Speaker 1: Welcome to tech Stuff, a production from iHeartRadio. Hey there,
Speaker 1: and welcome to tech Stuff. I'm your host Jonathan Strickland.
Speaker 1: I'm an executive producer with iHeart Podcasts and how the
Speaker 1: tech are you? Folks. We have a very very special
Speaker 1: episode of tech Stuff because I have a very very
Speaker 1: special guest with me today, Jacob Goldstein. Now, if I
Speaker 1: were to go and run down his entire resume, it
Speaker 1: would be an entire episode just by itself. He's got
Speaker 1: a long and distinguished career in journalism, and among his
Speaker 1: many accomplishments happens to be the fact that he's the
Speaker 1: host of a podcast called What's Your Problem, which is
Speaker 1: a show that really dives into things like engineering and
Speaker 1: how engineers tackle problems, how do they define them, and
Speaker 1: then how do they create solutions. So Jacob, welcome to
Speaker 1: tech Stuff. Thank you for being here.
Speaker 2: Hi, Jonathan, thanks so much for having me. I'm delighted
Speaker 2: to be here.
Speaker 1: Yeah, I'm delighted you're here too. And before we really
Speaker 1: dive into a full discussion, we're going to talk a
Speaker 1: lot about engineering and a lot about AI in particular,
Speaker 1: because many of your episodes in this last season of
Speaker 1: your show have been on AI. I want to learn
Speaker 1: more about you, So tell us a bit about your
Speaker 1: background and how you came to become a podcaster on
Speaker 1: What's Your Problem.
Speaker 2: So, before I started What's Your Problem, I was one
Speaker 2: of the co hosts of a podcast called Planet Money,
Speaker 2: which is a show about economics. And before I had
Speaker 2: that job, I didn't know that much about economics. You know,
Speaker 2: I was an English major in college. I'd covered healthcare
Speaker 2: for the Wall Street Journal, and so getting there and
Speaker 2: covering economics for a while, to me, the big exciting
Speaker 2: idea at the heart of economics is the pie can
Speaker 2: get bigger, right, everybody can be better off. The world
Speaker 2: is not a zero sum game, and I think that
Speaker 2: is a very non intuitive, big exciting idea. And basically
Speaker 2: the way we all get better off in the long
Speaker 2: run is through technology. Right, It's through people figuring out
Speaker 2: more efficient ways to do things, ways so that you know,
Speaker 2: we do the same amount of work basically, but we
Speaker 2: get more stuff. You get more output for every hour
Speaker 2: of labor. And that is fundamentally you know, engineering and technology,
Speaker 2: as you said, And so I wanted to go deeper
Speaker 2: on like how that actually works, Like, you know, there
Speaker 2: are people whose job is I'm going to go to
Speaker 2: work and like figure out a better way to do something,
Speaker 2: and so that is what I'm trying to do on
Speaker 2: What's Your Problem? Those are the kind of people I
Speaker 2: talk to on the show.
Speaker 1: That's awesome. I hear what you're saying because it resonates
Speaker 1: a lot with a lot of the stuff we talk
Speaker 1: about here on tech stuff. It is to me one
Speaker 1: of the key funny components about technology is how everyone
Speaker 1: anticipates the next big technology development means that their jobs
Speaker 1: are going to be way less labor intensive, and then
Speaker 1: often it turns out like, well, sure each individual task
Speaker 1: is easier, but now you're doing way more tasks because
Speaker 1: everyone's more efficient. So if you remember, because I'm not
Speaker 1: going to put an age on you, Jacob, but I
Speaker 1: will say that I am of a certain age where
Speaker 1: I remember the concept of the paperless office, yes, and
Speaker 1: how we were going to get to this incredibly efficient thing,
Speaker 1: and that maybe, like maybe your workday would be reduced
Speaker 1: to maybe three hours a day. Turns out that that
Speaker 1: was perhaps a bit idealistic on the part of the
Speaker 1: workers and not the way it worked out.
Speaker 2: Yes, although I mean there is a lot less paper
Speaker 2: to start with us, right, Like I am old enough
Speaker 2: to remember, like when I started working, everybody had like
Speaker 2: a file drawer by their desk and like hanging files
Speaker 2: with papers in them, so there is less paper. I mean,
Speaker 2: the sort of less work thing is interesting, right, because
Speaker 2: on the one hand, people are like, oh, I hope
Speaker 2: technology you'll mean I have less work. By the same token,
Speaker 2: people are like, oh I hope technology doesn't take my job, right, Yes,
Speaker 2: In fact, the basic mechanism is like technology the happy
Speaker 2: story anyways, the story that we hope happens is technology
Speaker 2: makes us more productive, not so that we can work less,
Speaker 2: but so that our output can be greater, right, Like
Speaker 2: you know, I did not start working on podcasts and
Speaker 2: radio in the real to real tape era, but I
Speaker 2: know people who did, and like they talk about how
Speaker 2: long it took to literally cut tape by hand, and
Speaker 2: you can cut a lot more tape now that it's
Speaker 2: not actual tape. Right, that's a productivity game.
Speaker 1: Yes, totally. It's so funny to kind of see these
Speaker 1: changes over time and the different perceptions that go into
Speaker 1: whether we was it going to be like versus what
Speaker 1: it actually turns out to be. We're gonna as I said,
Speaker 1: talk about AI a lot, and to your point, one
Speaker 1: of the things that I hear repeated about AI in
Speaker 1: general and specifically within the realm of robotics and AI
Speaker 1: is that its ideal role is to tackle tasks that
Speaker 1: fall into the three d's, which are dirty, dangerous, and dull.
Speaker 1: That these are technologies that are best suited to take
Speaker 1: on jobs that are perhaps less desirable for humans for
Speaker 1: various reasons, whether they could potentially cause injury, or they're
Speaker 1: not very rewarding, that sort of thing, And I really
Speaker 1: like that concept. The fear everyone has, obviously is that
Speaker 1: it's tackling everything. It's non discriminate, it's not looking at
Speaker 1: just the three d's, it's looking at every possible option.
Speaker 1: And you're seeing a lot of discourse, at least I
Speaker 1: am online where I see a lot of people saying,
Speaker 1: why aren't we looking at how to automate c suite jobs?
Speaker 1: Because it seems to me like a lot of the
Speaker 1: duties that c suite executives have are ones that would
Speaker 1: be best suited for some of the AI tools we're
Speaker 1: talking about. Why are we talking about eliminating these lower
Speaker 1: level jobs when some of these upper level ones particularly
Speaker 1: when you have stories about c suite executives who perhaps
Speaker 1: had a less than stellar run at the top of
Speaker 1: the company ladder, like maybe the company didn't perform as
Speaker 1: well as it should have, and yet they still retire
Speaker 1: with these massive packages. So it's funny to see how
Speaker 1: the perceptions of things like automation and AI are shaping
Speaker 1: social discussions in that way.
Speaker 2: Well, I think certainly. I mean, the rise of large
Speaker 2: language models, you know lms like chat GPT have shifted
Speaker 2: that conversation some, right. I think if you go back
Speaker 2: five years, people thought about, you know, automating warehouse jobs.
Speaker 2: But what I've seen in the last say year or
Speaker 2: so since chat GPT, you know, stormed, our discourse is
Speaker 2: people are talking a lot more about journalists and lawyers
Speaker 2: being automated away, right, and plausibly to some extent, plausibly
Speaker 2: to some margin. I mean. The other thing is like,
Speaker 2: do you want a robot boss? When people talk about like,
Speaker 2: I haven't heard about people talking about automating the c suite,
Speaker 2: but like, sure, bad bosses are bad, and overpaying bad
Speaker 2: bosses is bad. I actually think the role of a
Speaker 2: good boss, of a good CEO is largely to be
Speaker 2: a human being, right, is not fundamentally about you know,
Speaker 2: assessing the data and making the best decision, although obviously
Speaker 2: that's important. It's to be there to you know, talk
Speaker 2: to people, essentially, to be in the room to tell
Speaker 2: people that things are going to be okay. And that
Speaker 2: seems like the set of domains that are less likely
Speaker 2: to be automated, certainly in the short run.
Speaker 1: I think you're absolutely right. I think it's based on again, perception, right.
Speaker 1: A lot of people don't have like FaceTime with CEOs,
Speaker 1: and so their perception is from a distance, and they're
Speaker 1: looking at the effects that the CEO decisions are having
Speaker 1: on a broad level, especially in the wake of something
Speaker 1: like a round of layoffs, for example, Whereas you and
Speaker 1: I have had the opportunity to speak face to face
Speaker 1: and you find out very quickly these CEOs are human beings,
Speaker 1: some more so than others. I mean, there are some.
Speaker 1: There are some CEOs out there who I suspect maybe
Speaker 1: at least part cyborg, but.
Speaker 2: Could be had happen.
Speaker 1: Yeah, yeah, most of them. I mean, I'm not sure
Speaker 1: if Elon Musk is even robotic, he might be alien.
Speaker 1: I'm not entirely certain. But there are a lot of
Speaker 1: them out there. When you just have a short conversation
Speaker 1: and you realize these aren't just talking points. For a
Speaker 1: lot of these leaders, they sincerely believe their mission statement,
Speaker 1: or they sincerely believe in the strategies that they're following,
Speaker 1: and they sincerely feel bad when they have to make
Speaker 1: decisions that lead to things like layoffs. But when you
Speaker 1: are at more of a distance, I think it's easier
Speaker 1: to kind of dehumanize the person. And it's understandable, right
Speaker 1: you see those big effects and you just think this, Meanwhile,
Speaker 1: the CEO is potentially making enormous amounts of money. I'm
Speaker 1: reminded of a former CEO I worked for, not a
Speaker 1: former CEO. He's still a CEO, but he's a find,
Speaker 1: my former boss, David Zaslov. And whenever I see any
Speaker 1: stories about him, I sit there and think, well, I've
Speaker 1: met the man, I've had conversations with him. I know
Speaker 1: a little bit more. I feel like I could give
Speaker 1: a bit more perspective to this. But at the same time,
Speaker 1: you're not entirely wrong with some of the conclusions you've drawn.
Speaker 2: Yeah, and I mean, you know, to some extent, like
Speaker 2: I mean the sub I feel like a lot of
Speaker 2: the subtext of what you're talking about is inequality.
Speaker 1: Right.
Speaker 2: It's the gas between what CEOs make and whatever the
Speaker 2: median worker at their company makes, and that indeed ballooned
Speaker 2: out a lot at the end of the twentieth century
Speaker 2: and has stayed quite wide obviously, and to some extent,
Speaker 2: that is an effect of technology, right, although it's complicated,
Speaker 2: there's a lot of it is norms. Right. Yeah, that's
Speaker 2: a pretty subtle one.
Speaker 1: Yeah, that's another thing that as communicators about things that
Speaker 1: are in the technological space, often we do need to
Speaker 1: take a step back away from just the technology and
Speaker 1: acknowledge these other components that impact the entire direction of tech.
Speaker 1: I mean, I'm sure as someone who has looked into
Speaker 1: Silicon Valley you have seen how things like social norms
Speaker 1: and politics and even things like living expenses in San
Speaker 1: Francisco have a big impact on these sorts of things,
Speaker 1: and people can get frustrated when I step back and
Speaker 1: talk about these elements. But my argument is that you
Speaker 1: can't really have a full understanding of technology unless you
Speaker 1: also take into account these other things that do have
Speaker 1: an impact. But one of the things I wanted to
Speaker 1: ask you about so what's your problem. You are talking
Speaker 1: with a lot of problem solvers, obviously, and I was curious,
Speaker 1: now that you've spoken with quite a few people who
Speaker 1: either come directly from engineering or have kind of an
Speaker 1: engineering perspective, what's your take on engineers, Because I know
Speaker 1: that's a general question and not everyone falls into the
Speaker 1: same bucket, But I have a love for engineers in
Speaker 1: the way that they approach things.
Speaker 2: Yes, same, And you know, I also love engineers. And
Speaker 2: as I mentioned, I was an English major. I am
Speaker 2: not an engineer at all. I'm not even good at
Speaker 2: fixing things around the house, although I try. But in college,
Speaker 2: I took one computer science class my last term of college.
Speaker 2: It was just the intro class. And on the first
Speaker 2: day it was, you know, big lecture class. The professors
Speaker 2: computer scientist was talking about his grading system and it
Speaker 2: was this weird thing was like a check and a
Speaker 2: check plus. And he said the top grade is a
Speaker 2: plus plus and that is for code that makes me weep,
Speaker 2: like cause it's so beautiful, you know, so erien. And
Speaker 2: that was like a revelation to me because you know,
Speaker 2: as an English major, as a non engineer, I always
Speaker 2: thought of engineering as like, oh, a thing works or
Speaker 2: it doesn't work. The building falls down or it doesn't
Speaker 2: fall down. But this idea that there is elegance and
Speaker 2: beauty in the construction of the thing itself was really
Speaker 2: exciting to me and remains really exciting to me. And
Speaker 2: engineers really are like that, you know, like they love
Speaker 2: building things and they find beauty in an elegant solution,
Speaker 2: the way other people find beauty in a song or
Speaker 2: a poem or a painting.
Speaker 1: I love that. I would say there's like a spectrum
Speaker 1: in engineering as well, where you have sort of the artists,
Speaker 1: who are the ones who are very carefully creating and
Speaker 1: refining their code, and then maybe you have on the
Speaker 1: punk rock side the hackers who didn't necessarily build the thing,
Speaker 1: but they really want to know how the thing works,
Speaker 1: and they will take the thing down to the very
Speaker 1: base level of the structure, and then they'll say, what
Speaker 1: if I rebuild it so it does something else? Like
Speaker 1: I just think that entire culture, from the artists to
Speaker 1: the punk rockers, who, hey, they're artists. I'm a punk
Speaker 1: rock kind of fan myself, but I always find that
Speaker 1: to be a wonderful way to have a conversation is
Speaker 1: to talk about people and about how they're approach to
Speaker 1: sort of stuff. And I also always say whenever I
Speaker 1: talk with engineers, I come away with the feeling that
Speaker 1: they view the world, as you can think of it,
Speaker 1: as either a set of problems or a set of challenges,
Speaker 1: and they're constantly thinking about solutions, which is nice because
Speaker 1: I am unfortunately one of those people who's far more
Speaker 1: likely to point out a problem but not have a
Speaker 1: solution for you, right. So to talk to someone who's
Speaker 1: already thinking ahead about how to solve the problem, not
Speaker 1: just that there is a problem, I always find that
Speaker 1: very inspiring.
Speaker 2: Yeah, it's nice. I guess I also have tended to
Speaker 2: be problem focused, you know. I sort of came up
Speaker 2: in my career as a journalist, which is essentially all
Speaker 2: about pointing out problems right to a significant degree if
Speaker 2: you read the paper. Basically, what's going on in many
Speaker 2: many stories is here is a thing that is bad,
Speaker 2: and so talking to people who are trying to make
Speaker 2: things better or fix things is great. And you know,
Speaker 2: to be clear, I don't want to be too Pollyanna
Speaker 2: issue here, Like there are plenty of engineers who build
Speaker 2: things that on net don't help the world right, Building
Speaker 2: new things is not always helpful to the world, and
Speaker 2: there are certainly engineers who become enamored of just building
Speaker 2: the thing and don't think about what it might mean.
Speaker 2: And frankly, you know, in choosing who to talk to
Speaker 2: for the show, I do try and talk to people
Speaker 2: who I think are some combination of well cognizant of
Speaker 2: what they're building and actually trying to do a good thing,
Speaker 2: and you know, aware of the fact that there might
Speaker 2: be unintended bad consequences of the thing that they're building.
Speaker 2: Maybe I don't always succeed, but it is a useful
Speaker 2: frame totally.
Speaker 1: I also tend to think about how when engineers build
Speaker 1: things that make sense to them, assuming that's they're building
Speaker 1: something that ultimately is supposed to be used by the
Speaker 1: general public, the great ones will take into account how
Speaker 1: a quote unquote normal person would approach the whatever it is.
Speaker 2: Like.
Speaker 1: I'm thinking of user interfaces in particular, and that making
Speaker 1: sure that the user interface is going to make sense
Speaker 1: to enore me as opposed to an engineer. And then
Speaker 1: there are other engineers or in fact entire companies where
Speaker 1: they will build things that work great. If you're an
Speaker 1: engineer they're fantastic if you're an engineer. If you're not
Speaker 1: an engineer, it may require a bit more work on
Speaker 1: your part. I'm looking specifically at Google and the Android
Speaker 1: operating System because I'm an Android user, but at the
Speaker 1: same time, I fully recognize that iOS is an operating
Speaker 1: system that is so intuitive. You can literally hand an
Speaker 1: iOS device to a child and they will have it
Speaker 1: figured out in no time. You can hand an Android
Speaker 1: device to someone and they will spend a lot of
Speaker 1: time asking questions about how to do things and how
Speaker 1: to access things. And it's not that the Android operating
Speaker 1: system is bad, it's not that it's worse than iOS.
Speaker 1: If you happen to be an engineer, Android is awesome,
Speaker 1: and if you're not, it's still awesome. But you have
Speaker 1: to put in work to get to realize that. And
Speaker 1: it really differentiates the two, right because Apple has always
Speaker 1: had a focus on how can we make this into
Speaker 1: a product that people realize they need, even if they
Speaker 1: never had that need before. And Google's like, how can
Speaker 1: we make this so that it's really powerful and that
Speaker 1: it does what we wanted to do? But it may
Speaker 1: require a little bit of work on the user's part.
Speaker 1: In order to have it work out. So that's sort
Speaker 1: of also a fascinating thing about engineering that I've really
Speaker 1: loved to look at and to talk about. I don't
Speaker 1: necessarily think one is better than the other, apart from
Speaker 1: the fact that one is just much easier for the
Speaker 1: general public to kind of glom onto. As much as
Speaker 1: I love Android, I would never say that it's more
Speaker 1: user friendly than iOS.
Speaker 2: Yeah, I mean there's a few ways of thing about that, right,
Speaker 2: Like somebody a long time ago told me there's a
Speaker 2: phrase people use sometimes the user is never wrong, right,
Speaker 2: which is which is an interesting framework. And I think
Speaker 2: they told it to me to be nice, because I
Speaker 2: was like trying to do something in a radio studio
Speaker 2: and I couldn't figure it out. And it was an
Speaker 2: engineer who was like a thoughtful guy. I said, no, no,
Speaker 2: you're not bad at this, it's just not set up well.
Speaker 2: I mean, the other way of thinking about it, in
Speaker 2: terms of the Android versus io ask question is as
Speaker 2: an optimization problem. Right, if you're an engineer, then the
Speaker 2: question is, well, are we optimizing for sort of the
Speaker 2: mobile operating system that can do the most things or
Speaker 2: be the most flexible, or the mobile operating system that
Speaker 2: is just like bulletproof. You can give it to anybody
Speaker 2: in any language and they will immediately understand what it
Speaker 2: is and you get different sort of solutions depending on
Speaker 2: what you're optimizing for.
Speaker 1: Yeah, that's true. Like you've identified whatever your goal is,
Speaker 1: so obviously the execution is going to be different. Well,
Speaker 1: I'm glad that you weren't told that the problem was
Speaker 1: between keyboard and chair, which is the other classic classic answer.
Speaker 2: Wait a minute, that's me.
Speaker 1: Yeah, I've received that particular one more than more than
Speaker 1: once in my lifetime. Well, now you have a reply.
Speaker 1: Now you have to reply yes, yeah, yes, that's true.
Speaker 1: The user is never wrong. We've got a lot more
Speaker 1: to talk about, Jacob and I, but before we get
Speaker 1: to the rest of our conversation, we need to take
Speaker 1: a quick break to thank our sponsors. Your show covers
Speaker 1: all realms of technology, not just AI, but because this
Speaker 1: past year has been undeniably AI centric when it comes
Speaker 1: to tech news, clearly a lot of your episodes do
Speaker 1: tackle AI, and as someone else who tries to communicate
Speaker 1: technology in a way that's really accessible and understandable, one
Speaker 1: of the things I frequently run into is that talking
Speaker 1: about AI in a responsible way is in itself challenging.
Speaker 1: But I'm curious to hear what your take is when
Speaker 1: it comes time for you to communicate about AI. How
Speaker 1: do you perceive that and how do you approach it?
Speaker 2: A thing in general that I try and do on
Speaker 2: my show and certainly with respect to AI, is to
Speaker 2: go narrow essentially right, Like I'm not going on my
Speaker 2: show and saying here is what AI is and here's
Speaker 2: what's going to happen. I'm talking to people who are
Speaker 2: typically building a company to do a specific thing with AI.
Speaker 2: Not quite always, but usually right. And so that to
Speaker 2: me is a helpful way to well, AA say something
Speaker 2: new because so many people are making so many broad
Speaker 2: statements about AI and b steer clear of you know,
Speaker 2: over generalization. How about you, I mean, what's your take?
Speaker 1: So my concern is I always want to avoid being
Speaker 1: reductive because AI is such a huge discipline and it
Speaker 1: involves so many different aspects, that is very easy to
Speaker 1: fall into the trap because I mean, clearly we see
Speaker 1: this in mainstream media all the time. Not that I
Speaker 1: blame them, but they're just they're taking some shortcuts where
Speaker 1: they'll use the term artificial intelligence. It almost implies that
Speaker 1: what they're talking about is the end all be all
Speaker 1: of artificial intelligence. And usually they're talking about generative AI.
Speaker 1: In the last year, I would say, like, that's been
Speaker 1: the biggest topic in artificial intelligence, but it's one topic,
Speaker 1: and that AI actually covers a lot more than that,
Speaker 1: and it falls into so many other buckets too, Like
Speaker 1: robotics obviously has a lot to do with AI. That
Speaker 1: not always you can have a fully remote controlled robot,
Speaker 1: but often there's some AI components there, things like assisted vision,
Speaker 1: brain computer interfaces. I mean, there's so many different things
Speaker 1: that don't have anything to do with generative AI. That's
Speaker 1: still at least touch on AI.
Speaker 2: Doing a Google search, yeah, like exactly.
Speaker 1: Yeah, anything that you're getting into, like automation, I mean
Speaker 1: you can get a.
Speaker 2: Lot recommending a show to you. Yeah, blind spot monitoring
Speaker 2: system saying there's a cardex too. Like these are all side.
Speaker 1: The Yeah, they're all they're all different aspects of AI.
Speaker 1: And like you could argue, well, sometimes you get a
Speaker 1: little fuzzy. I'm like, well, so is the word intelligence?
Speaker 1: Like intelligence itself is a fuzzy term. I thought that.
Speaker 2: Would be better off if the AI did not exist
Speaker 2: me too, like I think it's an unfortunate choice of
Speaker 2: words that is unhelpful.
Speaker 1: Ultimately, it's I think largely because everyone starts to jump
Speaker 1: to They jump to science fiction, they jump to Skynet,
Speaker 1: they jump to Terminator, they jump to this idea of
Speaker 1: something that appears to think the way humans do. And
Speaker 1: of course we don't even know if we ever reach
Speaker 1: strong AI or general AI, however you want to define it.
Speaker 1: We don't know if it's going to quote unquote think
Speaker 1: like a human, or even think at all. It may
Speaker 1: just be indistinguishable to us from the way humans think.
Speaker 1: And I think Touring would argue, well, that's good enough.
Speaker 1: It doesn't matter. If it's indistinguishable, then it might as
Speaker 1: well be.
Speaker 2: You quoted somebody on your show a while back as
Speaker 2: saying something like intelligence is whatever machines can't do yet,
Speaker 2: which I thought was pretty good.
Speaker 1: Yeah. I think that it's very similar to how a
Speaker 1: lot of philosophers define consciousness, right They say, like, ugh,
Speaker 1: we don't.
Speaker 2: Know what consciousness is.
Speaker 1: All we do. All we do is we define what
Speaker 1: consciousness isn't we haven't gotten to a point where we
Speaker 1: can say what consciousness is. We get we chip away
Speaker 1: So what we're doing is we've got the marble slab, yeah,
Speaker 1: and we're chipping at it, but we haven't yet seen
Speaker 1: the statue that's living under the slab. Yet we're still
Speaker 1: just chipping.
Speaker 2: I was already worried about intelligence. As you say consciousness,
Speaker 2: I like, I don't even know what to do with
Speaker 2: that one.
Speaker 1: Oh no, well, I mean it often goes hand in
Speaker 1: hand with AI, right, because people immediately assure that intelligence
Speaker 1: and self awareness go hand in hand with one another,
Speaker 1: and maybe it will. We don't know. That's the point.
Speaker 1: We don't know. But long story short, to answer the
Speaker 1: question of how I approach this, I usually go where
Speaker 1: I start from the broad foundation and then I go narrow.
Speaker 1: So I start with saying, first we need to acknowledge
Speaker 1: that artificial intelligence is a very very big field, and
Speaker 1: that this is one aspect of AI, and that we're
Speaker 1: not going to talk about the other aspects of AI,
Speaker 1: but we need to remember they exist, and that while
Speaker 1: the thing we're talking about is important, and while it
Speaker 1: has its own set of challenges and potential, you know,
Speaker 1: rewards and risks and all the things that go with it,
Speaker 1: it's one part. It's like if you were to say,
Speaker 1: you wouldn't hold up a remote control and say this
Speaker 1: is all of technology. Right, This is one thing that's
Speaker 1: a technological gadget, but it doesn't represent all of technology.
Speaker 2: First of all, you'd have to find it, right, that's.
Speaker 1: True, which you know, you know, you got to have
Speaker 1: some sort of method to figure out where it is.
Speaker 1: This is, by the way, is why three D television
Speaker 1: never became a thing. Who wants to look for glasses
Speaker 1: so that they can watch True Detective season four? Not me.
Speaker 1: So yeah, that's kind of my approach. And so I
Speaker 1: don't think that our approaches are that different. I think
Speaker 1: that we're pretty similar. And it's that I think we
Speaker 1: both feel there's a responsibility to make certain that we
Speaker 1: never overgeneralize or be reductive, because that feeds into a
Speaker 1: narrative that I think actually contributes to the old fud
Speaker 1: the fear, uncertainty, in doubt. And while there are things
Speaker 1: to certainly be concerned about and to be aware of,
Speaker 1: we don't want to rush into anything, you know, with
Speaker 1: a poor understanding of the situation. I think that's true
Speaker 1: whether you're you know, really enthusiastic and excited about AI,
Speaker 1: I think it's true if you are really concerned about AI.
Speaker 1: I think, you know, taking critical thinking and a really
Speaker 1: methodical approach is absolutely key if you want to avoid pitfalls.
Speaker 2: Sure, it seems hard to argue against critical thinking and
Speaker 2: a methodical approach, right, Who's going to take the other
Speaker 2: side of that one?
Speaker 1: I mean, Mark.
Speaker 2: Fair enough, everybody, ye, fair enough?
Speaker 1: Like Sam Altman maybe?
Speaker 2: I mean, so it is interesting to think about the
Speaker 2: Sam Altman, the head of Open AI. I mean, one
Speaker 2: of the really interesting things to me about AI. And
Speaker 2: that seems different in particular when you know, we're talking
Speaker 2: about engineers, Like, I feel like the extent to which
Speaker 2: the engineers working on AI are worried about AI is
Speaker 2: really interesting and different, right, I feel like the traditional
Speaker 2: kind of engineer stance is like, this thing is cool,
Speaker 2: let's build it right again. That's obviously reductive and somewhat unfair,
Speaker 2: but whatever. Whereas with AI, to some significant degree, many
Speaker 2: of the people who are most worried about it are
Speaker 2: the people who understand it the best. And you know,
Speaker 2: I've heard people are you like, oh, that's just marketing,
Speaker 2: and that doesn't seem true to me. First of all,
Speaker 2: why would you market a thing by saying we should
Speaker 2: be worried about it. And second of all, if you
Speaker 2: just look like open ai was started as a nonprofit
Speaker 2: and then they you know, needed more money, but they
Speaker 2: became this weird capped profit model, and then people left
Speaker 2: open ai to start anthropic, which is another one of
Speaker 2: the big ones, because they thought open ai wasn't worried enough.
Speaker 2: And then you had, you know, people calling like Elon
Speaker 2: Musk calling for a six month pause on AI development.
Speaker 2: And so I do think that people who know a
Speaker 2: lot of it about AI are in fact really worried
Speaker 2: about it, which is just interesting on its face, indifferent
Speaker 2: than the way technology often works.
Speaker 1: Yeah, there are a lot of conspiracy theories that pop
Speaker 1: up or fringe theories that pop up around this. By
Speaker 1: the way, like you have the ones who say, well,
Speaker 1: they say they're worried about AI because what they're trying
Speaker 1: to do is shape the discussions around regulations so that
Speaker 1: their own personal organization ends up benefiting from those regulations
Speaker 1: while those same regulations slow down smaller companies that are
Speaker 1: in the space. You had people saying, well, Elon Musk, yes,
Speaker 1: he was arguing that there needs to be a pause,
Speaker 1: but it's because he was launching his own AI company,
Speaker 1: and he wanted a chance to be able to catch up.
Speaker 1: Like You've got a lot of other fringe theories out there,
Speaker 1: and I understand that there may be, you know, some
Speaker 1: credibility to some of those who knows. But I think
Speaker 1: when it gets to a point where the board of
Speaker 1: directors of open Ai get together and decide seemingly spontaneously
Speaker 1: that they're going to get rid of the CEO and
Speaker 1: co founder of the company and then do so, I
Speaker 1: think that speaks to a genuine and sincere concern that
Speaker 1: perhaps the organization is moving in a direction that they
Speaker 1: feel as fundamentally counteractive to what they had intended. And
Speaker 1: of course we know they subsequently had to reverse that
Speaker 1: decision and step down from the board of directors because
Speaker 1: the overwhelming support within the organization was to that co
Speaker 1: founder and CEO seemingly embracing this new approach toward developing
Speaker 1: AI that was a departure from the original organization's intent.
Speaker 1: But to your point, the fact that the board of
Speaker 1: directors was willing to do such an extreme move, even
Speaker 1: though it was on a Friday at the end of
Speaker 1: a news cycle, even that they were willing to do
Speaker 1: that knowing that it would lead to them having to
Speaker 1: leave the organization. I think that speaks to me a
Speaker 1: genuine concern. You don't go and remove a co founder
Speaker 1: and CEO for a small reason.
Speaker 2: Yeah, I mean sure, I'm sure that some amount of
Speaker 2: the public worrying over AI by people in the field
Speaker 2: is some kind of self interested behavior. But I think overall,
Speaker 2: there are clearly a lot of people who know a
Speaker 2: lot who are even building these things, who are genuinely worried.
Speaker 2: Like that seems obviously true.
Speaker 1: Yeah, And honestly, like anytime someone's building a technology and
Speaker 1: they're they're bringing concerns up to me, that's a good thing.
Speaker 1: And it doesn't necessarily mean that the technology is ultimately
Speaker 1: harmful or not beneficial. But you know, I think it's
Speaker 1: a responsible person who does ask those questions. For one thing,
Speaker 1: it really can save you a lot of time and
Speaker 1: heartache further down the line, if you're tackling these kinds
Speaker 1: of things before they've escalated to a point where they're
Speaker 1: actively causing catastrophe. So I like seeing that. Whether it
Speaker 1: ends up being merited or not, well, that's just sort
Speaker 1: of a curse we have to bear, right, What if
Speaker 1: it turns out that it was never merited, we won't know.
Speaker 1: And if it turns out if it was merited, we
Speaker 1: still don't know. Because they asked the questions ahead of
Speaker 1: time and fix the problems before they became problems. And
Speaker 1: it's only if we take the other path that we
Speaker 1: find out for sure, like WHOA, we should have thought
Speaker 1: of this before we did it.
Speaker 2: I mean, you know, tools are complicated, right. People come
Speaker 2: up with new tools, and then other people use those
Speaker 2: tools in various ways, some of which enhance human well
Speaker 2: being and some of which cause new miseries. And plainly
Speaker 2: AI will do.
Speaker 1: Both, yes, and so it really becomes important that we
Speaker 1: are really good stewards of the technology and we're paying attention,
Speaker 1: that we're calling things out and we're addressing them as
Speaker 1: they come up. I don't think that we're that close
Speaker 1: yet to the doom day problem of the superhuman intelligent
Speaker 1: AI that's stuck in a box and then is convincing
Speaker 1: people to let it out of the box. I don't
Speaker 1: think we're close to that yet. I mean, even quote
Speaker 1: unquote dumb AI can do terrible things if it's poorly implemented. Right,
Speaker 1: We've seen that, We've seen accidents with autonomous cars, which
Speaker 1: show that AI can make bad choices sometimes because perhaps
Speaker 1: it encounters a scenario that no one anticipated, because as
Speaker 1: it turns out, reality has far more variables than we
Speaker 1: can account for when we're designing things and then something
Speaker 1: terrible happens. That doesn't mean that the technology itself is
Speaker 1: deeply flawed or bad, but it does highlight that we
Speaker 1: constantly have to be asking how can we make it better,
Speaker 1: and how can we make it safer, and how can
Speaker 1: we make it so that it's actually benefiting us and
Speaker 1: not just causing you know, maybe a little bit of
Speaker 1: benefit but a larger amount of harm. Yeah.
Speaker 2: I mean autonomous cars you mentioned are an interesting one,
Speaker 2: right because plainly there have been, you know, tragic crashes
Speaker 2: by autonomous cars. One question there is what are we
Speaker 2: benchmarking them against? Right, Like, there are tragic crashes with
Speaker 2: non autonomous cars every hour of every day, and so
Speaker 2: in a sort of if people were just mathematically rational optimizers,
Speaker 2: we would all say, okay, well, let's see if you know,
Speaker 2: over a million hours of driving, autonomous cars are safer
Speaker 2: or less safe than human drivers. That's clearly not what's happening.
Speaker 2: People clearly favor human drivers for some complicated set of
Speaker 2: human reasons. And we're not obviously benchmarking autonomous cars against humans, who,
Speaker 2: by the way, are terrible drivers. Like one thing about
Speaker 2: human beings. We're really bad at driving.
Speaker 1: Yeah, if you look at the stats in the United
Speaker 1: States for the number of fatalities and injuries that result
Speaker 1: from car accidents that are just human error caused car accidents,
Speaker 1: it's a staggering number. And when you think how much
Speaker 1: that could be reduced through autonomous cars, and you imagine, well,
Speaker 1: think of the ripple effect. It's not just the idea
Speaker 1: that those people who died would still be alive, which
Speaker 1: on its own is already a phenomenal thing to talk about.
Speaker 1: That means that the impact on those people's friends and
Speaker 1: families that would not have happened. It means that the
Speaker 1: impact on whatever their place of employment was that would
Speaker 1: not have happened. They would be contributing members of society.
Speaker 1: That would be a phenomenal change there. So when you
Speaker 1: start thinking about that, you realize the overall benefit is
Speaker 1: so huge that it only makes sense to really pursue
Speaker 1: autonomous vehicles. And as long as the data does show
Speaker 1: that in fact, they are better drivers per million miles
Speaker 1: than humans are, and that to me is something I
Speaker 1: try and keep in mind. You have to balance it out.
Speaker 1: I think it's the same thing as people who really
Speaker 1: flip out when they go on a flight they are
Speaker 1: not directly in control of the plane typically. I mean,
Speaker 1: if they're flipping out, whether they're the pilot, that's a
Speaker 1: whole different issue. But if you're going on a flight
Speaker 1: and you flip it out because you lack a sense
Speaker 1: of control, I feel like that's a very similar thing
Speaker 1: to how people feel when they're thinking about autonomous cars.
Speaker 1: It's that somehow the fact that someone's not in control
Speaker 1: brings up something very scary to a lot of people.
Speaker 1: It also raises other questions obviously, like accountability. Who do
Speaker 1: you hold accountable in these cases? I mean, there are
Speaker 1: a lot of questions that as a society we have
Speaker 1: to solve. It's not just the technology. But yeah, I
Speaker 1: agree with you that that gets complicated because it involves
Speaker 1: a lot of human feelings. And once you get to
Speaker 1: human feelings, the whole data and stats and everything kind
Speaker 1: of falls away. It's hard to convince someone who has
Speaker 1: a deep seated distrust of changing their mind just by
Speaker 1: showing them data, because they're always going to think of
Speaker 1: the things that fall outside the norm as being more
Speaker 1: important than the norm. Right, So if the accident rate
Speaker 1: per million miles is let's say, one tenth of what
Speaker 1: it would be for humans, they would still be looking
Speaker 1: at that tenth and not the nine tenths. Right.
Speaker 2: People don't think statistically, right they yeah, clearly, Like in general,
Speaker 2: statistics don't convince people of the way the world works.
Speaker 2: I do think, I mean I would have thought autonomous
Speaker 2: vehicles would have developed faster, right. They are the classic
Speaker 2: thing that's been five years away for fifteen years. Yeah,
Speaker 2: and they still feel five years away. Maybe they feel
Speaker 2: a little farther right, like five years ago they really
Speaker 2: felt five years away. It's like, okay, but this time
Speaker 2: we mean it. Look we got these, you know things
Speaker 2: for have around San Francisco. But I do think like
Speaker 2: that one which is AI by the way, right, it's
Speaker 2: basically computer vision is essential to that to autonomous cars. Uh,
Speaker 2: I feel like that one's gonna happen, don't you like?
Speaker 2: And yes, people be worried about it, but it's the
Speaker 2: kind of thing like you know, you drive, you ride
Speaker 2: in like a driverless train when you go to the
Speaker 2: airport and you take the train from Terminal A to
Speaker 2: Terminal C or whatever. And yes, obviously driving is more complicated,
Speaker 2: and obviously we're used to driving the car and not
Speaker 2: driving the train. It's not exactly the same, but like,
Speaker 2: you get used to it. People just get used to things, right,
Speaker 2: Like people didn't used to all walk around looking at
Speaker 2: their phones all the time, and now they do. And
Speaker 2: I'm old enough that it still seems a little weird
Speaker 2: to me. But I'm the weirdo for thinking it's weird, right,
Speaker 2: And I think that's gonna happen with driverless cars in Michael.
Speaker 1: Jacob, you just called me a weirdo, because I also
Speaker 1: I do too. Once in a while, I'll take my
Speaker 1: smartphone out and I'll just stop for a second and
Speaker 1: think I have a computer in my pocket. When I
Speaker 1: was a kid, a computer or my Apple to e
Speaker 1: was a fraction of of what I'm holding in my
Speaker 1: hand right now. I also have a device where I
Speaker 1: could contact pretty much anyone. I know. It was just
Speaker 1: a couple of like, like, it'll hit me for a second,
Speaker 1: it's most attentive.
Speaker 2: And no, maybe I should just check Twitter real quick, Like, yeah,
Speaker 2: it's a it's I mean, it's another tool that has
Speaker 2: like a complicated set of effects positive AGA.
Speaker 1: But you know, you had said, like, do do you
Speaker 1: think that autonomous cars are still going to be a thing?
Speaker 1: I absolutely do think they're going to be a thing.
Speaker 1: I think there there are companies out there that are
Speaker 1: so invested in it that it's going to happen. The
Speaker 1: timeline is really interesting. I would actually argue that some
Speaker 1: other issues in AI that are not related to autonomous
Speaker 1: cars could potentially keep that five years out going for
Speaker 1: a while. Because people have this concern about A I
Speaker 1: think they port that concern over to pretty much every
Speaker 1: kind of AI, whether it's warranted or not. Because the
Speaker 1: scary risks of generative AI, this idea that it's going
Speaker 1: to displace people out of their jobs and such, which
Speaker 1: it very may well do. They then kind of say like, well,
Speaker 1: that application of AI is is really seems very harmful
Speaker 1: to me. Then I think there's a tendency to kind
Speaker 1: of apply that, even if it's not the same sort
Speaker 1: of artificial intelligence to other implementations. And maybe I'm being
Speaker 1: a little too cynical with that, but because I've seen
Speaker 1: so much reporting go on where there isn't any effort
Speaker 1: made to distinguish between different types of artificial intelligence and
Speaker 1: what their purposes are and what their limitations are. It
Speaker 1: feels like we are conditioning the public, and by we,
Speaker 1: I mean like mass media conditioning the public to think
Speaker 1: of AI as all existing in this one single bucket.
Speaker 2: I feel like you consume a lot of really bad
Speaker 2: media based on what you've been saying.
Speaker 1: I mean, I'm reading articles all the time, and it's
Speaker 1: not that they're written poorly or that the people who
Speaker 1: write them are bad writers, but they are taking shortcuts,
Speaker 1: there's no getting around it, and those shortcuts I think
Speaker 1: are ultimately harmful. But then I also understand, especially if
Speaker 1: you're assigned to write a certain number of articles per week,
Speaker 1: you're probably not going to take the time to sit
Speaker 1: there and explain the intricacies of how this is different
Speaker 1: from every other implementation of artificial intelligence. But I certainly
Speaker 1: can take the time on my show, so I do.
Speaker 1: Jacob Goldstein of What's Your Problem has a lot more
Speaker 1: to say about tech and engineering and AI, But before
Speaker 1: we jump into that, let's take another quick break. Let's
Speaker 1: talk a little bit of about some of the episodes
Speaker 1: you've done on What's your Problem? Are there Are there
Speaker 1: any that kind of stand out as like a particularly
Speaker 1: fun or informative conversation, perhaps opening your eyes to something
Speaker 1: that you hadn't considered before.
Speaker 2: Yeah, yeah, a lot. Actually. You know, when you told
Speaker 2: me you wanted to talk about the AI shows that
Speaker 2: I've done, the interviews that I've done, I actually went
Speaker 2: back through the back catalog and you know, we listened
Speaker 2: to some shows and looked, and there really are a
Speaker 2: lot of them, as you said, in sort of some
Speaker 2: different domains, Like I've done a lot on AI and health,
Speaker 2: which is really interesting to me. I mean, one of
Speaker 2: the things that I try and find are places where
Speaker 2: it's like, oh, this is there's actually real stakes here, right.
Speaker 2: It's not just like, oh, making some kind of company
Speaker 2: I don't care about ten percent more profitable or whatever,
Speaker 2: which fine, like it's fine for people to do that,
Speaker 2: it's just not that interesting to me. Whereas with health,
Speaker 2: it's like, oh, if you can make it less likely
Speaker 2: for me and the people I love to die, I'm interested.
Speaker 2: When I just did recently, I interviewed this woman Succi Saria,
Speaker 2: who she's a professor at Johns Hopkins and She also
Speaker 2: has this company called Baesian Health, and her story is
Speaker 2: really interesting. She started out as a grad student. She
Speaker 2: was interested in AI and robots, and as she told
Speaker 2: me about it, she's like, you know, I was like
Speaker 2: trying to figure out how to make a robot juggle
Speaker 2: or whatever, just because it was fun. And she had
Speaker 2: this friend who was a doctor. She was a grad
Speaker 2: student at Stanford and her friend was a doctor at
Speaker 2: Stanford Hospital who was taken care of of premature babies
Speaker 2: in the neonatal intensive care unit. And this was about
Speaker 2: twelve years ago, and at this time hospitals were just
Speaker 2: starting to use electronic health records, which is kind of
Speaker 2: amazing that it was that late. Like we're talking like,
Speaker 2: you know, I don't know, twenty twelve or something. And
Speaker 2: it is one of the really interesting things to me
Speaker 2: about healthcare is in some ways it's super high tech,
Speaker 2: you know, like these crazy CT scanners and like, you know,
Speaker 2: everybody's got like bionic knees and amazing stuff. But when
Speaker 2: you get to actual like care at the bedside, like
Speaker 2: doctor treating patients in the hospital, it has remained rather
Speaker 2: old fashioned in many ways. Right, you know, twelve years
Speaker 2: ago it was still paper charts. Today, it's still doctors relying,
Speaker 2: you know, to a significant degree on evidence, but also
Speaker 2: to a significantry on essentially intuition. And so this computer scientist,
Speaker 2: who she's sorry it, basically decides, oh, I'm going to
Speaker 2: try and figure out how to use AI to make
Speaker 2: patient care in hospitals better. Like that's basically her big project.
Speaker 2: And she starts doing it with these premature babies twelve
Speaker 2: years ago and in fact figures out that by using
Speaker 2: this data that's now being captured in the electronic health record,
Speaker 2: she can build an AI model that can essentially better
Speaker 2: predict outcomes for these premature babies than the standard of care.
Speaker 2: But it's so early that it doesn't really go anywhere, right, Like,
Speaker 2: hospitals are just starting to use electronic health records, and
Speaker 2: a lot of doctors don't want to hear from some
Speaker 2: random computers scientists. They studied medicine for a long time
Speaker 2: and they've treated a lot of patients and they know
Speaker 2: what they're doing, and so it takes a long time,
Speaker 2: but she eventually starts this company, and more recently she
Speaker 2: decided to go after sepsis, which is this really common
Speaker 2: complication at hospitals. In hospitalized patients. It's basically a terrible
Speaker 2: infection to your body's response to infection, and you can
Speaker 2: die from it. Lots of people die from it. It's complicated,
Speaker 2: it's somewhat hard to diagnose. If you can diagnose it sooner,
Speaker 2: the patient has a much better chance of surviving, right, So,
Speaker 2: very high stakes and fundamentally, you know, if you think
Speaker 2: about what AI is today, people generally mean machine learning
Speaker 2: when they say that, right, as you know, and what
Speaker 2: machine learning is really good at doing is taking a
Speaker 2: lot of data and matching it to patterns, right saying, Oh,
Speaker 2: when you have all of this set of data like this,
Speaker 2: you tend to get this kind of out, which really
Speaker 2: is what a medical diagnosis is, right, Like, that's what
Speaker 2: a doctor is doing when they look at a patient
Speaker 2: who has some set of symptoms, age, everything, and they say, oh,
Speaker 2: this person might have sepsis. Let's do a test to see.
Speaker 2: And so she built this system and it basically works.
Speaker 2: They did some trials. But a really interesting thing she said,
Speaker 2: and it goes back, Jonathan, something you were talking about
Speaker 2: earlier in the conversation, is like she realized getting the
Speaker 2: AI to work. You know, it's not one hundred percent,
Speaker 2: but to usefully flag that a patient might have sepsis
Speaker 2: essentially what it does is like maybe half the problem,
Speaker 2: maybe not even What's really hard is getting super busy
Speaker 2: doctors who are getting a million alerts all the time
Speaker 2: to believe that this alert is worth paying attention to.
Speaker 2: And like you were talking about UI, she was like,
Speaker 2: it's totally a UI problem. Like the math was the
Speaker 2: easy part, Like you know, getting it so that instead
Speaker 2: of doctors having to spend one minute when this alert
Speaker 2: comes up, they can spend three seconds. Like that was
Speaker 2: actually a huge breakthrough for like more than the AI model.
Speaker 2: So like that's an example of an episode where there's
Speaker 2: like a cool aipiece, big stakes, but also like this
Speaker 2: interesting human UI kind of messy humanity piece.
Speaker 1: Yeah. I love talking with folks who who really tackle
Speaker 1: those sorts of challenges. I remember chatting with some roboticists
Speaker 1: who were focused not on the robotics side necessarily, not
Speaker 1: on how the robot actually functioned, but rather how to
Speaker 1: design the robots so that they could interact within a
Speaker 1: human environment in a way that did not disrupt that
Speaker 1: environment at all. And it turns out that's a really
Speaker 1: tough challenge right, like creating a robot that can navigate
Speaker 1: through a human environment still do useful things. So you
Speaker 1: have to design the robot so it can go through
Speaker 1: an environment that we have designed to make sense to us,
Speaker 1: which doesn't necessarily make sense to a robot, but then
Speaker 1: to all. So do it in a way where people
Speaker 1: aren't just stopping everything they're doing to watch the robot
Speaker 1: bump into a wall fourteen times before it finds the doorway.
Speaker 1: So yeah, I think that those conversations can be really
Speaker 1: fascinating because it does open up your eyes to other
Speaker 1: issues within technology that don't necessarily relate directly to how
Speaker 1: the tech functions, but rather how do we interact with that,
Speaker 1: what happens when you have the intersection of human experience
Speaker 1: and technology. Those are really really great. We need to
Speaker 1: take one more break to thank our sponsors, but we'll
Speaker 1: be back with more conversation about communicating technology to the
Speaker 1: general public. So another episode, I just wanted to call out.
Speaker 1: We don't have to talk about it, really, but I
Speaker 1: wanted to call out because you spoke with someone I
Speaker 1: had spoken with as well on a different show. Casner,
Speaker 1: the founder of a Panoai, which is a company that
Speaker 1: uses cameras that are co located, typically on cellular towers,
Speaker 1: to monitor for forest fires in remote places, and then
Speaker 1: it's using AI to look for signs of forest fires,
Speaker 1: which then it flags anything that it suspects as a
Speaker 1: forest fire. A human reviews the footage, so it's not
Speaker 1: just relying upon AI, and if the human determines, oh
Speaker 1: my gosh, yes, this does look like the beginnings of
Speaker 1: a forest fire, they can then send an alert to
Speaker 1: the authorities that would be responsible to respond to that
Speaker 1: and potentially cut off disasters before they could happen. When
Speaker 1: I spoke with her, it was at a time where
Speaker 1: there were the infamous Canadian forest fires that were really
Speaker 1: ravaging Canada, and so it was very clear that this
Speaker 1: sort of application of artificial intelligence had a potentially like
Speaker 1: a really beneficial implementation where it could save property in
Speaker 1: people and all sorts of benefits beyond that. You think
Speaker 1: about just even just cutting back the amount of air
Speaker 1: pollution that affected all of the Northeast. You know, all
Speaker 1: those folks who had to breathe smoky air for months
Speaker 1: because of this, Like you start again, I always talk
Speaker 1: about the ripple effect. You always want to look at
Speaker 1: how this is rippling outward because you start to realize, oh,
Speaker 1: this has even greater benefit than just the the ground
Speaker 1: zero point, right, It has all these other things that
Speaker 1: will end up benefiting people, most of which you won't
Speaker 1: even realize because you have prevented the bad thing so
Speaker 1: you don't experience the bad thing. And so I just
Speaker 1: wanted to call that out for listeners who might be
Speaker 1: looking to see where to start off, because you've got
Speaker 1: quite a few episodes.
Speaker 2: She's really interesting and you know, one of the things
Speaker 2: that was interesting to me about Sonya, the person who
Speaker 2: started this company, was she has this big idea that
Speaker 2: actually goes beyond wildfires. Is that's what they're doing now,
Speaker 2: that's their business now. But her big dream is is
Speaker 2: about data and adapting to climate change. Basically, right, there
Speaker 2: are more wildfires because of climate change. But she's like, look,
Speaker 2: we're going to be spending trillions of dollars over the
Speaker 2: next decades to mitigate the effects of climate change to
Speaker 2: you know, deal with seawater rise and flooding, and like,
Speaker 2: are you know flood maps are one hundred years old,
Speaker 2: and so if we can in different domains bring data
Speaker 2: to bear on like where should we prioritize the money.
Speaker 2: Where are there going to be more floods if we
Speaker 2: can bring technology to bear? And in her case, what
Speaker 2: that really means is data. Right, Like the sort of
Speaker 2: substrate of AI. The thing AI needs to be clever
Speaker 2: is a lot of data. If we can bring data
Speaker 2: to that, it'll just work better. For every million dollars
Speaker 2: we spend, for every billion dollars we spend, if we
Speaker 2: can be more smart about it, we will get better results.
Speaker 1: Right. It's it's like the difference between being proactive and reactive, right,
Speaker 1: being able to being able to plan for something and
Speaker 1: minimize its impact, as opposed to, Oh, now we have
Speaker 1: to clean up because this catastrophic event has happened, and
Speaker 1: how do we deal with that? And I think when
Speaker 1: we look back at some of those catastrophic events that
Speaker 1: have happened in our lifetimes, you can really see the
Speaker 1: benefit of mitigation versus reaction and cleaning up, you know,
Speaker 1: the ability to save lives and prevent damage. It's tremendous.
Speaker 1: So certainly there are plenty of artificial intelligence applications that
Speaker 1: would be incredibly helpful when put to the proper use.
Speaker 1: So again, I think if there's any lesson to take
Speaker 1: home with this conversation is use that critical thinking, try
Speaker 1: not to be reductive. I know that I can get
Speaker 1: really cynical about artificial intelligence, but again it's mostly because
Speaker 1: of the marketing language around it rather than the technology itself.
Speaker 2: Yeah.
Speaker 1: I think it's also because, like I see a lot
Speaker 1: of similarities in the AI evangelists that I saw with
Speaker 1: NFT evangelists. And that's low We all know how that.
Speaker 2: Yeah, you know, I think AI has more legs than NFTs.
Speaker 2: I feel like I'm not going out on a limb
Speaker 2: to say that.
Speaker 1: I certainly think it has more potential beneficial uses than
Speaker 1: n FTS. I think NFTs probably have some benefits too,
Speaker 1: but the problem is that no one was focusing on
Speaker 1: those when they were going crazy about them.
Speaker 2: I mean, one of the interesting things to me, when
Speaker 2: you know, when you think about what should we worry
Speaker 2: about with AI, there's sort of these two there's sort
Speaker 2: of like a Barbelle where like the thing you hear
Speaker 2: about most is it's going to take all our jobs,
Speaker 2: where a robot's going to kill us, all right, that's
Speaker 2: the like amazing there's an interesting other end of the
Speaker 2: spectrum that you know, some of the people have talked
Speaker 2: to on the show have talked about which is the
Speaker 2: risk of people over relying on AI, right, People worry that,
Speaker 2: you know, critical decisions are going to be made based
Speaker 2: on AI outputs that are not that good, that are
Speaker 2: not that robust, that are not that reliable. And you know,
Speaker 2: one of the people I talk to like runs a
Speaker 2: company basically like to stress test AI to catch eyes mistakes,
Speaker 2: and he talked about just like really dumb mistakes that
Speaker 2: he sees all the time, you know. Where he gave
Speaker 2: the example of like on a life insurance application if
Speaker 2: someone puts their year of birth instead of their age, right,
Speaker 2: so you put whatever, nineteen eighty four instead of forty,
Speaker 2: and I will actually think the person is one nine
Speaker 2: hundred and eighty four years old and will want to
Speaker 2: charge them a lot for their life insurance because boy,
Speaker 2: if you're that old, you're gonna have a lot of
Speaker 2: health risks. And I said to him, like, is it
Speaker 2: really that dumb, Like is it really or you being
Speaker 2: you know, is this hyperbole? He said, no, it's really
Speaker 2: that dumb. And so that is an interesting side of
Speaker 2: it to me, right, Like, oh, there's also a risk,
Speaker 2: there's a risk from AI being too smart. There's also
Speaker 2: a risk from a being not smart enough if people
Speaker 2: are over reliant.
Speaker 1: On it, which you would hope that people wouldn't fall
Speaker 1: into that trap. But at the same time, you just
Speaker 1: look back over the history of technology and how would
Speaker 1: we've had technology that helps remove certain tasks that we
Speaker 1: just let them go. So, for example, I can probably
Speaker 1: rattle off maybe half a dozen phone numbers of people
Speaker 1: that I know would love, but all the rest are
Speaker 1: just buried in my phone contacts because I don't need
Speaker 1: to have them stored in my own brain. I have
Speaker 1: offloaded that to technology.
Speaker 2: Half does it is a lot? Are those from twenty
Speaker 2: years ago? I don't. I haven't member a city, but
Speaker 2: he's phone number in a long time.
Speaker 1: My parents haven't changed their phone numbers since I was
Speaker 1: a child, so that one I remember, honestly, if I'm
Speaker 1: being really honest, it's probably more like three, but I.
Speaker 2: Might be down to two at this point when my
Speaker 2: mom got rid of her landline a few years ago.
Speaker 2: I definitely don't know my mom's cell number.
Speaker 1: Yeah, I know my parents' landline number because they still
Speaker 1: have it. I couldn't tell you they're cell numbers either,
Speaker 1: but that's kind of a simple example, and you know,
Speaker 1: obviously it's going to be a lot more complicated when
Speaker 1: you're talking about offloading, you know, potentially decisions to AI.
Speaker 1: But I think the argument I can make is that
Speaker 1: there's precedent, So I think that concern is well warranted, right.
Speaker 1: I think it also gets back to that concern about
Speaker 1: autonomous cars. Everyone worries that the autonomous car they're getting
Speaker 1: into is the one that's being driven by a crazy robot.
Speaker 1: So it's odd also to think of a world where
Speaker 1: people might be nervous to get into an autonomous car,
Speaker 1: but they might be willing to have an AI complete
Speaker 1: their taxes for them. For example. It's a weird world
Speaker 1: we live in.
Speaker 2: Hey, I do in taxes? Is interesting? Are you do
Speaker 2: you have an AI accountant? I mean, I like my accountant, but.
Speaker 1: My account's pretty good and I'm ninety seven percent sure
Speaker 1: she's human, So I think I'm in the clear on
Speaker 1: that one. But I could easily see that being a thing,
Speaker 1: especially for something like the United States, where the tax
Speaker 1: code gets complicated enough where people like you and I
Speaker 1: we feel the need to go out and reach out
Speaker 1: to a professional because handling it yourself as daunting.
Speaker 2: You could imagine like a happy story is like the
Speaker 2: AI does a lot of the work. An accountant can
Speaker 2: have more clients and charge each of them less, and
Speaker 2: like go over the work of the AI. Right, Like
Speaker 2: there's yes, and this it's sort of mundane, right. The
Speaker 2: reason people don't talk about outcomes like that is because
Speaker 2: it's boring. But there are a lot of boring incremental gains.
Speaker 2: If I could pay my accountant half as much and
Speaker 2: my accountant could have twice as many clients and do work,
Speaker 2: that's maybe a little better or at least as good.
Speaker 2: Everybody wins. I mean, I suppose at the margin there's
Speaker 2: need for fewer accountants in that world, but like that's
Speaker 2: okay with me, right, Like those people who would have
Speaker 2: been accountants can go and like, you know, work on
Speaker 2: AI healthcare or something.
Speaker 1: Yeah. I like the people who argue that instead of
Speaker 1: calling it artificial intelligence, maybe call it augmented intelligence, where
Speaker 1: the goal is to augment our abilities to get things done.
Speaker 1: And I think it would be a lot easier to
Speaker 1: do that if we heard fewer stories like a CEO
Speaker 1: suggesting that eight thousand unfilled jobs will ultimately be filled
Speaker 1: by AI and not humans. If we heard fewer stories
Speaker 1: like that and more stories about no, we we implemented
Speaker 1: this so that people could respond to customer concerns at
Speaker 1: a rate that's five times faster than before, which means
Speaker 1: they can resolve your issue and you're you're spending less
Speaker 1: time frustrated and sitting on hold. Like I think that's
Speaker 1: the direction that everyone wants it to go, and they're
Speaker 1: just worried that's going to go in the direction of, Hey,
Speaker 1: those coworkers you used to like they're all replaced by
Speaker 1: algorithms now, Like that's that's where we need to really go.
Speaker 2: Yes, I mean, technological unemployment is complicated, right, Like people
Speaker 2: have been certainly afraid of it for hundreds of years.
Speaker 2: Now today let's talk about some let's talk about the
Speaker 2: Dutch weavers high yeah right, I mean, you know, unemployment
Speaker 2: is below four percent today, wages are going up. People
Speaker 2: get angry when you point that out. But it's true,
Speaker 2: and it's possible that AI will be bad for workers,
Speaker 2: but we don't know yet, Like that's one like I
Speaker 2: just don't know, and I don't think anybody knows the
Speaker 2: answer to that one.
Speaker 1: Yeah, yeah, And then this lays the scariness. Well, Jacob,
Speaker 1: thank you so much for joining the show. This has
Speaker 1: been a really fun conversation. I've really enjoyed it. I'm
Speaker 1: sure my listeners have too. And just to remind everybody,
Speaker 1: your podcast is What's Your Problem. You have these kinds
Speaker 1: of conversations with decision makers and the people who are
Speaker 1: actually creating the systems we've been talking about, and who
Speaker 1: are actively tackling these questions and determining how to address them.
Speaker 1: So I highly recommend to my listeners you check out
Speaker 1: What's Your Problem. You've got so many different episodes, I'm
Speaker 1: sure like there's going to be one on there that's
Speaker 1: going to speak to every single person who listens to
Speaker 1: my show.
Speaker 2: Thank you so much. That's such a kind generous thing
Speaker 2: to say, and thanks for having me. It was great.
Speaker 1: I hope you all enjoyed this conversation I had with
Speaker 1: Jacob Goldstein. It was a pleasure having him on the show.
Speaker 1: I know this was a long one. We literally could
Speaker 1: have gone another hour easy, so I had to use
Speaker 1: some restraint there. I hope all of you out there
Speaker 1: are well. I'm looking forward to having a lot more
Speaker 1: interviews in the future. In fact, I've got a couple
Speaker 1: that I'm working on right now to kind of line up,
Speaker 1: So that's really exciting for me. I love having another
Speaker 1: point of view come into the conversation. I hope you
Speaker 1: do too, and I will talk to you again really soon.
Speaker 1: Tech Stuff is an iHeartRadio production. For more podcasts from iHeartRadio,
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