Speaker 1: Now here's a highlight from coast to coast AM on iHeartRadio, Jeremy.
Speaker 2: We already have language interpreter devices, but with AI, don't
Speaker 2: you think it'll be even better?
Speaker 3: Absolutely? I mean one of the things you can do
Speaker 3: with these systems is translated very easily between languages, and
Speaker 3: there already people talking about, you know, putting it into
Speaker 3: simple wearable devices. It might be possible in the future
Speaker 3: that you can have a conversation with somebody in two
Speaker 3: languages or neither of you actually speak, you know, not
Speaker 3: as you rather understand, and you will get simultaneous translation
Speaker 3: in those languages, you know, directly in your ear, and
Speaker 3: you won't have to worry about potentially learning those languages,
Speaker 3: although you know, there's obviously drawbacks to that if people
Speaker 3: are not actually learning language.
Speaker 2: So just incredible technology, it really is. What do you
Speaker 2: think of driverless vehicles? That's artificial intelligence is, isn't it?
Speaker 1: Yeah?
Speaker 3: Yes, driver's vehicles use various kinds of AI, and obviously
Speaker 3: we have driver's vehicles in a few cities in the US.
Speaker 3: In San Francisco there are these Waymo taxis. In Phoenix,
Speaker 3: you can get taxies as well. I think from Waymo
Speaker 3: and Cruise, which is this self driving car company. It's
Speaker 3: owned by General Motors, but it's been fairly limited areas,
Speaker 3: and I think part of the problem is that driving
Speaker 3: the real world is very complex and lots of things
Speaker 3: that can happen out on the road. They're very hard
Speaker 3: to anticipate, and unfortunately, the way we've had to create
Speaker 3: driver's cards so far, they've needed very specific information about
Speaker 3: where they are, very detailed mapping, and we've tried very
Speaker 3: hard to train them for all different kinds of scenarios,
Speaker 3: but ultimately it's very hard to have enough data from
Speaker 3: enough place to train them to work well in all
Speaker 3: kinds of environments, particularly in all kinds of weather, so
Speaker 3: they don't do as well in snow, for instance. It's
Speaker 3: a really tricky one for driver's cars, and as a result,
Speaker 3: they haven't really been deployed that widely yet. I think
Speaker 3: that's going to change though over the next ten year
Speaker 3: is I think we're going to see more and more
Speaker 3: cities where driver's cars will be available. And I do
Speaker 3: think that some of these newer AI techniques, the sort
Speaker 3: of techniques that I talked about in the book, will
Speaker 3: enable us to create self driving cars that are more capable,
Speaker 3: because we'll start they will start to actually understand and
Speaker 3: I use the understands they're somewhat loosely as a term,
Speaker 3: but I think they will start to understand more about
Speaker 3: how the world actually works, and therefore they'll be able
Speaker 3: to cope. They'll have essentially something closer to common sense.
Speaker 3: They'll know that if a tree falls across a road,
Speaker 3: that you have to stop the tree, even if that
Speaker 3: had never happened in a narrative seen before.
Speaker 2: How about people that have been killed by driverless cars.
Speaker 3: Yeah, I mean, I think that's terrible and I think
Speaker 3: that's a problem with the technology we have. So far,
Speaker 3: there haven't been too many incidants of people being killed
Speaker 3: by one of these ROWA taxes are full self driving cars.
Speaker 3: There was the one incident that in Phoenix, there was
Speaker 3: another intent where somebody had already been hit by a
Speaker 3: car than was dragged by driver's car.
Speaker 2: Phoenix was hit by one, right.
Speaker 3: Yeah, the woman in Phoenix was hit by one, and
Speaker 3: that that is a case where it's that again it
Speaker 3: was a censor. It did not recognize her as the person.
Speaker 3: I think again that's why we need careful testing of
Speaker 3: these systems. We have to remember that, you know, fifty
Speaker 3: thousand Americans are killed every year by other humans on
Speaker 3: the road, and I think we can do better than that.
Speaker 3: You know, that would be a good thing. And I
Speaker 3: think it's one of the things we should get used
Speaker 3: to when we think about these systems, is you know,
Speaker 3: what are we comparing against and what standard do we require.
Speaker 3: We have lots of humans on the roads that are
Speaker 3: not particularly safe drivers every day, and if we can
Speaker 3: do better than that, should that be the standard or
Speaker 3: should the standard be one hundred percent you know, accuracy
Speaker 3: and no death.
Speaker 4: Ever.
Speaker 3: I think there is this interesting where we were willing
Speaker 3: to countenance certain kinds of human error, that including fatal
Speaker 3: human errors, and think that that's okay or just the
Speaker 3: price of life. And yet you're not willing to put
Speaker 3: systems into place, automated systems that would actually be less
Speaker 3: dangerous than having humans do it. Because I think that
Speaker 3: part of it has to do we don't really understand
Speaker 3: the way these errors occur when other humans make mistakes.
Speaker 3: We kind of have a good sense of what the
Speaker 3: things are that cause humans to make mistakes are. What
Speaker 3: are the human errors that can happen to cause to cause,
Speaker 3: you know, fatal accidents. We don't have a good sense
Speaker 3: of exactly what all the errors are that can crop
Speaker 3: up in these automated AI systems, and I think that
Speaker 3: that frightens us a bit more, and we're a little
Speaker 3: bit more hesitant to put these systems into places of
Speaker 3: high consequence. And I think, you know, that's that's that's good.
Speaker 3: I think we should be careful, but I think we
Speaker 3: should also have a standard. What are we comparing this against?
Speaker 3: What do we want these systems to do.
Speaker 2: I'm opposed to driverless trucks, jure, maybe because I don't
Speaker 2: want to see truck drivers displaced from jobs.
Speaker 3: Yeah, well, look, I mean I think that it is
Speaker 3: potentially an issue, but there are of other jobs people
Speaker 3: can do. I think we don't have to worry so
Speaker 3: much about math employment. They're all going to with their jobs.
Speaker 3: But if you think in certain industries we may see
Speaker 3: people lose their jobs. I think in case a trucking
Speaker 3: is really a shortage of trucking driver. So the question is,
Speaker 3: you know, does this mean that all truckers you know,
Speaker 3: lose their jobs or does it just mean that some
Speaker 3: trucks are automated and there's still a role for human drivers.
Speaker 3: I think it depends. That depends what the cost is
Speaker 3: of these systems and how they're deployed exactly. It's one
Speaker 3: of the things we're going to have to wrestle with
Speaker 3: going forward.
Speaker 2: Let's go to the phones. Let's go to Thomas and
Speaker 2: La Jolla, California to start. Hey, Tom, welcome, Hi George,
Speaker 2: Thank you very much. I'm getting a lot of static
Speaker 2: on my line, so I will try to make the
Speaker 2: short I want to extend the conversation on self driving trucks.
Speaker 2: In my opinion, it will not displice jobs. Okay, if
Speaker 2: we have a truck that is automated, it's going to
Speaker 2: be like the Apollo Moon capsule, you know, with AI
Speaker 2: running the truck. But there will always be a teamster
Speaker 2: there in the truck always, and they will be like astronauts.
Speaker 2: You know, They're not going to be using a steering
Speaker 2: wheel or a stick shift, or a brake pedal or
Speaker 2: gas pedal, or a rear view mirror or any of that.
Speaker 2: You know, it's all going to be handled by the AI.
Speaker 2: But you know, occasionally the AI will say, sir or madam,
Speaker 2: we need to make a course correction, and so our
Speaker 2: teamster puts down his Fortune magazine and participates in the
Speaker 2: course correction. So I was wondering, it just seems to
Speaker 2: me we should have this technology available now. We had
Speaker 2: it available like in the Apollo Moon Capsule fifty five
Speaker 2: years ago, and the Apollo Moon Capsule only used a
Speaker 2: computer that was two megabytes two megabytes, and of course
Speaker 2: our smartphones have hundreds of gigabytes. So I'm wondering if
Speaker 2: you would if we could extend the conversation. I don't
Speaker 2: think we'll lose jobs. I think that our truck drivers
Speaker 2: will be trained like astronauts.
Speaker 4: What do you think, sir, I.
Speaker 3: Mean, I think yeah, I think that's quite That's a
Speaker 3: possible scenario. People have also talked about whether you would
Speaker 3: have still the truckers, but they would operate in some
Speaker 3: sort of remote center and you would have AI Infensa
Speaker 3: driving the trucks most of the time, and then if
Speaker 3: there was a problem, it would alert the driver, but
Speaker 3: the driver wouldn't necessarily be in the truck themselves. They
Speaker 3: might be in a remote center where they could take
Speaker 3: over a remote operation of the truck using off the camera.
Speaker 3: They'd be able to see what's going on and they
Speaker 3: would then drive the trucks that are remotely if they're
Speaker 3: an error, at least the side of the road, and
Speaker 3: then you could have a recovery vehicle coming and and
Speaker 3: move the truck or yeah, I mean as possible as
Speaker 3: you say, we will want the teacher or the truck
Speaker 3: driver to actually be in the cab, but they'll just
Speaker 3: have to sit there and wait until there's a problem
Speaker 3: or something that needs human intervention or human decision making.
Speaker 3: That's that is one possible scenario. I think that that
Speaker 3: could happen. It's some of the company that are pushing
Speaker 3: self driving trucks though, really do see it as a
Speaker 3: system where there is no driver there at least for
Speaker 3: large stretches of the journey.
Speaker 2: They're doing they're doing it money, aren't they.
Speaker 4: I'm sorry said that again, George.
Speaker 2: They're doing it to save money. There won't be a truck.
Speaker 3: Well, yeah, they want that is right. They want to
Speaker 3: save money. They but they also want to they want
Speaker 3: to save money and they also want to In a
Speaker 3: case of trucking, there aren't enough truck drivers to actually
Speaker 3: haul all the things that be hauling, and there's a
Speaker 3: real labor short Not enough people are going into trucking.
Speaker 3: So this is a case where you know there aren't
Speaker 3: enough people doing these jobs. And I think AI could
Speaker 3: actually help because you could have some self driving trucks.
Speaker 3: You could have them in a convoy where you have
Speaker 3: one human driver to front, all the others essentially follow
Speaker 3: and are driven automatically. I think those scenarios, you know,
Speaker 3: could help us with the shortage of truck drivers that
Speaker 3: we have.
Speaker 2: I may have been the last reporter to talk to
Speaker 2: Jimmy Hoffa back in nineteen seventy five. Jeremy, he would
Speaker 2: be going ballistic right now. Yeah, driver trucks.
Speaker 3: I think that's true, and a lot of other industries,
Speaker 3: you know, people have gone ballistic. Unions have been very
Speaker 3: upset about displacement of workers, but in the end, I
Speaker 3: think the people have found those jobs.
Speaker 2: There.
Speaker 3: Again, I don't think we're gonna have massive employment. There
Speaker 3: will be jobs for people to do, and I think ultimately,
Speaker 3: you know, this technology is coming along, and I think
Speaker 3: you have to figure out how are you going to
Speaker 3: work along outside it? And are the ways to shape it?
Speaker 3: And I think you know, one of possibility is, as
Speaker 3: the caller suggested, that you have truck drivers in the cab,
Speaker 3: but they are doing less of the actual driving. They're
Speaker 3: kind of supervising the automated system. In the book, I
Speaker 3: talk a lot about some problems. I actually use the
Speaker 3: examples of Massa. Massa is on a ton of research
Speaker 3: on this, because astern outs are very much often in
Speaker 3: this role of supervising automated systems, and I think that's
Speaker 3: increasingly going to be all of us. We're going to
Speaker 3: be in our jobs doing a lot more supervising of
Speaker 3: AI than we are maybe doing the task ourselves. The
Speaker 3: problems is that humans are not very good at being
Speaker 3: that vigilant over long periods of time. So a pretty
Speaker 3: bu if the system is pretty good and it doesn't
Speaker 3: make mistakes very often, it's very hard to maintain that vigilance,
Speaker 3: and often we fail to catch the errors when they happen.
Speaker 3: And there's this human cognitive bias that I talked about
Speaker 3: in the book called automation bias, where we tend to
Speaker 3: assume that the automated system is right, even in the
Speaker 3: face of contradictory data that should alert us to the
Speaker 3: fact that the system is making a mistake. Very often
Speaker 3: people think, oh, no, the system, you know, it's a computer.
Speaker 3: It must know best, even when what it's suggesting kind
Speaker 3: of defies common sense. And I think that's a danger
Speaker 3: we're going to have to guard against. And the other
Speaker 3: thing that happens with people when they're placing that role
Speaker 3: of kind of just being this the overseer of what
Speaker 3: an automated system does is when they when a system
Speaker 3: does go wrong, if they do recognize the air that
Speaker 3: there is in there, they're often quite surprised by the error.
Speaker 3: They have a lot of trouble figuring out what's going
Speaker 3: wrong exactly and taking corrective action. That's increasingly been a
Speaker 3: problem in aviation, where we have, again a lot of
Speaker 3: automated systems, a lot of autopilots. Most of the time
Speaker 3: they work perfectly well. When they do go wrong, pilots
Speaker 3: often struggle because they're surprised that there's an error. They
Speaker 3: often kind of panic and will make mistakes in the
Speaker 3: process of trying to correct and will work back to
Speaker 3: a manual process. And the key, as NASA's found and
Speaker 3: as we found in aviation, is very often just to
Speaker 3: drill people in simulation with lots of potential error scenarios
Speaker 3: so they know how to respond and they're kind of
Speaker 3: practice and how to respond. And I think companies are
Speaker 3: going to have to start doing this as well as
Speaker 3: we move with more of these AI copilots into how
Speaker 3: we work, and maybe that we'll all have to train.
Speaker 3: If you're the salesperson and you often rely on the
Speaker 3: AI copidits to write your sales pitch for you, companies
Speaker 3: may have to say once a month do a drill
Speaker 3: where you have to write the sales pitch on your
Speaker 3: own to just so you keep those human skills and
Speaker 3: you know how to recover if the AI system isn't available.
Speaker 2: Go to my condenver Michael, go ahead.
Speaker 4: George, thank you for taking my callage. So great to
Speaker 4: be with you again. And Jeremy, thank you for this
Speaker 4: incredible presentation tonight. And what I was going to ask
Speaker 4: you about. Interesting kind of headline here, Dee Appliances is
Speaker 4: using Google Cloud AI to make recipes from what's already
Speaker 4: inside of fridge. And you know, this is something that's
Speaker 4: a relatively new thing, something I don't think AI could
Speaker 4: do at conception. And that leads me to my question,
Speaker 4: which is what are some misconceptions when it comes to
Speaker 4: say what businesses, for example, think AI can do as
Speaker 4: far as like AIS capabilities and what AI AI actually
Speaker 4: can do.
Speaker 3: Yeah, so this is a great a great question. Uh,
Speaker 3: And it's a great example. I mean, I think it's
Speaker 3: one of the things that the AI systems that have
Speaker 3: kind of come online in the last two years can do.
Speaker 3: Is like it could take you could just take a
Speaker 3: picture of your fridge actually now and you you will
Speaker 3: recognize what those items are in your fridge, and then
Speaker 3: you know, it will suggest recipes based on those items,
Speaker 3: which is pretty amazing. And then I think the next
Speaker 3: step that we're about to see, uh, which I talked
Speaker 3: about the beginning of the program, are these AI agents
Speaker 3: And in that case, it would would actually recognize what
Speaker 3: was in your fridge. You could suggest recipes for tonight,
Speaker 3: but it would also know, oh, you're running low on
Speaker 3: milk or you're out of uh, you know this particular ingredient,
Speaker 3: and it would go online and order those things for you.
Speaker 3: It might be your kind of weekly shop for you
Speaker 3: online without you having to tell it very much. It
Speaker 3: might learn your preferences and just be able to do
Speaker 3: this automatically, which and I think that's coming in terms
Speaker 3: of what AI can do right now. And there is
Speaker 3: sometimes a disconnect particularly, there's a disconnect in large companies
Speaker 3: I think between what the CEOs or the board think
Speaker 3: AI can do, and then if you talk to the
Speaker 3: actual engineers who have to create this technology, they're often
Speaker 3: sort of frustrated by what the capabilities are. This often
Speaker 3: comes down to reliability. So a lot of AI systems
Speaker 3: right now can do lots of things, some of the
Speaker 3: time very well, and then some of the times, on
Speaker 3: what seems like a very similar task, or it's the
Speaker 3: same task but you've given the instruction slightly differently, the
Speaker 3: system will fail completely. And I think that's frustrating for people.
Speaker 3: This is something of all the tech companies that are
Speaker 3: working on AI systems are working very hard on is
Speaker 3: to increase the reliability of these systems. There's some debate
Speaker 3: in the field about how easy it is to do this,
Speaker 3: how whether this will be possible. Some people think the
Speaker 3: underlying large language models that we use right now create
Speaker 3: AI software that they have a kind of fundamental problem
Speaker 3: where they will never get to the kind of accuracy
Speaker 3: that we need them to to really be reliable in
Speaker 3: these systems, and that we're going to need something else,
Speaker 3: some other kind of change in the architecture to improve
Speaker 3: these systems. One would be, as I talked about, trying
Speaker 3: to do more training and simulation of AI systems, So
Speaker 3: you put them in that kind of state environment, but
Speaker 3: let them just experiment and learn through trial and error.
Speaker 3: That does tend to result in more reliable AI systems,
Speaker 3: and I think it's one approach that we may see
Speaker 3: companies pursue going forward. Some specific things I think they're
Speaker 3: very good at right now. I think in any sort
Speaker 3: of scenario which involves composing something that is going to
Speaker 3: be then checked by a human, So if it's a
Speaker 3: particularly any kind of text they're writing, writing letters, writing documents,
Speaker 3: as only kind of a human in a loop in
Speaker 3: that process, and it's one of the reasons we may
Speaker 3: not see much job loss from AIS. I think you
Speaker 3: still are going to need this human in the loop
Speaker 3: anytime in the near term to check what the AI
Speaker 3: is doing.
Speaker 4: And in those.
Speaker 3: Scenarios it's probably states because if a human check on
Speaker 3: what's being put out and ultimately it's just a kind
Speaker 3: of a documentation, it's not directly taking action. So those
Speaker 3: are safer scenarios. I think situations where you would see AI,
Speaker 3: you know, actually taking action, these kind of AI agents
Speaker 3: that I think are coming along those I'm a little
Speaker 3: bit more worried about given the current reliability of systems,
Speaker 3: because they are error prone and it will be harder
Speaker 3: to have a human check on what they're doing, I think.
Speaker 3: And that's the case where I think businesses, you know,
Speaker 3: have to be careful not to run ahead of themselves
Speaker 3: and think, oh, we can have an AI system that
Speaker 3: will automatically do our purchasing for us, will automatically do
Speaker 3: a particular process within in the company. Maybe it's moving
Speaker 3: data even from like one one type of software to
Speaker 3: another often that you know people want to use AI
Speaker 3: could do that to kind of transfer data between different programs.
Speaker 3: That again, you're going to have to be very careful
Speaker 3: that it's not making mistakes in the process.
Speaker 2: Surely, if you asked AI what is God? Would it
Speaker 2: give you an answer?
Speaker 3: Yeah, absolutely, it would give you an answer. It might,
Speaker 3: you know if that might, it doesn't. But you have
Speaker 3: to remember that right now, these systems do they just
Speaker 3: make up an answer, right, So it would give you
Speaker 3: an answer. It would give you an answer based on
Speaker 3: you know what the data has been trained on, which
Speaker 3: is mostly human written data about you know what if
Speaker 3: humans in the past, then what is God? It would
Speaker 3: and it would probably give you whatever the kind of
Speaker 3: majority viewpoint expressed, and that data is within some sort
Speaker 3: of range, and it might give you several different opinions.
Speaker 3: It might say, oh, some people think God is this,
Speaker 3: and some people think God is that, and some people
Speaker 3: think God doesn't exist. It would definitely give you an answer,
Speaker 3: but that doesn't mean it's the correct answer. You know, AI,
Speaker 3: I think pretty AI systems right now they're not omnissioned.
Speaker 3: They don't know. And then the ones we're really impressed
Speaker 3: with actually don't know anything that we as as a
Speaker 3: human species don't already know and haven't already thought of
Speaker 3: and written down, because that's all all the information has
Speaker 3: been trained on, is all this human written information, So
Speaker 3: it doesn't have a way of answering beyond is training data.
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