Speaker 1: Brought to you by Toyota. Let's go places. Welcome to
Speaker 1: Forward Thinking, Tather and welcome to Forward Thinking, the podcast
Speaker 1: that looks at the future and says, take this bot
Speaker 1: and shove it. I'm Jonathan Strickland, I'm Lauren Volga, and
Speaker 1: I'm Joe McCormick. As you might have guessed from the
Speaker 1: thing that just came out of Jonathan's mouth, today, we're
Speaker 1: going to be talking about robots, robots and jobs. Yeah.
Speaker 1: As it turns out, we've had quite a few listeners
Speaker 1: and viewers of Forward Thinking ask us to cover this topic.
Speaker 1: It's it's one that's had a lot of coverage in
Speaker 1: the media, and so we're looking at robots and jobs
Speaker 1: right well, specifically the question will robots steal our jobs? Right? Right? Yeah?
Speaker 1: I mean, which is kind of a terrific headline, which
Speaker 1: I think is part of why it's so popular in
Speaker 1: the media. But but I mean it captures our imagination
Speaker 1: in a very specific way, because I mean, we are
Speaker 1: all seeing all of this autumn nation that we've never
Speaker 1: seen before kind of come up and and influence our
Speaker 1: workforce in very interesting ways. Yeah, not just in uh,
Speaker 1: in the ways that have been traditionally depicted like in manufacturing,
Speaker 1: you know, heavy manufacturing, but in ways that affect you know,
Speaker 1: white colored jobs as well. And we're going to cover
Speaker 1: all this kind of stuff. But uh, let's let's look
Speaker 1: down memory lane, because Joe, you pulled up something fascinating,
Speaker 1: something I had heard but never looked into, and you
Speaker 1: really found some information that that taught me some stuff. Well,
Speaker 1: I want to get to the core of what we're
Speaker 1: talking about, which is not just robots in the sense
Speaker 1: of the way we picture them, which is a thing
Speaker 1: with legs and arms. You know, it's vaguely human basically, right,
Speaker 1: I go with Robbie the robot, But that's that's classic. Generally,
Speaker 1: we're talking about all automation, any any way in which
Speaker 1: a machine can do a job that previously could have
Speaker 1: only been done by a human or an animal. Maybe.
Speaker 1: Uh so, how about the Luodites. You all know the Luodite.
Speaker 1: It's not just a term we call each other when we, like,
Speaker 1: you know, accidentally sent a text message to the wrong
Speaker 1: person or something like that. It's actually a historical group.
Speaker 1: And of course you know who they were, right, Well,
Speaker 1: well I do now because you did the research, Joe. Well,
Speaker 1: they hated machines, right, that's the that's the whole thing.
Speaker 1: That's the way I the way I've always heard it
Speaker 1: used is yeah, it's people who just dog on it
Speaker 1: can't either grasp technology and therefore they hate it because
Speaker 1: of that, or they just outright think that new stuff
Speaker 1: is bad for some reason. I assumed it was people
Speaker 1: who had some kind of aesthetic or religious opposition to
Speaker 1: technology in general. And turns out I was being a
Speaker 1: total nano rod because that is not what the Luddites were.
Speaker 1: So the original Luddites were a movement of textile workers
Speaker 1: in England in the early eighteen hundreds, and they were
Speaker 1: not against technology in general. They were famous for like
Speaker 1: burning factories and smashing factory machines, but not because they
Speaker 1: believe machines were evil. In fact, they were more or
Speaker 1: less a labor movement. Uh. They were probably workers who
Speaker 1: used machines themselves in their jobs, and they arose from
Speaker 1: conditions of economic trouble and unemployment in Great Britain that
Speaker 1: was part of a depression during and following the Napoleonic
Speaker 1: Wars in the early eighteen hundred. So they were seeking
Speaker 1: better employment, more work, better wages, but part of the
Speaker 1: problem they perceived with the labor market. Then it was
Speaker 1: the so called labor saving devices and machines, which often
Speaker 1: meant that the same manufacturing job that used to require
Speaker 1: a skilled crafts person could be performed by a machine,
Speaker 1: or for a lower wage, by an unskilled worker paired
Speaker 1: with the machine. So in a kind of certain qualified
Speaker 1: since machines took their jerbs and they weren't happy about it.
Speaker 1: And you can understand why. We've heard the same story,
Speaker 1: the the apocryphal story that sabotage comes from the sabo
Speaker 1: thrown into the cogs of the machine. From this but
Speaker 1: similar story easy like uh and we Joe, you and
Speaker 1: I had had a brief conversation before we came in
Speaker 1: to record the podcast. We were just kind of talking
Speaker 1: about the subject, and we mentioned that this this complaint
Speaker 1: was again about the loss and jobs, not necessarily technology.
Speaker 1: If in fact the manufacturers had found a super cheap
Speaker 1: source of labor, the same problem would have existed. It
Speaker 1: wasn't just that it was a machine. Yeah, if you
Speaker 1: could have just found people who would do the same
Speaker 1: jobs cheaper and take those jobs away from these workers.
Speaker 1: I found a really easy way to train horses to
Speaker 1: do it. Are horses are terrible at weaving, but uh,
Speaker 1: you know they they just like all manual dexterity. But
Speaker 1: in economy in England, it would have had to be rabbits, right,
Speaker 1: they have a factory, they would have factories of rabbits
Speaker 1: weaving cloth. I think it would be hairs actually sorry,
Speaker 1: but uh, in economics we call we call this this
Speaker 1: process substitution, where we use a machine to substitute for uh,
Speaker 1: human labor. Uh. And there's another idea called complimentarianism complimenting.
Speaker 1: So we're gonna be talking a lot about substituting versus
Speaker 1: complimenting in this podcast. But as long as humans have
Speaker 1: been innovating, we've had this issue to some extent. I mean,
Speaker 1: if you look at agriculture, and we'll talk a lot
Speaker 1: about agriculture, uh, you know, just the advent of the
Speaker 1: plow made a lot of of changes because now you
Speaker 1: didn't need as many human workers howing the fields as
Speaker 1: you did before. And so this is not a new
Speaker 1: idea by any extent. It goes back, you know, centuries,
Speaker 1: but we see it uh accelerated in the era of
Speaker 1: technological advances and development. Yeah, and so I think a
Speaker 1: good place to start with the most recent modern incarnation
Speaker 1: of this worry that that machines, automation, robots will take
Speaker 1: our jobs is this pretty optimistic article that Kevin Kelly
Speaker 1: wrote for Why, aired in December, is called better than
Speaker 1: Human Why Robots will and Must take Our Jobs? Um,
Speaker 1: and Kelly says this, two hundred years ago, seventy percent
Speaker 1: of American workers were farmers. By about a hundred years
Speaker 1: after that, it was some yeah, I read in another
Speaker 1: article entirely, but I wanted to toss that in. Yeah,
Speaker 1: that that that that particular term that two hundred years
Speaker 1: ago seventy of American workers were farmers showed up a
Speaker 1: lot in our research. Joe did an amazing job of
Speaker 1: gathering tons of articles on this subject, and I must
Speaker 1: have read that one statistic at least six or seven times.
Speaker 1: The people on the internet sometimes use the same sources,
Speaker 1: it might be or each other. Well, it's the startling
Speaker 1: figure because apparently, according to Kelly, only one percent of
Speaker 1: those previous farming jobs remain today, and that's because of automation.
Speaker 1: So automation all of these jobs that used to require
Speaker 1: humans or animals to be done on land have pretty
Speaker 1: much all been replaced by machines now. But but Joe,
Speaker 1: what happened to all those people who formerly had been
Speaker 1: working on farms? Now they have other jobs, or I
Speaker 1: mean some of them might be unemployed, but the ones
Speaker 1: who are employed have found other jobs to do, either
Speaker 1: other jobs using the equipment that has created automation on farms.
Speaker 1: So now they can drive a tractor or or or
Speaker 1: help marator build a tractor, do maintenance on the chicken
Speaker 1: milking machine whatever it is. Yeah, or maybe they work
Speaker 1: in it, or maybe maybe moved to a city and yeah,
Speaker 1: I did something else. So in other words, we see
Speaker 1: job creation as well some innovation and uh which again
Speaker 1: necessity mother of invention, right, sure, But Kelly goes on
Speaker 1: from that to claim that over the next century, So
Speaker 1: in the next like ninety years, the same trend will continue,
Speaker 1: seventy of today's jobs will be replaced by automation. And
Speaker 1: if that, obviously that's just a speculation which we can't
Speaker 1: know for sure. But if that's true, what does that
Speaker 1: mean for the world? Right? Will we continue to see
Speaker 1: the same sort of of of progression where new jobs
Speaker 1: get created that are totally either dependent upon the fact
Speaker 1: that we have robots or or just totally new that
Speaker 1: people then will enter into. And I mean it's it's
Speaker 1: a very exciting thing to think about, but also a
Speaker 1: little bit terrifying. Yeah, sure, right, Well, nobody wants to
Speaker 1: lose their job. And your job, your job is you
Speaker 1: in many ways. It's a huge part of your identity,
Speaker 1: and it's how you make your money, which is how
Speaker 1: you do everything else that isn't your job exactly right,
Speaker 1: I mean, without your job, you're in big trouble. So
Speaker 1: we all like to think that our jobs can't be
Speaker 1: replaced by robots. There is no way a computer program
Speaker 1: or an autonomous robot or some other kind of machine
Speaker 1: could do my job, right right, Well, I don't know
Speaker 1: about that. There are some people who have made I think,
Speaker 1: pretty convincing cases that yes, your job will be replaced
Speaker 1: by a robot almost no matter what you do. The
Speaker 1: chances are, whatever you do, it's probably going to be
Speaker 1: done by a machine before too long. Now, the before
Speaker 1: too long does require a little more examination because some
Speaker 1: some jobs are more easily automated than others. Right, So,
Speaker 1: so jobs, for example, that have a repetitive action that
Speaker 1: must be done over and over, those are prime candidates
Speaker 1: to be replaced by a computer or by a machine,
Speaker 1: and we've seen that happen already things like Amazon's warehouses,
Speaker 1: where they're depending more and more heavily upon robots, including
Speaker 1: robotic shelves that will zoom over to the packaging line
Speaker 1: whenever someone orders a specific item and then zoom back
Speaker 1: to where they were supposed to be, so instead of
Speaker 1: a worker going out to retrieve something from a shelf,
Speaker 1: the shelf comes to the worker. Right. On the other hand,
Speaker 1: jobs that require a lot of flexibility, where you're constantly
Speaker 1: moving from different kinds of tasks to other ones, those
Speaker 1: are going to be harder to automate, right, But we
Speaker 1: shouldn't be able to count on them being indefinitely difficult
Speaker 1: to automate, or at least to divide up among many
Speaker 1: other automated machines that can do the different parts of
Speaker 1: the tasks. Um, So I think we should look first
Speaker 1: at h There was a book that came out called
Speaker 1: Race Against the Machine by Eric Brynjolfson and Andrew McAfee,
Speaker 1: and they have made the case that, yeah, technology is
Speaker 1: very much going to be replacing jobs. Now. They might
Speaker 1: have a spin on it, says that's not necessarily a
Speaker 1: bad thing. But what's their case. Well, you know, they
Speaker 1: do say the sort of things we've been talking about already,
Speaker 1: that technology has always replaced jobs and that this is
Speaker 1: something that we've seen throughout history. But they claim that also,
Speaker 1: you know, this current era we're in is a little
Speaker 1: different from others uh that we've seen. We're seeing more
Speaker 1: of the replacement and more automation and uh quote less
Speaker 1: of the complimenting and creating of new jobs end quote.
Speaker 1: So in other words, there was an era early in
Speaker 1: the two thousands that were still in where we saw
Speaker 1: a dip in the number of jobs that were available,
Speaker 1: but an increase in productivity, which was kind of unprecedented,
Speaker 1: where where we didn't see the jobs rebound, we saw
Speaker 1: the productivity hold steady, but we didn't see new jobs
Speaker 1: being created. And there's a potential that this could be uh,
Speaker 1: you know, a long term trend, not something that is
Speaker 1: a little you know, just blip, yeah. Yeah. Andrew McAfee
Speaker 1: gave a ted X talk in Boston in June where
Speaker 1: he showed, as part of his presentation the data on
Speaker 1: the Great Recession where where it seemed okay as things
Speaker 1: started getting better towards the end of the Great Recession. Uh,
Speaker 1: we were seeing GDP come back up, we were seeing
Speaker 1: profits come back up. It looked like from a zoomed
Speaker 1: out level that the economy was doing better again, except
Speaker 1: the number of people employed did not rebound at the
Speaker 1: same time. Uh. So you may be experiencing a situation
Speaker 1: here where the economy as a whole might be doing fine,
Speaker 1: but lots of people aren't. Yeah, and well, it also
Speaker 1: depends on how you're defining how well the economy does.
Speaker 1: If you if you're talking about total wealth generated, right,
Speaker 1: if you're looking at it as a measurement of the
Speaker 1: number of people employed or percentage of employment, that it's
Speaker 1: not doing so well. Right, right, Well, I mean there's
Speaker 1: this is a multi factor issue, of course, partially related
Speaker 1: to how hard you can convince people to work, uh
Speaker 1: for a lower amount of money. But yeah, yeah, certainly, Uh,
Speaker 1: and that's got to compete with automation also, right, I mean,
Speaker 1: and like, would it be cheaper to hire somebody to
Speaker 1: work for a really low wage or to just pay
Speaker 1: up for the robots to do it for us? You know,
Speaker 1: it's a calculated decision that the the employer must make.
Speaker 1: Can you compete with increasingly efficient automation. That's a tough decision.
Speaker 1: But anyway, Uh so McAfee said pretty much, Yes, the
Speaker 1: droids are definitely coming for our jobs. Uh even the
Speaker 1: jobs of knowledge workers, he claims. So it's not just
Speaker 1: the three point five million truck drivers in the United
Speaker 1: States should be worried about the Google autonomous car. They should,
Speaker 1: but it's also that, for example, finance writers should have
Speaker 1: their eye on algorithms that can already write perfect journalistic
Speaker 1: reports on stock performance based off of the daily data
Speaker 1: that comes into You can send this program some numbers
Speaker 1: and it will generate a completely perfect report on the
Speaker 1: stock performance in English that can be read by humans.
Speaker 1: There are plenty of other examples of this too. There are,
Speaker 1: in fact, lots of trading algorithms that that stock traders
Speaker 1: are depending more and more heavily upon. In fact, we
Speaker 1: talked about that in the previous episode. Yeah. Yeah, it
Speaker 1: used to be people crunching numbers, and now it's computers
Speaker 1: crunching numbers way better than the people really could have. Now,
Speaker 1: I don't want to give the wrong impression about McAfee,
Speaker 1: because he's revealed in other places and uh, he's basically
Speaker 1: an optimist about the overall implications of technology for human existence.
Speaker 1: You know, we can do good things with it, but
Speaker 1: they're there are different paths that we could take from here,
Speaker 1: and some of them might not be good, right, uh,
Speaker 1: And almost all of them are going to involve the
Speaker 1: droids taking our jobs. That's that's going to be hard
Speaker 1: to escape at this point. Right. Again, it's a question
Speaker 1: of time scales, right right. And also the thing where
Speaker 1: in here in in in America are jobs have not
Speaker 1: really increased in UH an alignment with automation. But that's
Speaker 1: not true for all countries, is it. No, Well, for example,
Speaker 1: the i f R, the International Federation of Robotics, they
Speaker 1: found a piece where they were drawing attention to a
Speaker 1: study showing that in some countries rates of employment grew
Speaker 1: alongside increased use of industrial robots. But then at the
Speaker 1: same time i I I found an interesting observation made by
Speaker 1: a piece UH in April in the m I T
Speaker 1: Technology Review, where they pointed out this study cited by
Speaker 1: the International Federation of Robotics, which by the way, is
Speaker 1: a trade organization for robotics, so they sort of have
Speaker 1: ah UM pointed out that this study they're referring to
Speaker 1: is talking about dangerous and stupid robots that are not
Speaker 1: very versatile, like the ones that are cutting out our
Speaker 1: car bits, maybe not the ones that are potentially handling
Speaker 1: our children exactly right, So the effect could be very
Speaker 1: different when a smarter, next generation robot shows up that
Speaker 1: can more effectively replace human workers without needing so much handling.
Speaker 1: Now that being said, David au Tour of m I
Speaker 1: T has actually expressed some skepticism that machines will take
Speaker 1: over most jobs for the foreseeable future. And now he
Speaker 1: doesn't say that it's uh, you know that we're always
Speaker 1: going to have people in these jobs and that's that's
Speaker 1: going to be the fact forever and ever, but rather
Speaker 1: that this may be a longer timeline than what some
Speaker 1: people might have you believe. He points out that we've
Speaker 1: made machines that are really good at handling those repetitive,
Speaker 1: predictable tasks, but they aren't, like we said, as good
Speaker 1: at flexible ones, uh and responding to situations that have
Speaker 1: dynamic conditions like a human interaction. So, in other words,
Speaker 1: like if you've ever had to call into any kind
Speaker 1: of of tech support where you get the automated menus.
Speaker 1: The automated menus are really good at channeling you to
Speaker 1: the specific person who can help you. They're not so
Speaker 1: good at being able to handle the actual problem. I
Speaker 1: don't know if they're always even as good at the
Speaker 1: first thing you make. And I've been like, none of
Speaker 1: the options you just listed are what I want. Well,
Speaker 1: that that's that is again another limitation on the system, though, right.
Speaker 1: I mean, if it were a person that you were
Speaker 1: talking to, they could at least respond by saying, oh, well,
Speaker 1: then the best place to funnel your problem is to
Speaker 1: this person. Uh So that's kind of his point is
Speaker 1: saying that, you know, machines are not that great at
Speaker 1: those kind of tasks, and there's no reason to believe
Speaker 1: that artificial intelligence is going to reach a point that
Speaker 1: will make them comparable to human performance in terms of
Speaker 1: things like problem solving in common sense, Yeah, common sense,
Speaker 1: critical thinking, that kind of thing. That that sort of
Speaker 1: stuff is largely going to be in the human domain
Speaker 1: for the foreseeable future. Not that machines will never have
Speaker 1: a stab at this kind of thing, but that it's
Speaker 1: not as uh, it's not as imminent as something like
Speaker 1: the automation of routine like the dangerous dirty uh job
Speaker 1: that a lot of of robotic experts talk about, you know,
Speaker 1: these machines taking over for us. The experts in general
Speaker 1: are really divided on how they think all of this
Speaker 1: is going to play out. I mean, you know, it's
Speaker 1: it's easy for us being relatively uh amateurish in our opinions,
Speaker 1: and you know, you know, but but these people who
Speaker 1: work in this industry every day can't really agree whether
Speaker 1: this is leading to a utopia or a dystopia. It's
Speaker 1: a coin flip. Well, yeah, it almost is. Because there
Speaker 1: was a Pew survey released in augusteen that pulled almost
Speaker 1: two thousand experts in relevant fields like robotics, or economics
Speaker 1: or AI about the effects of robotics and AI on
Speaker 1: our economies, and the results were that fifty two per
Speaker 1: cent of these experts predicted a quote optimistic path, which
Speaker 1: entailed quote a future in which robots and digital agents
Speaker 1: do not displace more jobs than they create unquote, and
Speaker 1: at the same time, forty percent of the x or
Speaker 1: it's predicted a pessimistic outcome in which robots quote will
Speaker 1: display significant numbers of both blue and white collar workers
Speaker 1: and many of the experts expressed concern that this will
Speaker 1: lead to vast increases in income inequality, masses of people
Speaker 1: who are effectively unemployable, and breakdowns in the social order.
Speaker 1: That's that's what we would call a a bad outcome. Yeah,
Speaker 1: it would be chaotic, it would be uh and we'll
Speaker 1: talk more about that kind of dystopian outcome in the
Speaker 1: a little bit later in the episode. Yeah. Now, some people,
Speaker 1: I guess would argue that it's not so stark that
Speaker 1: a robot will either just completely do your job, like
Speaker 1: replace you outright, but the more robots may sort of
Speaker 1: enter the workforce in middle roles. You know what I mean.
Speaker 1: This is that complementary approach to the idea that we
Speaker 1: will have machines that will complement what we do. Yeah,
Speaker 1: construction is a really good ample of how this is
Speaker 1: working today because you know, we've got a lot of
Speaker 1: machines that do the heavy lifting and the precision cutting
Speaker 1: and stuff like that. But this this work does in
Speaker 1: fact complement, not replace, skilled construction workers who can do
Speaker 1: the planning and the physical control in the in the
Speaker 1: moment judgments. Right, it's that flexibility we mentioned that the
Speaker 1: workers themselves have that the robots don't have I mean,
Speaker 1: a robot is really good at doing one thing over
Speaker 1: and over, but it can't walk around the site and
Speaker 1: you know, figure out what's the next job that somebody
Speaker 1: needs help with. Right. Robot surgery is another great example.
Speaker 1: The robot surgery tools are tools. They are extensions of
Speaker 1: an actual human surgeon. The human surgeon uses a device
Speaker 1: that allows him to or her to make control the incisions,
Speaker 1: and you know, you have the robotics that are able
Speaker 1: to translate those movements into actual actions against the patient.
Speaker 1: So you could have larger movements being trans lay it
Speaker 1: into very precise movements on the robotic scale. Yeah. Yeah,
Speaker 1: Together the machine and the human can be more precise
Speaker 1: than either would have been able to be alone. Right,
Speaker 1: So the human can make human decisions, the robot can
Speaker 1: translate motions into very precise movements, and you get the
Speaker 1: best of both worlds. But again, it's it's complementary, it's
Speaker 1: not substitution. Now, I guess the question is, but how
Speaker 1: long will that be the case, Because as robots get
Speaker 1: smarter and more agile and more flexible, they're going to
Speaker 1: increasingly be able to do these things that Right now,
Speaker 1: we're feeling like few only humans can do them. Well, yeah,
Speaker 1: I mean, let's let's go back to the driverless cars
Speaker 1: as an example. We talk about that in a positive sense,
Speaker 1: about how the cars are able to sense changing conditions
Speaker 1: much more quickly than humans are. So, for example, our
Speaker 1: our human reaction time means that when something happens, there's
Speaker 1: a delay between when we perceive it and when we
Speaker 1: can take any action. That delay is much much shorter
Speaker 1: for a robotic system that can react almost I mean
Speaker 1: to us it seems instantly. Uh, they can react to
Speaker 1: changing conditions. So when we look at the example of
Speaker 1: the driverless cars, like Google's driverless cars, they've been involved
Speaker 1: in two accidents, neither of which were caused by the car,
Speaker 1: right and at least not the driverless car system. They
Speaker 1: were both human error. I think one was when a
Speaker 1: human operator was operating the car under manual control exactly. Yeah, yeah,
Speaker 1: neither of them were the actual robotic system. So we
Speaker 1: can already see that at least in that in that
Speaker 1: kind of test scenario, because of course that's not a
Speaker 1: wide rollout, but we can see within that controlled test
Speaker 1: scenario that it appears the robots have the edge on
Speaker 1: us in that field. Already, so it stands to reason
Speaker 1: that we will see this continue in other disciplines over time. Obviously,
Speaker 1: some are gonna take a lot longer than others, because
Speaker 1: some tasks are more innately human than others. Yeah, So
Speaker 1: I think a theme I'm seeing immer ridge and the
Speaker 1: question of will robots take our jobs? Is the people
Speaker 1: who are optimistic about it aren't usually saying no, robots
Speaker 1: will not take most of our jobs. They're saying, yeah,
Speaker 1: they will, but it will be okay for one reason
Speaker 1: or another. Yeah, it seems to be most people agree. Yeah,
Speaker 1: robots are going to take a huge portion of our
Speaker 1: jobs that exist today, maybe eventually all of them. And
Speaker 1: so if they do take our jobs, the next question
Speaker 1: is will this necessarily be a bad thing? And I
Speaker 1: want to revisit or earlier I mentioned that Wired article
Speaker 1: from twelve by Kevin kelly Um and his whole point
Speaker 1: is that though robots and automation might eliminate old jobs,
Speaker 1: they will create new jobs, and the new jobs will
Speaker 1: be better jobs. So I want to read a quote
Speaker 1: from part of his article that I think sums this
Speaker 1: up pretty well. He says, in the coming years, robot
Speaker 1: driven cars and trucks will become ubiquitous. This automation will
Speaker 1: spawn the new human occupation of trip optimizer, a person
Speaker 1: who tweaks the traffic system for optimal energy and time usage.
Speaker 1: Routine robo surgery will necessitate new skills of keeping machines sterile.
Speaker 1: When automatic self tracking of all your activities becomes a
Speaker 1: normal thing to do, a new breed of professional analysts
Speaker 1: will arise to help you make sense of the data.
Speaker 1: And of course we will need a whole army of
Speaker 1: robot nanny's dedicated to keeping your personal bots up and running.
Speaker 1: Each of these new vocations will in turn be taken
Speaker 1: over by robots later. And so this sort of leads
Speaker 1: to where he proposes there's like a seven stage cycle
Speaker 1: of automation, and then automation anxiety, and then finally automation complacence,
Speaker 1: where first it seems like a robot could never do
Speaker 1: what you can do. Then you say, well, okay, it
Speaker 1: can do some of what I can do, but it
Speaker 1: can't do everything I can do. Then it says it
Speaker 1: can do everything I can do, but it needs me
Speaker 1: to take care of it when it messes up or
Speaker 1: breaks down, which all the time. So this this would
Speaker 1: be like the copy machine jamming yet again. Yeah. Then
Speaker 1: he says, oh, well, okay, so it operates flawlessly on
Speaker 1: the routine, but I still need to tell it what
Speaker 1: to do. I need to train it for new tasks,
Speaker 1: he says. Then after that, okay, can just have that job.
Speaker 1: I didn't want it anyway because it's that job was
Speaker 1: not a good job for humans to do. And then
Speaker 1: the next stage is wow, quote wow, now that robots
Speaker 1: are doing my old job, my new job is much
Speaker 1: more fun and pays much more. And then finally I'm
Speaker 1: so glad a robot and computer cannot possibly do my
Speaker 1: new job, and so the whole thing begins again. This
Speaker 1: actually reminds me also of what Dr Henrik Christensen said
Speaker 1: when uh I did the the Um for Thinking video
Speaker 1: episode from Georgia Tech and we talked about Robotics Week
Speaker 1: and we looked at the various robots that they have
Speaker 1: in their in their labs. We only saw a few
Speaker 1: of them. They were really fascinating, and he talked very
Speaker 1: much in the same kind of sense, the idea that
Speaker 1: that robots are taking over the three DS, the dull,
Speaker 1: dangerous and dirty jobs. And then uh, you know, we'll
Speaker 1: see that continue and then we'll see it grow into
Speaker 1: the next round. But each time it's like we're just
Speaker 1: a little bit ahead, like you know, they they the
Speaker 1: robots catch up to us, and then we find the
Speaker 1: new thing to do. And people might ask, well, what's
Speaker 1: the new thing to do? And the honest answer is,
Speaker 1: we can't anticipate that. If you had looked back two
Speaker 1: hundred years ago, before we got into the real industrial
Speaker 1: Revolution and ask people, you know what, what do you foresee,
Speaker 1: Like if you explain to them this job that you
Speaker 1: have is not going to exist in another decade or so,
Speaker 1: they wouldn't have been able to anticipate. Sure. And this
Speaker 1: is also part of the problem with advancing technology and
Speaker 1: robotics and AI, because it seems that the gap between
Speaker 1: the creation of new jobs and the creation of robots
Speaker 1: that can do them better than human it's closing or
Speaker 1: or shortening at any rate. Yeah, that certainly could be
Speaker 1: the case. In fact, in response to that article by
Speaker 1: Kelly I was just talking about, Gary Marcus wrote an
Speaker 1: interesting response in The New Yorker. It was also in December,
Speaker 1: and Marcus argued that these new professions that Kelly claims
Speaker 1: will be created by automation or will actually be taken
Speaker 1: over by robots just as easily and just as quickly. Uh,
Speaker 1: And of course Kelly says eventually they will be. But
Speaker 1: I think the idea is that there's no reason to
Speaker 1: think they'll lag behind all that long to be taken
Speaker 1: up by humans for any significant period of time in
Speaker 1: the meantime, you know. So as examples, he points out
Speaker 1: that there's already such a thing as the robots sterilization
Speaker 1: expert that uh, that you know, Kelly claimed would be
Speaker 1: done by humans, and that there are already forms of
Speaker 1: automated trip optimization. And this is a quote. With advances
Speaker 1: in both hardware and software, the time between the invention
Speaker 1: of a job and it's automated replacement is getting shorter, right,
Speaker 1: So that we're getting our our machines are getting better faster,
Speaker 1: and that eventually there's not going to be much of
Speaker 1: a gap between when a new job is created and
Speaker 1: when a robot can do it. Pretty well. Yeah, now,
Speaker 1: this seems to me to be based upon the assumption
Speaker 1: that this this will be a continuously accelerating trend, very
Speaker 1: much the way we look at Moore's law. How Moore's
Speaker 1: Law has meant that we see a doubling, effectively a
Speaker 1: doubling and computer power every eighteen to twenty four months,
Speaker 1: depending upon when you consult Moore's law. Um, And I
Speaker 1: don't know that it's safe to make that assumption. Software
Speaker 1: development is a different It takes a different pathway than uh,
Speaker 1: the ability to cram more discrete components onto a square
Speaker 1: inch of silicon wafer. But but I do see where
Speaker 1: he's getting at. And I it's not that I have
Speaker 1: the data right in front of me to to dismiss
Speaker 1: it or to disagree it. Just something to me says
Speaker 1: that we don't, hite have the evidence to prove that
Speaker 1: this this trend we're seeing right now is going to
Speaker 1: be continuous. I think that we're I think we'll continue
Speaker 1: to see advances. I just don't know that it will
Speaker 1: always be at the same accelerated rate. So it maybe
Speaker 1: that we're in a golden age of of development right now,
Speaker 1: But then things might hit a wall and slow down.
Speaker 1: We don't we don't have a way of knowing. Maybe
Speaker 1: they won't, maybe they will continue the way he has predicted.
Speaker 1: But yeah, yeah, again, like like the incredible features, incredible
Speaker 1: and unknowable. Um, so so either way, Uh, we have
Speaker 1: a couple of options for for dealing with this potential situation. Right,
Speaker 1: So we've been talking a lot in you know, kind
Speaker 1: of hypotheticals. But let's say that you are genuinely a
Speaker 1: person whose job is going to be eliminated because of automation.
Speaker 1: Put yourself in this scenario. You are you come into
Speaker 1: work one Monday morning and your boss says, I just
Speaker 1: bought a robot that does exactly what you do. Yeah,
Speaker 1: you are no longer needed here. We wish you well
Speaker 1: on your future opportunities. Um. So, the the options are
Speaker 1: really one of two things. You can either go and
Speaker 1: look for another job you can do that isn't already
Speaker 1: being automated by other devices, which uh, you know, depending
Speaker 1: upon your education level, you would you might have some
Speaker 1: limitations there. Or you go and pursue further education so
Speaker 1: that you can get a better job one of the
Speaker 1: one that has not yet been automated. Uh. Those are
Speaker 1: really your two and most of the robotics experts that
Speaker 1: I've seen talk about this issue, uh tend to say
Speaker 1: that that's the path they hope people all take the
Speaker 1: idea to to educate yourself to better yourself so that
Speaker 1: you can end up getting a better and more fulfilling sure,
Speaker 1: which is a lovely idea, except the part where you're
Speaker 1: asking someone who's unemployed to somehow pay for education and
Speaker 1: also food us get a new degree. It's one of
Speaker 1: those things that's really easy to say because you're talking
Speaker 1: about it, like, you know, the idea on paper, and
Speaker 1: then when you start getting into okay, let's avoid the
Speaker 1: whole paper subject. Let's talk about this as if it's
Speaker 1: a real event. I've just lost my job, I no
Speaker 1: longer have income, I still have outstanding UH bills to pay.
Speaker 1: How do I do this thing? You're from? Ye, yes, yes,
Speaker 1: some of us are. Some of us may still be
Speaker 1: carrying those student loan debts. Um, yeah, it's it's you know,
Speaker 1: when you get into those specifics, it gets to be
Speaker 1: a real issue. But the thing to keep in mind
Speaker 1: is that people on like robots, we are flexible. We
Speaker 1: have that capability to switch gears. And then I made
Speaker 1: a joke about if your mechanical robot, you might physically
Speaker 1: be able to switch gears, but I'm talking the figurative
Speaker 1: switching gears. We have the option of being able to say,
Speaker 1: you know, this is not working out for me, or
Speaker 1: the say is no longer an option for me. I
Speaker 1: need to pursue something else. And while it's easy to
Speaker 1: say that, I mean, obviously personal circumstances might limit what
Speaker 1: your your real opportunities are, but that's still more opportunities
Speaker 1: than a robot. A robot is built to do a
Speaker 1: specific task. Uh. We don't have any general purpose robots
Speaker 1: that can do anything. So and we probably well and
Speaker 1: we I don't think we're going to within a century.
Speaker 1: I honestly don't think we're going to get quite that far. Um.
Speaker 1: I think we will get there, but I think it's
Speaker 1: a harder problem than what a lot of the discussion
Speaker 1: has kind of led up to. Yeah. Um. The aforementioned
Speaker 1: David Autor of of m I T stresses how much
Speaker 1: we human laborers need to be training for and supplying
Speaker 1: tasks that will be complimented by automation, not substituted by it. Um.
Speaker 1: He calls this Polanis paradox, after the mid twentieth century
Speaker 1: philosopher Michael Polani, who said, we know more than we
Speaker 1: can tell um. And to to to illustrate this to yeah, yeah,
Speaker 1: to to fall back on kind of our favorite example
Speaker 1: of machine learning here on this podcast, the cat identification problem. Okay,
Speaker 1: any toddler can identify a picture of a cat or
Speaker 1: most of them at any rate. Um, but it took
Speaker 1: a network of sixteen thousand processors to figure out the
Speaker 1: same thing in computer terms. Um, So what does the
Speaker 1: toddler know that all of those computers had to learn?
Speaker 1: And Okay, in this particular example, we've enumerated the answers
Speaker 1: multiple times here on this podcast. Um but and it
Speaker 1: was a lot a lot of things, was basically the answer.
Speaker 1: But in a lot of other cases the answer is
Speaker 1: we're not sure. Yeah, yeah, A lot of it has
Speaker 1: comes down to just the fact that the wiring of
Speaker 1: the brain is so different from the classical computer. And
Speaker 1: we've talked about machine learning and neural networks and how
Speaker 1: they are a fraction of the complexity of an actual brain.
Speaker 1: I mean, they they work on the same principle, but
Speaker 1: they are a tiny, tiny, you know, a minuscule example
Speaker 1: how a brain works. Because to produce anything that would
Speaker 1: operate on a brain's level would require a huge amount
Speaker 1: of electricity and a lot of processors. But yeah, it's
Speaker 1: it's that's something to keep in mind. So, so it's
Speaker 1: a very optimistic view, or a relatively optimistic view compared
Speaker 1: to some of the other that that what forty percent
Speaker 1: of of naysayers out there who think that things are
Speaker 1: going to be potentially terrible? Um, but is it a
Speaker 1: possibility that eliminating these jobs for humans would be okay? Yeah,
Speaker 1: that's another option we haven't so we've talked about. It
Speaker 1: could be bad. It could lead to uh just massive unemployment,
Speaker 1: social unrest, and the breakdown of society, dogs and cats
Speaker 1: living together. It could be okay in that Well, we'll
Speaker 1: just keep creating new jobs people. There will always be
Speaker 1: jobs for us even as we replace the old ones.
Speaker 1: What if it's the case that we replace all the jobs,
Speaker 1: there are no jobs for people, and that's still okay.
Speaker 1: So we've eliminated the need for labor. Could it be
Speaker 1: possible that we live in a world where labor is
Speaker 1: no longer necessary and we still are able to be people? Right,
Speaker 1: So there's this idea that you can create more wealth
Speaker 1: for everyone. You've probably heard this phrase make the pie higher,
Speaker 1: al right, instead of making it bigger. Ringe. Well, okay,
Speaker 1: so the idea goes like this, Yes, replacing human workers
Speaker 1: with robots puts humans out of the job, but it
Speaker 1: could also create just a starkly enormous amount of wealth. Uh,
Speaker 1: the reason humans are being replaced by robots is that
Speaker 1: the robots are more efficient. They do the job better,
Speaker 1: they can do it faster, they can create more of something,
Speaker 1: create more product or value you in a shorter amount
Speaker 1: of time, and putting aside the cost of maintenance and procurement,
Speaker 1: you don't actually have to pay the robots. They're they're
Speaker 1: creating new wealth. So if this leads to societies that
Speaker 1: are flush with surplus money and value, it could quote
Speaker 1: make the pie higher. Everybody's little slice gets bigger without
Speaker 1: taking any more from anybody else. Now, if that could happen,
Speaker 1: that would be great, but that's not a guaranteed. That's
Speaker 1: not something that we oh, sure, if we just eliminate
Speaker 1: all the jobs and have lots of robots, everybody will
Speaker 1: be happy. Because, for example, the economist Paul Krugman pointed
Speaker 1: this out in a New York Times column in December,
Speaker 1: where he said, quote smart machines may make higher GDP possible,
Speaker 1: so higher gross domestic product, but also reduce the demand
Speaker 1: for people, including smart people. So we could be looking
Speaker 1: at a society that grows ever richer, but in which
Speaker 1: all the gains in wealth accrue to whoever owned the robots.
Speaker 1: So that yeah, and by a robot guys. Marcus also
Speaker 1: pointed this out in his New Yorker piece, So what
Speaker 1: do you do if you have a society that's creating
Speaker 1: ridiculous amounts of prosperity but most people can't get work
Speaker 1: of any kind or enjoy that prosperity at all. Yeah,
Speaker 1: And so here's where I think we might earn some
Speaker 1: hate mail, But we're going to have to say it
Speaker 1: because I think it's the logical conclusion. Could it be
Speaker 1: that advances in automation will make some really major form
Speaker 1: of wellthree distribution or socialism necessary in the future if
Speaker 1: we want to have a stable society. Yeah, I think
Speaker 1: that's possible. And even if you're very, very opposed to
Speaker 1: socialism currently, you might not be in this scenario because
Speaker 1: I want to highlight a few things. People are usually
Speaker 1: opposed to socialism because they believe, for one thing, it
Speaker 1: discourages productivity. You know, if you are sure that you
Speaker 1: can get help and that you always have a net
Speaker 1: to fall act on, it gives you less incentive to
Speaker 1: work hard at your job and create wealth. Well, this
Speaker 1: doesn't really matter. If you have a robotic workforce, you
Speaker 1: don't need an incentive to work harder. There's there's no
Speaker 1: there's no where to work harder in the first place, right, right,
Speaker 1: What about the argument that it's not fair to take
Speaker 1: hard earned money from from workers. Yeah, I mean that
Speaker 1: that could make sense to a lot of people today,
Speaker 1: But at the same time, it doesn't really make sense
Speaker 1: sense anymore if you're imagining a society where more than
Speaker 1: of people literally cannot make money through labor, and the
Speaker 1: society is replete with extra wealth, so there's just tons
Speaker 1: and tons to go around, and most people have no
Speaker 1: way of getting it themselves. Yeah, and then on top
Speaker 1: of that, let's talk about a world where because we
Speaker 1: might imagine a dystopian future in which you've got this
Speaker 1: this tiny elite that owners robot owners, they control all
Speaker 1: the wealth, and then everybody else if everybody else is
Speaker 1: genuinely unemployed, as in there is no way for them
Speaker 1: to earn income, then you have no consumers. You have
Speaker 1: no customers. Know, when buying all of the products that
Speaker 1: the robots are making so industriously right, So there's no
Speaker 1: reason for robots to make anything because there's no buying
Speaker 1: power to purchase the things. I mean, unless you're making
Speaker 1: the three families that the roots trading stuff. So unless
Speaker 1: those three families just want to see the rest of
Speaker 1: the world whither away. It it makes it's it's not
Speaker 1: a supportable system. It ultimately would crumble in on itself.
Speaker 1: So at some point some sort of redistribution is absolutely
Speaker 1: necessary because otherwise you have no consumption. Yeah, redistribution or
Speaker 1: other ideas that make people uncomfortable in the same way,
Speaker 1: like a sort of centrally planned economy. Uh, you know,
Speaker 1: examples of which would be things like communism. But this
Speaker 1: wouldn't be like communism. It would be the fact that well,
Speaker 1: I guess we would call it something like we have
Speaker 1: collected of ownership of our robot workforce, and the proceeds
Speaker 1: of what these robots generate or split up equally among
Speaker 1: everybody in the country. Yeah. One other issue that I
Speaker 1: thought was interesting was the psychological burden that this place
Speaker 1: is on people. The idea that you get some sense
Speaker 1: of purpose from your job, and you know, I guess
Speaker 1: that that depends upon each individual, right, I mean, it
Speaker 1: all depends on who you are and what job you have.
Speaker 1: I know, I get a lot of satisfaction out of
Speaker 1: my job and I find a lot of purpose in it,
Speaker 1: and the thought of not having that, certainly is one
Speaker 1: of those where you start to question what would you
Speaker 1: do to give yourself purpose? And this is another one
Speaker 1: of those areas where I think we would say, you know,
Speaker 1: we don't know, we're we're inventive people, and it's very
Speaker 1: possible that we would have Uh. Each individual comes up
Speaker 1: with his or her own purpose and becomes a you know,
Speaker 1: something they define for him or herself, and uh, I
Speaker 1: kind of like that idea. I don't know if that's
Speaker 1: actually realistic or not. I mean, not being in that world,
Speaker 1: it makes it very difficult for me to draw a conclusion. Well,
Speaker 1: the only examples that we have of anything this this
Speaker 1: stark is science fiction. I mean stuff like Star Trek,
Speaker 1: for example, has this kind of utopian society in which
Speaker 1: it's a it's a post post economic world at least
Speaker 1: post scarcity. Right, we should do an episode all about
Speaker 1: Star Trek and the economics. Guys. That should be the
Speaker 1: very next episode we do. The Star Trek economy. I
Speaker 1: totally agree, and it's really convenient because we already have
Speaker 1: notes typed up for it. I guess that wraps up
Speaker 1: robots for today. But join us next time where we're
Speaker 1: actually going to tackle something that's been asked for multiple
Speaker 1: times by fans. Uh, the star trek economy. Now does
Speaker 1: it work? And can we put it in place? And
Speaker 1: boy was that a fun one to research? So yeah,
Speaker 1: this this was a really fascinating topic and I mean
Speaker 1: it is one that clearly is important and obviously it
Speaker 1: has has a real impact on real people. Uh. And
Speaker 1: I think that there's still room for optimism. I I
Speaker 1: kind of side with the of that of that Pew study,
Speaker 1: but I think it's one of those things that we
Speaker 1: do have to keep in mind in order for that
Speaker 1: optimism to remain realistic. Absolutely. Yeah, if if we were
Speaker 1: replaced by robotic podcasters, would you guys go all letite
Speaker 1: on them? I mean, would you destroy those robots? Um?
Speaker 1: I mean, to be fair, I've suspected Josh Clark of
Speaker 1: being a robot for like the last five years. There
Speaker 1: is no robot on earth that could equal the badness
Speaker 1: of your puns. I that's true, job is saying that's
Speaker 1: that is an extremely human feature. Yet I did make
Speaker 1: the entire Have you ever read where the robots tried
Speaker 1: to write jokes? They're so bad there not as bad
Speaker 1: as yours. The editorial department as a whole groaned at
Speaker 1: one of my puns last week. So that was a
Speaker 1: proud day for me. People. It was it was great.
Speaker 1: I don't remember what it was, but it was beautiful.
Speaker 1: I remember specifically. I'll tell you after the podcast. So
Speaker 1: we're gonna wrap this up, guys. If you have any
Speaker 1: suggestions for future topics. I mean, this was a listener
Speaker 1: suggestion and it was so much fun. If you have
Speaker 1: a suggestion, send us an email our addresses f W
Speaker 1: Thinking at how Stuff Works dot com, or drop us
Speaker 1: a line on Facebook, Twitter or Google Plus. At Twitter
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Speaker 1: you think, and we will talk to you again really soon.
Speaker 1: For more on this topic in the future of technology,
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