy. I think it's more an infrastructure
Speaker 2: matter than like a matter of AI decision making per se. Right,
Speaker 2: But like, but yeah, no, look anything where there's not
Speaker 2: like a kill switch, get I get nervous about, yeah,
Speaker 2: or a redundant backup.
Speaker 1: And oh, by the way, it doesn't it doesn't even
Speaker 1: have to be hacking. By the way, I was just
Speaker 1: thinking about the AWS outage that recently happened, where people's
Speaker 1: smart beds got affected and like people got woken up
Speaker 1: in the middle of the night because the beds started
Speaker 1: doing something really weird and then got caught in these
Speaker 1: ridiculous positions and there was nothing they could do because
Speaker 1: there was no redundancy, and like the entire system had
Speaker 1: gone off.
Speaker 2: It's a smart mattress or smart toothbrush or smart clipper.
Speaker 1: But exactly no, neither do I. But now imagine if
Speaker 1: you have to be in a smart car, right, because
Speaker 1: autonomous vehicles are the thing. That's the thing that I
Speaker 1: actually like that. I think that we don't worry enough about, right,
Speaker 1: Like what happens if there are these outages?
Speaker 3: Well, and we have these coercive like if you go
Speaker 3: I've been in where was I in? Norway? Right?
Speaker 2: When ever the car goes over this feed limit, you
Speaker 2: get like a little same thing in Korea, right, So
Speaker 2: like so that can be a.
Speaker 3: Little bit coercive.
Speaker 2: I don't know, I know how I feel about that, right, Yeah,
Speaker 2: but you're certainly giving like the government a lot of control.
Speaker 1: Over, Yeah, you are, and you're giving up and you're
Speaker 1: also giving up a ton of privacy because you know,
Speaker 1: you're presumably you are associated with you know, all the
Speaker 1: routes you take, everything you do, all your habits. So
Speaker 1: I think there are these downstream concerns that we haven't
Speaker 1: really reached yet because we're still figuring out by we,
Speaker 1: I mean the companies who are designing autonomous cars.
Speaker 3: I don't mean me.
Speaker 1: Personally, but I think we're still figuring out like the
Speaker 1: algorithms and all this stuff. But like, even downstream, there
Speaker 1: are lots of things to be thinking about, safety, security,
Speaker 1: redundancy of you know, all of this technology, privacy concerns,
Speaker 1: control like when when people can actually like override, who
Speaker 1: can override your car? Who can make those types of decisions.
Speaker 1: I think that these are really important questions. By the way,
Speaker 1: there was a huge you know, there have been huge
Speaker 1: outcries in the last few years where all of a sudden,
Speaker 1: features of cars that you paid for stopped working because
Speaker 1: the company decided they wanted you to pay for them,
Speaker 1: like heated seats. Right. Like now, imagine you buy a
Speaker 1: car and you don't have a choice anymore to buy
Speaker 1: something where it's not smart and you can control everything,
Speaker 1: and you're just giving all of that up, and all
Speaker 1: of a sudden, they're like, oh, it's now a subscription model. Sorry, Nate,
Speaker 1: you can't go to where you're going today because you
Speaker 1: haven't paid for the upgrades to this feature. I am
Speaker 1: going to lock your car down. So there are all
Speaker 1: of these different considerations that I don't think you know,
Speaker 1: we've only touched the tip of the iceberg when it
Speaker 1: comes to this. Let us know what you think of
Speaker 1: the show. Reach out to us at Risky Business at
Speaker 1: pushkin dot Fm. Risky Business is hosted by me Maria Kanakova.
Speaker 3: And by me Nate Silver.
Speaker 2: The show was a cool production of Pushing Industries and iHeartMedia.
Speaker 2: This episode was produced by Isaac Carter. Our associate producer
Speaker 2: is Sonya gerwit Lydia, Jean Kott and Daphne Chen are
Speaker 2: our editors, and our executive producer is Jacob Goldstein. Mixing
Speaker 2: by Sarah Bruger.
Speaker 1: If you like the show, please rate and review us
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