December 10, 2014 • 44 mins
Are robots going to take our jobs? Will we just get new jobs? And will it be a smooth transition?
Learn more about your ad-choices at https://www.iheartpodcastnetwork.com
See omnystudio.com/listener for privacy information.
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
Brought to you by Toyota. Let's go places. Welcome to
Forward Thinking, Tather and welcome to Forward Thinking, the podcast
that looks at the future and says, take this bot
and shove it. I'm Jonathan Strickland, I'm Lauren Volga, and
(00:21):
I'm Joe McCormick. As you might have guessed from the
thing that just came out of Jonathan's mouth, today, we're
going to be talking about robots, robots and jobs. Yeah.
As it turns out, we've had quite a few listeners
and viewers of Forward Thinking ask us to cover this topic.
It's it's one that's had a lot of coverage in
the media, and so we're looking at robots and jobs
(00:44):
right well, specifically the question will robots steal our jobs? Right? Right? Yeah?
I mean, which is kind of a terrific headline, which
I think is part of why it's so popular in
the media. But but I mean it captures our imagination
in a very specific way, because I mean, we are
all seeing all of this autumn nation that we've never
seen before kind of come up and and influence our
(01:04):
workforce in very interesting ways. Yeah, not just in uh,
in the ways that have been traditionally depicted like in manufacturing,
you know, heavy manufacturing, but in ways that affect you know,
white colored jobs as well. And we're going to cover
all this kind of stuff. But uh, let's let's look
down memory lane, because Joe, you pulled up something fascinating,
(01:25):
something I had heard but never looked into, and you
really found some information that that taught me some stuff. Well,
I want to get to the core of what we're
talking about, which is not just robots in the sense
of the way we picture them, which is a thing
with legs and arms. You know, it's vaguely human basically, right,
I go with Robbie the robot, But that's that's classic. Generally,
(01:47):
we're talking about all automation, any any way in which
a machine can do a job that previously could have
only been done by a human or an animal. Maybe.
Uh so, how about the Luodites. You all know the Luodite.
It's not just a term we call each other when we, like,
you know, accidentally sent a text message to the wrong
person or something like that. It's actually a historical group.
(02:09):
And of course you know who they were, right, Well,
well I do now because you did the research, Joe. Well,
they hated machines, right, that's the that's the whole thing.
That's the way I the way I've always heard it
used is yeah, it's people who just dog on it
can't either grasp technology and therefore they hate it because
of that, or they just outright think that new stuff
is bad for some reason. I assumed it was people
(02:31):
who had some kind of aesthetic or religious opposition to
technology in general. And turns out I was being a
total nano rod because that is not what the Luddites were.
So the original Luddites were a movement of textile workers
in England in the early eighteen hundreds, and they were
not against technology in general. They were famous for like
(02:53):
burning factories and smashing factory machines, but not because they
believe machines were evil. In fact, they were more or
less a labor movement. Uh. They were probably workers who
used machines themselves in their jobs, and they arose from
conditions of economic trouble and unemployment in Great Britain that
was part of a depression during and following the Napoleonic
(03:15):
Wars in the early eighteen hundred. So they were seeking
better employment, more work, better wages, but part of the
problem they perceived with the labor market. Then it was
the so called labor saving devices and machines, which often
meant that the same manufacturing job that used to require
a skilled crafts person could be performed by a machine,
(03:38):
or for a lower wage, by an unskilled worker paired
with the machine. So in a kind of certain qualified
since machines took their jerbs and they weren't happy about it.
And you can understand why. We've heard the same story,
the the apocryphal story that sabotage comes from the sabo
thrown into the cogs of the machine. From this but
(03:59):
similar story easy like uh and we Joe, you and
I had had a brief conversation before we came in
to record the podcast. We were just kind of talking
about the subject, and we mentioned that this this complaint
was again about the loss and jobs, not necessarily technology.
If in fact the manufacturers had found a super cheap
source of labor, the same problem would have existed. It
(04:22):
wasn't just that it was a machine. Yeah, if you
could have just found people who would do the same
jobs cheaper and take those jobs away from these workers.
I found a really easy way to train horses to
do it. Are horses are terrible at weaving, but uh,
you know they they just like all manual dexterity. But
in economy in England, it would have had to be rabbits, right,
(04:45):
they have a factory, they would have factories of rabbits
weaving cloth. I think it would be hairs actually sorry,
but uh, in economics we call we call this this
process substitution, where we use a machine to substitute for uh,
human labor. Uh. And there's another idea called complimentarianism complimenting.
(05:06):
So we're gonna be talking a lot about substituting versus
complimenting in this podcast. But as long as humans have
been innovating, we've had this issue to some extent. I mean,
if you look at agriculture, and we'll talk a lot
about agriculture, uh, you know, just the advent of the
plow made a lot of of changes because now you
(05:26):
didn't need as many human workers howing the fields as
you did before. And so this is not a new
idea by any extent. It goes back, you know, centuries,
but we see it uh accelerated in the era of
technological advances and development. Yeah, and so I think a
(05:47):
good place to start with the most recent modern incarnation
of this worry that that machines, automation, robots will take
our jobs is this pretty optimistic article that Kevin Kelly
wrote for Why, aired in December, is called better than
Human Why Robots will and Must take Our Jobs? Um,
(06:07):
and Kelly says this, two hundred years ago, seventy percent
of American workers were farmers. By about a hundred years
after that, it was some yeah, I read in another
article entirely, but I wanted to toss that in. Yeah,
that that that that particular term that two hundred years
ago seventy of American workers were farmers showed up a
(06:27):
lot in our research. Joe did an amazing job of
gathering tons of articles on this subject, and I must
have read that one statistic at least six or seven times.
The people on the internet sometimes use the same sources,
it might be or each other. Well, it's the startling
figure because apparently, according to Kelly, only one percent of
(06:49):
those previous farming jobs remain today, and that's because of automation.
So automation all of these jobs that used to require
humans or animals to be done on land have pretty
much all been replaced by machines now. But but Joe,
what happened to all those people who formerly had been
working on farms? Now they have other jobs, or I
(07:12):
mean some of them might be unemployed, but the ones
who are employed have found other jobs to do, either
other jobs using the equipment that has created automation on farms.
So now they can drive a tractor or or or
help marator build a tractor, do maintenance on the chicken
milking machine whatever it is. Yeah, or maybe they work
(07:34):
in it, or maybe maybe moved to a city and yeah,
I did something else. So in other words, we see
job creation as well some innovation and uh which again
necessity mother of invention, right, sure, But Kelly goes on
from that to claim that over the next century, So
in the next like ninety years, the same trend will continue,
(07:55):
seventy of today's jobs will be replaced by automation. And
if that, obviously that's just a speculation which we can't
know for sure. But if that's true, what does that
mean for the world? Right? Will we continue to see
the same sort of of of progression where new jobs
get created that are totally either dependent upon the fact
(08:19):
that we have robots or or just totally new that
people then will enter into. And I mean it's it's
a very exciting thing to think about, but also a
little bit terrifying. Yeah, sure, right, Well, nobody wants to
lose their job. And your job, your job is you
in many ways. It's a huge part of your identity,
(08:39):
and it's how you make your money, which is how
you do everything else that isn't your job exactly right,
I mean, without your job, you're in big trouble. So
we all like to think that our jobs can't be
replaced by robots. There is no way a computer program
or an autonomous robot or some other kind of machine
could do my job, right right, Well, I don't know
(09:03):
about that. There are some people who have made I think,
pretty convincing cases that yes, your job will be replaced
by a robot almost no matter what you do. The
chances are, whatever you do, it's probably going to be
done by a machine before too long. Now, the before
too long does require a little more examination because some
(09:24):
some jobs are more easily automated than others. Right, So,
so jobs, for example, that have a repetitive action that
must be done over and over, those are prime candidates
to be replaced by a computer or by a machine,
and we've seen that happen already things like Amazon's warehouses,
(09:46):
where they're depending more and more heavily upon robots, including
robotic shelves that will zoom over to the packaging line
whenever someone orders a specific item and then zoom back
to where they were supposed to be, so instead of
a worker going out to retrieve something from a shelf,
the shelf comes to the worker. Right. On the other hand,
(10:08):
jobs that require a lot of flexibility, where you're constantly
moving from different kinds of tasks to other ones, those
are going to be harder to automate, right, But we
shouldn't be able to count on them being indefinitely difficult
to automate, or at least to divide up among many
other automated machines that can do the different parts of
the tasks. Um, So I think we should look first
(10:30):
at h There was a book that came out called
Race Against the Machine by Eric Brynjolfson and Andrew McAfee,
and they have made the case that, yeah, technology is
very much going to be replacing jobs. Now. They might
have a spin on it, says that's not necessarily a
bad thing. But what's their case. Well, you know, they
(10:51):
do say the sort of things we've been talking about already,
that technology has always replaced jobs and that this is
something that we've seen throughout history. But they claim that also,
you know, this current era we're in is a little
different from others uh that we've seen. We're seeing more
of the replacement and more automation and uh quote less
(11:13):
of the complimenting and creating of new jobs end quote.
So in other words, there was an era early in
the two thousands that were still in where we saw
a dip in the number of jobs that were available,
but an increase in productivity, which was kind of unprecedented,
where where we didn't see the jobs rebound, we saw
(11:35):
the productivity hold steady, but we didn't see new jobs
being created. And there's a potential that this could be uh,
you know, a long term trend, not something that is
a little you know, just blip, yeah. Yeah. Andrew McAfee
gave a ted X talk in Boston in June where
he showed, as part of his presentation the data on
(11:58):
the Great Recession where where it seemed okay as things
started getting better towards the end of the Great Recession. Uh,
we were seeing GDP come back up, we were seeing
profits come back up. It looked like from a zoomed
out level that the economy was doing better again, except
the number of people employed did not rebound at the
(12:19):
same time. Uh. So you may be experiencing a situation
here where the economy as a whole might be doing fine,
but lots of people aren't. Yeah, and well, it also
depends on how you're defining how well the economy does.
If you if you're talking about total wealth generated, right,
if you're looking at it as a measurement of the
number of people employed or percentage of employment, that it's
(12:43):
not doing so well. Right, right, Well, I mean there's
this is a multi factor issue, of course, partially related
to how hard you can convince people to work, uh
for a lower amount of money. But yeah, yeah, certainly, Uh,
and that's got to compete with automation also, right, I mean,
and like, would it be cheaper to hire somebody to
(13:03):
work for a really low wage or to just pay
up for the robots to do it for us? You know,
it's a calculated decision that the the employer must make.
Can you compete with increasingly efficient automation. That's a tough decision.
But anyway, Uh so McAfee said pretty much, Yes, the
droids are definitely coming for our jobs. Uh even the
(13:25):
jobs of knowledge workers, he claims. So it's not just
the three point five million truck drivers in the United
States should be worried about the Google autonomous car. They should,
but it's also that, for example, finance writers should have
their eye on algorithms that can already write perfect journalistic
reports on stock performance based off of the daily data
(13:47):
that comes into You can send this program some numbers
and it will generate a completely perfect report on the
stock performance in English that can be read by humans.
There are plenty of other examples of this too. There are,
in fact, lots of trading algorithms that that stock traders
are depending more and more heavily upon. In fact, we
(14:08):
talked about that in the previous episode. Yeah. Yeah, it
used to be people crunching numbers, and now it's computers
crunching numbers way better than the people really could have. Now,
I don't want to give the wrong impression about McAfee,
because he's revealed in other places and uh, he's basically
an optimist about the overall implications of technology for human existence.
You know, we can do good things with it, but
(14:29):
they're there are different paths that we could take from here,
and some of them might not be good, right, uh,
And almost all of them are going to involve the
droids taking our jobs. That's that's going to be hard
to escape at this point. Right. Again, it's a question
of time scales, right right. And also the thing where
in here in in in America are jobs have not
(14:53):
really increased in UH an alignment with automation. But that's
not true for all countries, is it. No, Well, for example,
the i f R, the International Federation of Robotics, they
found a piece where they were drawing attention to a
study showing that in some countries rates of employment grew
alongside increased use of industrial robots. But then at the
(15:16):
same time i I I found an interesting observation made by
a piece UH in April in the m I T
Technology Review, where they pointed out this study cited by
the International Federation of Robotics, which by the way, is
a trade organization for robotics, so they sort of have
ah UM pointed out that this study they're referring to
(15:37):
is talking about dangerous and stupid robots that are not
very versatile, like the ones that are cutting out our
car bits, maybe not the ones that are potentially handling
our children exactly right, So the effect could be very
different when a smarter, next generation robot shows up that
can more effectively replace human workers without needing so much handling.
(16:00):
Now that being said, David au Tour of m I
T has actually expressed some skepticism that machines will take
over most jobs for the foreseeable future. And now he
doesn't say that it's uh, you know that we're always
going to have people in these jobs and that's that's
going to be the fact forever and ever, but rather
that this may be a longer timeline than what some
(16:21):
people might have you believe. He points out that we've
made machines that are really good at handling those repetitive,
predictable tasks, but they aren't, like we said, as good
at flexible ones, uh and responding to situations that have
dynamic conditions like a human interaction. So, in other words,
like if you've ever had to call into any kind
(16:41):
of of tech support where you get the automated menus.
The automated menus are really good at channeling you to
the specific person who can help you. They're not so
good at being able to handle the actual problem. I
don't know if they're always even as good at the
first thing you make. And I've been like, none of
(17:02):
the options you just listed are what I want. Well,
that that's that is again another limitation on the system, though, right.
I mean, if it were a person that you were
talking to, they could at least respond by saying, oh, well,
then the best place to funnel your problem is to
this person. Uh So that's kind of his point is
saying that, you know, machines are not that great at
(17:24):
those kind of tasks, and there's no reason to believe
that artificial intelligence is going to reach a point that
will make them comparable to human performance in terms of
things like problem solving in common sense, Yeah, common sense,
critical thinking, that kind of thing. That that sort of
stuff is largely going to be in the human domain
for the foreseeable future. Not that machines will never have
(17:48):
a stab at this kind of thing, but that it's
not as uh, it's not as imminent as something like
the automation of routine like the dangerous dirty uh job
that a lot of of robotic experts talk about, you know,
these machines taking over for us. The experts in general
are really divided on how they think all of this
(18:11):
is going to play out. I mean, you know, it's
it's easy for us being relatively uh amateurish in our opinions,
and you know, you know, but but these people who
work in this industry every day can't really agree whether
this is leading to a utopia or a dystopia. It's
a coin flip. Well, yeah, it almost is. Because there
was a Pew survey released in augusteen that pulled almost
(18:33):
two thousand experts in relevant fields like robotics, or economics
or AI about the effects of robotics and AI on
our economies, and the results were that fifty two per
cent of these experts predicted a quote optimistic path, which
entailed quote a future in which robots and digital agents
do not displace more jobs than they create unquote, and
(18:57):
at the same time, forty percent of the x or
it's predicted a pessimistic outcome in which robots quote will
display significant numbers of both blue and white collar workers
and many of the experts expressed concern that this will
lead to vast increases in income inequality, masses of people
who are effectively unemployable, and breakdowns in the social order.
(19:21):
That's that's what we would call a a bad outcome. Yeah,
it would be chaotic, it would be uh and we'll
talk more about that kind of dystopian outcome in the
a little bit later in the episode. Yeah. Now, some people,
I guess would argue that it's not so stark that
a robot will either just completely do your job, like
(19:43):
replace you outright, but the more robots may sort of
enter the workforce in middle roles. You know what I mean.
This is that complementary approach to the idea that we
will have machines that will complement what we do. Yeah,
construction is a really good ample of how this is
working today because you know, we've got a lot of
machines that do the heavy lifting and the precision cutting
(20:06):
and stuff like that. But this this work does in
fact complement, not replace, skilled construction workers who can do
the planning and the physical control in the in the
moment judgments. Right, it's that flexibility we mentioned that the
workers themselves have that the robots don't have I mean,
a robot is really good at doing one thing over
and over, but it can't walk around the site and
(20:28):
you know, figure out what's the next job that somebody
needs help with. Right. Robot surgery is another great example.
The robot surgery tools are tools. They are extensions of
an actual human surgeon. The human surgeon uses a device
that allows him to or her to make control the incisions,
(20:49):
and you know, you have the robotics that are able
to translate those movements into actual actions against the patient.
So you could have larger movements being trans lay it
into very precise movements on the robotic scale. Yeah. Yeah,
Together the machine and the human can be more precise
than either would have been able to be alone. Right,
So the human can make human decisions, the robot can
(21:11):
translate motions into very precise movements, and you get the
best of both worlds. But again, it's it's complementary, it's
not substitution. Now, I guess the question is, but how
long will that be the case, Because as robots get
smarter and more agile and more flexible, they're going to
increasingly be able to do these things that Right now,
(21:34):
we're feeling like few only humans can do them. Well, yeah,
I mean, let's let's go back to the driverless cars
as an example. We talk about that in a positive sense,
about how the cars are able to sense changing conditions
much more quickly than humans are. So, for example, our
our human reaction time means that when something happens, there's
a delay between when we perceive it and when we
(21:55):
can take any action. That delay is much much shorter
for a robotic system that can react almost I mean
to us it seems instantly. Uh, they can react to
changing conditions. So when we look at the example of
the driverless cars, like Google's driverless cars, they've been involved
in two accidents, neither of which were caused by the car,
(22:17):
right and at least not the driverless car system. They
were both human error. I think one was when a
human operator was operating the car under manual control exactly. Yeah, yeah,
neither of them were the actual robotic system. So we
can already see that at least in that in that
kind of test scenario, because of course that's not a
wide rollout, but we can see within that controlled test
(22:40):
scenario that it appears the robots have the edge on
us in that field. Already, so it stands to reason
that we will see this continue in other disciplines over time. Obviously,
some are gonna take a lot longer than others, because
some tasks are more innately human than others. Yeah, So
I think a theme I'm seeing immer ridge and the
question of will robots take our jobs? Is the people
(23:03):
who are optimistic about it aren't usually saying no, robots
will not take most of our jobs. They're saying, yeah,
they will, but it will be okay for one reason
or another. Yeah, it seems to be most people agree. Yeah,
robots are going to take a huge portion of our
jobs that exist today, maybe eventually all of them. And
(23:23):
so if they do take our jobs, the next question
is will this necessarily be a bad thing? And I
want to revisit or earlier I mentioned that Wired article
from twelve by Kevin kelly Um and his whole point
is that though robots and automation might eliminate old jobs,
they will create new jobs, and the new jobs will
(23:43):
be better jobs. So I want to read a quote
from part of his article that I think sums this
up pretty well. He says, in the coming years, robot
driven cars and trucks will become ubiquitous. This automation will
spawn the new human occupation of trip optimizer, a person
who tweaks the traffic system for optimal energy and time usage.
(24:03):
Routine robo surgery will necessitate new skills of keeping machines sterile.
When automatic self tracking of all your activities becomes a
normal thing to do, a new breed of professional analysts
will arise to help you make sense of the data.
And of course we will need a whole army of
robot nanny's dedicated to keeping your personal bots up and running.
(24:25):
Each of these new vocations will in turn be taken
over by robots later. And so this sort of leads
to where he proposes there's like a seven stage cycle
of automation, and then automation anxiety, and then finally automation complacence,
where first it seems like a robot could never do
what you can do. Then you say, well, okay, it
(24:48):
can do some of what I can do, but it
can't do everything I can do. Then it says it
can do everything I can do, but it needs me
to take care of it when it messes up or
breaks down, which all the time. So this this would
be like the copy machine jamming yet again. Yeah. Then
he says, oh, well, okay, so it operates flawlessly on
(25:09):
the routine, but I still need to tell it what
to do. I need to train it for new tasks,
he says. Then after that, okay, can just have that job.
I didn't want it anyway because it's that job was
not a good job for humans to do. And then
the next stage is wow, quote wow, now that robots
are doing my old job, my new job is much
(25:31):
more fun and pays much more. And then finally I'm
so glad a robot and computer cannot possibly do my
new job, and so the whole thing begins again. This
actually reminds me also of what Dr Henrik Christensen said
when uh I did the the Um for Thinking video
episode from Georgia Tech and we talked about Robotics Week
(25:53):
and we looked at the various robots that they have
in their in their labs. We only saw a few
of them. They were really fascinating, and he talked very
much in the same kind of sense, the idea that
that robots are taking over the three DS, the dull,
dangerous and dirty jobs. And then uh, you know, we'll
see that continue and then we'll see it grow into
(26:15):
the next round. But each time it's like we're just
a little bit ahead, like you know, they they the
robots catch up to us, and then we find the
new thing to do. And people might ask, well, what's
the new thing to do? And the honest answer is,
we can't anticipate that. If you had looked back two
hundred years ago, before we got into the real industrial
(26:35):
Revolution and ask people, you know what, what do you foresee,
Like if you explain to them this job that you
have is not going to exist in another decade or so,
they wouldn't have been able to anticipate. Sure. And this
is also part of the problem with advancing technology and
robotics and AI, because it seems that the gap between
the creation of new jobs and the creation of robots
(26:58):
that can do them better than human it's closing or
or shortening at any rate. Yeah, that certainly could be
the case. In fact, in response to that article by
Kelly I was just talking about, Gary Marcus wrote an
interesting response in The New Yorker. It was also in December,
and Marcus argued that these new professions that Kelly claims
will be created by automation or will actually be taken
(27:20):
over by robots just as easily and just as quickly. Uh,
And of course Kelly says eventually they will be. But
I think the idea is that there's no reason to
think they'll lag behind all that long to be taken
up by humans for any significant period of time in
the meantime, you know. So as examples, he points out
that there's already such a thing as the robots sterilization
(27:41):
expert that uh, that you know, Kelly claimed would be
done by humans, and that there are already forms of
automated trip optimization. And this is a quote. With advances
in both hardware and software, the time between the invention
of a job and it's automated replacement is getting shorter, right,
So that we're getting our our machines are getting better faster,
(28:04):
and that eventually there's not going to be much of
a gap between when a new job is created and
when a robot can do it. Pretty well. Yeah, now,
this seems to me to be based upon the assumption
that this this will be a continuously accelerating trend, very
much the way we look at Moore's law. How Moore's
Law has meant that we see a doubling, effectively a
(28:26):
doubling and computer power every eighteen to twenty four months,
depending upon when you consult Moore's law. Um, And I
don't know that it's safe to make that assumption. Software
development is a different It takes a different pathway than uh,
the ability to cram more discrete components onto a square
inch of silicon wafer. But but I do see where
(28:50):
he's getting at. And I it's not that I have
the data right in front of me to to dismiss
it or to disagree it. Just something to me says
that we don't, hite have the evidence to prove that
this this trend we're seeing right now is going to
be continuous. I think that we're I think we'll continue
to see advances. I just don't know that it will
(29:11):
always be at the same accelerated rate. So it maybe
that we're in a golden age of of development right now,
But then things might hit a wall and slow down.
We don't we don't have a way of knowing. Maybe
they won't, maybe they will continue the way he has predicted.
But yeah, yeah, again, like like the incredible features, incredible
and unknowable. Um, so so either way, Uh, we have
(29:35):
a couple of options for for dealing with this potential situation. Right,
So we've been talking a lot in you know, kind
of hypotheticals. But let's say that you are genuinely a
person whose job is going to be eliminated because of automation.
Put yourself in this scenario. You are you come into
work one Monday morning and your boss says, I just
(29:57):
bought a robot that does exactly what you do. Yeah,
you are no longer needed here. We wish you well
on your future opportunities. Um. So, the the options are
really one of two things. You can either go and
look for another job you can do that isn't already
being automated by other devices, which uh, you know, depending
(30:21):
upon your education level, you would you might have some
limitations there. Or you go and pursue further education so
that you can get a better job one of the
one that has not yet been automated. Uh. Those are
really your two and most of the robotics experts that
I've seen talk about this issue, uh tend to say
that that's the path they hope people all take the
(30:44):
idea to to educate yourself to better yourself so that
you can end up getting a better and more fulfilling sure,
which is a lovely idea, except the part where you're
asking someone who's unemployed to somehow pay for education and
also food us get a new degree. It's one of
those things that's really easy to say because you're talking
(31:05):
about it, like, you know, the idea on paper, and
then when you start getting into okay, let's avoid the
whole paper subject. Let's talk about this as if it's
a real event. I've just lost my job, I no
longer have income, I still have outstanding UH bills to pay.
How do I do this thing? You're from? Ye, yes, yes,
(31:28):
some of us are. Some of us may still be
carrying those student loan debts. Um, yeah, it's it's you know,
when you get into those specifics, it gets to be
a real issue. But the thing to keep in mind
is that people on like robots, we are flexible. We
have that capability to switch gears. And then I made
a joke about if your mechanical robot, you might physically
(31:49):
be able to switch gears, but I'm talking the figurative
switching gears. We have the option of being able to say,
you know, this is not working out for me, or
the say is no longer an option for me. I
need to pursue something else. And while it's easy to
say that, I mean, obviously personal circumstances might limit what
(32:10):
your your real opportunities are, but that's still more opportunities
than a robot. A robot is built to do a
specific task. Uh. We don't have any general purpose robots
that can do anything. So and we probably well and
we I don't think we're going to within a century.
I honestly don't think we're going to get quite that far. Um.
(32:31):
I think we will get there, but I think it's
a harder problem than what a lot of the discussion
has kind of led up to. Yeah. Um. The aforementioned
David Autor of of m I T stresses how much
we human laborers need to be training for and supplying
tasks that will be complimented by automation, not substituted by it. Um.
(32:54):
He calls this Polanis paradox, after the mid twentieth century
philosopher Michael Polani, who said, we know more than we
can tell um. And to to to illustrate this to yeah, yeah,
to to fall back on kind of our favorite example
of machine learning here on this podcast, the cat identification problem. Okay,
(33:14):
any toddler can identify a picture of a cat or
most of them at any rate. Um, but it took
a network of sixteen thousand processors to figure out the
same thing in computer terms. Um, So what does the
toddler know that all of those computers had to learn?
And Okay, in this particular example, we've enumerated the answers
(33:35):
multiple times here on this podcast. Um but and it
was a lot a lot of things, was basically the answer.
But in a lot of other cases the answer is
we're not sure. Yeah, yeah, A lot of it has
comes down to just the fact that the wiring of
the brain is so different from the classical computer. And
we've talked about machine learning and neural networks and how
(33:56):
they are a fraction of the complexity of an actual brain.
I mean, they they work on the same principle, but
they are a tiny, tiny, you know, a minuscule example
how a brain works. Because to produce anything that would
operate on a brain's level would require a huge amount
of electricity and a lot of processors. But yeah, it's
(34:20):
it's that's something to keep in mind. So, so it's
a very optimistic view, or a relatively optimistic view compared
to some of the other that that what forty percent
of of naysayers out there who think that things are
going to be potentially terrible? Um, but is it a
possibility that eliminating these jobs for humans would be okay? Yeah,
(34:44):
that's another option we haven't so we've talked about. It
could be bad. It could lead to uh just massive unemployment,
social unrest, and the breakdown of society, dogs and cats
living together. It could be okay in that Well, we'll
just keep creating new jobs people. There will always be
jobs for us even as we replace the old ones.
What if it's the case that we replace all the jobs,
(35:08):
there are no jobs for people, and that's still okay.
So we've eliminated the need for labor. Could it be
possible that we live in a world where labor is
no longer necessary and we still are able to be people? Right,
So there's this idea that you can create more wealth
for everyone. You've probably heard this phrase make the pie higher,
(35:29):
al right, instead of making it bigger. Ringe. Well, okay,
so the idea goes like this, Yes, replacing human workers
with robots puts humans out of the job, but it
could also create just a starkly enormous amount of wealth. Uh,
the reason humans are being replaced by robots is that
(35:51):
the robots are more efficient. They do the job better,
they can do it faster, they can create more of something,
create more product or value you in a shorter amount
of time, and putting aside the cost of maintenance and procurement,
you don't actually have to pay the robots. They're they're
creating new wealth. So if this leads to societies that
(36:12):
are flush with surplus money and value, it could quote
make the pie higher. Everybody's little slice gets bigger without
taking any more from anybody else. Now, if that could happen,
that would be great, but that's not a guaranteed. That's
not something that we oh, sure, if we just eliminate
all the jobs and have lots of robots, everybody will
(36:33):
be happy. Because, for example, the economist Paul Krugman pointed
this out in a New York Times column in December,
where he said, quote smart machines may make higher GDP possible,
so higher gross domestic product, but also reduce the demand
for people, including smart people. So we could be looking
at a society that grows ever richer, but in which
(36:56):
all the gains in wealth accrue to whoever owned the robots.
So that yeah, and by a robot guys. Marcus also
pointed this out in his New Yorker piece, So what
do you do if you have a society that's creating
ridiculous amounts of prosperity but most people can't get work
(37:16):
of any kind or enjoy that prosperity at all. Yeah,
And so here's where I think we might earn some
hate mail, But we're going to have to say it
because I think it's the logical conclusion. Could it be
that advances in automation will make some really major form
of wellthree distribution or socialism necessary in the future if
we want to have a stable society. Yeah, I think
(37:38):
that's possible. And even if you're very, very opposed to
socialism currently, you might not be in this scenario because
I want to highlight a few things. People are usually
opposed to socialism because they believe, for one thing, it
discourages productivity. You know, if you are sure that you
can get help and that you always have a net
(37:59):
to fall act on, it gives you less incentive to
work hard at your job and create wealth. Well, this
doesn't really matter. If you have a robotic workforce, you
don't need an incentive to work harder. There's there's no
there's no where to work harder in the first place, right, right,
What about the argument that it's not fair to take
(38:19):
hard earned money from from workers. Yeah, I mean that
that could make sense to a lot of people today,
But at the same time, it doesn't really make sense
sense anymore if you're imagining a society where more than
of people literally cannot make money through labor, and the
society is replete with extra wealth, so there's just tons
(38:40):
and tons to go around, and most people have no
way of getting it themselves. Yeah, and then on top
of that, let's talk about a world where because we
might imagine a dystopian future in which you've got this
this tiny elite that owners robot owners, they control all
the wealth, and then everybody else if everybody else is
(39:01):
genuinely unemployed, as in there is no way for them
to earn income, then you have no consumers. You have
no customers. Know, when buying all of the products that
the robots are making so industriously right, So there's no
reason for robots to make anything because there's no buying
power to purchase the things. I mean, unless you're making
(39:21):
the three families that the roots trading stuff. So unless
those three families just want to see the rest of
the world whither away. It it makes it's it's not
a supportable system. It ultimately would crumble in on itself.
So at some point some sort of redistribution is absolutely
necessary because otherwise you have no consumption. Yeah, redistribution or
(39:44):
other ideas that make people uncomfortable in the same way,
like a sort of centrally planned economy. Uh, you know,
examples of which would be things like communism. But this
wouldn't be like communism. It would be the fact that well,
I guess we would call it something like we have
collected of ownership of our robot workforce, and the proceeds
(40:04):
of what these robots generate or split up equally among
everybody in the country. Yeah. One other issue that I
thought was interesting was the psychological burden that this place
is on people. The idea that you get some sense
of purpose from your job, and you know, I guess
that that depends upon each individual, right, I mean, it
(40:26):
all depends on who you are and what job you have.
I know, I get a lot of satisfaction out of
my job and I find a lot of purpose in it,
and the thought of not having that, certainly is one
of those where you start to question what would you
do to give yourself purpose? And this is another one
of those areas where I think we would say, you know,
we don't know, we're we're inventive people, and it's very
(40:48):
possible that we would have Uh. Each individual comes up
with his or her own purpose and becomes a you know,
something they define for him or herself, and uh, I
kind of like that idea. I don't know if that's
actually realistic or not. I mean, not being in that world,
it makes it very difficult for me to draw a conclusion. Well,
(41:09):
the only examples that we have of anything this this
stark is science fiction. I mean stuff like Star Trek,
for example, has this kind of utopian society in which
it's a it's a post post economic world at least
post scarcity. Right, we should do an episode all about
(41:31):
Star Trek and the economics. Guys. That should be the
very next episode we do. The Star Trek economy. I
totally agree, and it's really convenient because we already have
notes typed up for it. I guess that wraps up
robots for today. But join us next time where we're
actually going to tackle something that's been asked for multiple
(41:52):
times by fans. Uh, the star trek economy. Now does
it work? And can we put it in place? And
boy was that a fun one to research? So yeah,
this this was a really fascinating topic and I mean
it is one that clearly is important and obviously it
has has a real impact on real people. Uh. And
I think that there's still room for optimism. I I
(42:13):
kind of side with the of that of that Pew study,
but I think it's one of those things that we
do have to keep in mind in order for that
optimism to remain realistic. Absolutely. Yeah, if if we were
replaced by robotic podcasters, would you guys go all letite
on them? I mean, would you destroy those robots? Um?
I mean, to be fair, I've suspected Josh Clark of
(42:35):
being a robot for like the last five years. There
is no robot on earth that could equal the badness
of your puns. I that's true, job is saying that's
that is an extremely human feature. Yet I did make
the entire Have you ever read where the robots tried
to write jokes? They're so bad there not as bad
as yours. The editorial department as a whole groaned at
(42:57):
one of my puns last week. So that was a
proud day for me. People. It was it was great.
I don't remember what it was, but it was beautiful.
I remember specifically. I'll tell you after the podcast. So
we're gonna wrap this up, guys. If you have any
suggestions for future topics. I mean, this was a listener
suggestion and it was so much fun. If you have
a suggestion, send us an email our addresses f W
(43:20):
Thinking at how Stuff Works dot com, or drop us
a line on Facebook, Twitter or Google Plus. At Twitter
and Google Plus, we are f W Thinking at Facebook.
Just type in fw thinking and the search bar will
pop right up. Leave us a message, let's know what
you think, and we will talk to you again really soon.
(43:41):
For more on this topic in the future of technology,
I visit forward thinking dot com, brought to you by Toyota.
Let's go places
Brought to you by Toyota. Let's go places. Welcome to
Forward Thinking, Tather and welcome to Forward Thinking, the podcast
that looks at the future and says, take this bot
and shove it. I'm Jonathan Strickland, I'm Lauren Volga, and
(00:21):
I'm Joe McCormick. As you might have guessed from the
thing that just came out of Jonathan's mouth, today, we're
going to be talking about robots, robots and jobs. Yeah.
As it turns out, we've had quite a few listeners
and viewers of Forward Thinking ask us to cover this topic.
It's it's one that's had a lot of coverage in
the media, and so we're looking at robots and jobs
(00:44):
right well, specifically the question will robots steal our jobs? Right? Right? Yeah?
I mean, which is kind of a terrific headline, which
I think is part of why it's so popular in
the media. But but I mean it captures our imagination
in a very specific way, because I mean, we are
all seeing all of this autumn nation that we've never
seen before kind of come up and and influence our
(01:04):
workforce in very interesting ways. Yeah, not just in uh,
in the ways that have been traditionally depicted like in manufacturing,
you know, heavy manufacturing, but in ways that affect you know,
white colored jobs as well. And we're going to cover
all this kind of stuff. But uh, let's let's look
down memory lane, because Joe, you pulled up something fascinating,
(01:25):
something I had heard but never looked into, and you
really found some information that that taught me some stuff. Well,
I want to get to the core of what we're
talking about, which is not just robots in the sense
of the way we picture them, which is a thing
with legs and arms. You know, it's vaguely human basically, right,
I go with Robbie the robot, But that's that's classic. Generally,
(01:47):
we're talking about all automation, any any way in which
a machine can do a job that previously could have
only been done by a human or an animal. Maybe.
Uh so, how about the Luodites. You all know the Luodite.
It's not just a term we call each other when we, like,
you know, accidentally sent a text message to the wrong
person or something like that. It's actually a historical group.
(02:09):
And of course you know who they were, right, Well,
well I do now because you did the research, Joe. Well,
they hated machines, right, that's the that's the whole thing.
That's the way I the way I've always heard it
used is yeah, it's people who just dog on it
can't either grasp technology and therefore they hate it because
of that, or they just outright think that new stuff
is bad for some reason. I assumed it was people
(02:31):
who had some kind of aesthetic or religious opposition to
technology in general. And turns out I was being a
total nano rod because that is not what the Luddites were.
So the original Luddites were a movement of textile workers
in England in the early eighteen hundreds, and they were
not against technology in general. They were famous for like
(02:53):
burning factories and smashing factory machines, but not because they
believe machines were evil. In fact, they were more or
less a labor movement. Uh. They were probably workers who
used machines themselves in their jobs, and they arose from
conditions of economic trouble and unemployment in Great Britain that
was part of a depression during and following the Napoleonic
(03:15):
Wars in the early eighteen hundred. So they were seeking
better employment, more work, better wages, but part of the
problem they perceived with the labor market. Then it was
the so called labor saving devices and machines, which often
meant that the same manufacturing job that used to require
a skilled crafts person could be performed by a machine,
(03:38):
or for a lower wage, by an unskilled worker paired
with the machine. So in a kind of certain qualified
since machines took their jerbs and they weren't happy about it.
And you can understand why. We've heard the same story,
the the apocryphal story that sabotage comes from the sabo
thrown into the cogs of the machine. From this but
(03:59):
similar story easy like uh and we Joe, you and
I had had a brief conversation before we came in
to record the podcast. We were just kind of talking
about the subject, and we mentioned that this this complaint
was again about the loss and jobs, not necessarily technology.
If in fact the manufacturers had found a super cheap
source of labor, the same problem would have existed. It
(04:22):
wasn't just that it was a machine. Yeah, if you
could have just found people who would do the same
jobs cheaper and take those jobs away from these workers.
I found a really easy way to train horses to
do it. Are horses are terrible at weaving, but uh,
you know they they just like all manual dexterity. But
in economy in England, it would have had to be rabbits, right,
(04:45):
they have a factory, they would have factories of rabbits
weaving cloth. I think it would be hairs actually sorry,
but uh, in economics we call we call this this
process substitution, where we use a machine to substitute for uh,
human labor. Uh. And there's another idea called complimentarianism complimenting.
(05:06):
So we're gonna be talking a lot about substituting versus
complimenting in this podcast. But as long as humans have
been innovating, we've had this issue to some extent. I mean,
if you look at agriculture, and we'll talk a lot
about agriculture, uh, you know, just the advent of the
plow made a lot of of changes because now you
(05:26):
didn't need as many human workers howing the fields as
you did before. And so this is not a new
idea by any extent. It goes back, you know, centuries,
but we see it uh accelerated in the era of
technological advances and development. Yeah, and so I think a
(05:47):
good place to start with the most recent modern incarnation
of this worry that that machines, automation, robots will take
our jobs is this pretty optimistic article that Kevin Kelly
wrote for Why, aired in December, is called better than
Human Why Robots will and Must take Our Jobs? Um,
(06:07):
and Kelly says this, two hundred years ago, seventy percent
of American workers were farmers. By about a hundred years
after that, it was some yeah, I read in another
article entirely, but I wanted to toss that in. Yeah,
that that that that particular term that two hundred years
ago seventy of American workers were farmers showed up a
(06:27):
lot in our research. Joe did an amazing job of
gathering tons of articles on this subject, and I must
have read that one statistic at least six or seven times.
The people on the internet sometimes use the same sources,
it might be or each other. Well, it's the startling
figure because apparently, according to Kelly, only one percent of
(06:49):
those previous farming jobs remain today, and that's because of automation.
So automation all of these jobs that used to require
humans or animals to be done on land have pretty
much all been replaced by machines now. But but Joe,
what happened to all those people who formerly had been
working on farms? Now they have other jobs, or I
(07:12):
mean some of them might be unemployed, but the ones
who are employed have found other jobs to do, either
other jobs using the equipment that has created automation on farms.
So now they can drive a tractor or or or
help marator build a tractor, do maintenance on the chicken
milking machine whatever it is. Yeah, or maybe they work
(07:34):
in it, or maybe maybe moved to a city and yeah,
I did something else. So in other words, we see
job creation as well some innovation and uh which again
necessity mother of invention, right, sure, But Kelly goes on
from that to claim that over the next century, So
in the next like ninety years, the same trend will continue,
(07:55):
seventy of today's jobs will be replaced by automation. And
if that, obviously that's just a speculation which we can't
know for sure. But if that's true, what does that
mean for the world? Right? Will we continue to see
the same sort of of of progression where new jobs
get created that are totally either dependent upon the fact
(08:19):
that we have robots or or just totally new that
people then will enter into. And I mean it's it's
a very exciting thing to think about, but also a
little bit terrifying. Yeah, sure, right, Well, nobody wants to
lose their job. And your job, your job is you
in many ways. It's a huge part of your identity,
(08:39):
and it's how you make your money, which is how
you do everything else that isn't your job exactly right,
I mean, without your job, you're in big trouble. So
we all like to think that our jobs can't be
replaced by robots. There is no way a computer program
or an autonomous robot or some other kind of machine
could do my job, right right, Well, I don't know
(09:03):
about that. There are some people who have made I think,
pretty convincing cases that yes, your job will be replaced
by a robot almost no matter what you do. The
chances are, whatever you do, it's probably going to be
done by a machine before too long. Now, the before
too long does require a little more examination because some
(09:24):
some jobs are more easily automated than others. Right, So,
so jobs, for example, that have a repetitive action that
must be done over and over, those are prime candidates
to be replaced by a computer or by a machine,
and we've seen that happen already things like Amazon's warehouses,
(09:46):
where they're depending more and more heavily upon robots, including
robotic shelves that will zoom over to the packaging line
whenever someone orders a specific item and then zoom back
to where they were supposed to be, so instead of
a worker going out to retrieve something from a shelf,
the shelf comes to the worker. Right. On the other hand,
(10:08):
jobs that require a lot of flexibility, where you're constantly
moving from different kinds of tasks to other ones, those
are going to be harder to automate, right, But we
shouldn't be able to count on them being indefinitely difficult
to automate, or at least to divide up among many
other automated machines that can do the different parts of
the tasks. Um, So I think we should look first
(10:30):
at h There was a book that came out called
Race Against the Machine by Eric Brynjolfson and Andrew McAfee,
and they have made the case that, yeah, technology is
very much going to be replacing jobs. Now. They might
have a spin on it, says that's not necessarily a
bad thing. But what's their case. Well, you know, they
(10:51):
do say the sort of things we've been talking about already,
that technology has always replaced jobs and that this is
something that we've seen throughout history. But they claim that also,
you know, this current era we're in is a little
different from others uh that we've seen. We're seeing more
of the replacement and more automation and uh quote less
(11:13):
of the complimenting and creating of new jobs end quote.
So in other words, there was an era early in
the two thousands that were still in where we saw
a dip in the number of jobs that were available,
but an increase in productivity, which was kind of unprecedented,
where where we didn't see the jobs rebound, we saw
(11:35):
the productivity hold steady, but we didn't see new jobs
being created. And there's a potential that this could be uh,
you know, a long term trend, not something that is
a little you know, just blip, yeah. Yeah. Andrew McAfee
gave a ted X talk in Boston in June where
he showed, as part of his presentation the data on
(11:58):
the Great Recession where where it seemed okay as things
started getting better towards the end of the Great Recession. Uh,
we were seeing GDP come back up, we were seeing
profits come back up. It looked like from a zoomed
out level that the economy was doing better again, except
the number of people employed did not rebound at the
(12:19):
same time. Uh. So you may be experiencing a situation
here where the economy as a whole might be doing fine,
but lots of people aren't. Yeah, and well, it also
depends on how you're defining how well the economy does.
If you if you're talking about total wealth generated, right,
if you're looking at it as a measurement of the
number of people employed or percentage of employment, that it's
(12:43):
not doing so well. Right, right, Well, I mean there's
this is a multi factor issue, of course, partially related
to how hard you can convince people to work, uh
for a lower amount of money. But yeah, yeah, certainly, Uh,
and that's got to compete with automation also, right, I mean,
and like, would it be cheaper to hire somebody to
(13:03):
work for a really low wage or to just pay
up for the robots to do it for us? You know,
it's a calculated decision that the the employer must make.
Can you compete with increasingly efficient automation. That's a tough decision.
But anyway, Uh so McAfee said pretty much, Yes, the
droids are definitely coming for our jobs. Uh even the
(13:25):
jobs of knowledge workers, he claims. So it's not just
the three point five million truck drivers in the United
States should be worried about the Google autonomous car. They should,
but it's also that, for example, finance writers should have
their eye on algorithms that can already write perfect journalistic
reports on stock performance based off of the daily data
(13:47):
that comes into You can send this program some numbers
and it will generate a completely perfect report on the
stock performance in English that can be read by humans.
There are plenty of other examples of this too. There are,
in fact, lots of trading algorithms that that stock traders
are depending more and more heavily upon. In fact, we
(14:08):
talked about that in the previous episode. Yeah. Yeah, it
used to be people crunching numbers, and now it's computers
crunching numbers way better than the people really could have. Now,
I don't want to give the wrong impression about McAfee,
because he's revealed in other places and uh, he's basically
an optimist about the overall implications of technology for human existence.
You know, we can do good things with it, but
(14:29):
they're there are different paths that we could take from here,
and some of them might not be good, right, uh,
And almost all of them are going to involve the
droids taking our jobs. That's that's going to be hard
to escape at this point. Right. Again, it's a question
of time scales, right right. And also the thing where
in here in in in America are jobs have not
(14:53):
really increased in UH an alignment with automation. But that's
not true for all countries, is it. No, Well, for example,
the i f R, the International Federation of Robotics, they
found a piece where they were drawing attention to a
study showing that in some countries rates of employment grew
alongside increased use of industrial robots. But then at the
(15:16):
same time i I I found an interesting observation made by
a piece UH in April in the m I T
Technology Review, where they pointed out this study cited by
the International Federation of Robotics, which by the way, is
a trade organization for robotics, so they sort of have
ah UM pointed out that this study they're referring to
(15:37):
is talking about dangerous and stupid robots that are not
very versatile, like the ones that are cutting out our
car bits, maybe not the ones that are potentially handling
our children exactly right, So the effect could be very
different when a smarter, next generation robot shows up that
can more effectively replace human workers without needing so much handling.
(16:00):
Now that being said, David au Tour of m I
T has actually expressed some skepticism that machines will take
over most jobs for the foreseeable future. And now he
doesn't say that it's uh, you know that we're always
going to have people in these jobs and that's that's
going to be the fact forever and ever, but rather
that this may be a longer timeline than what some
(16:21):
people might have you believe. He points out that we've
made machines that are really good at handling those repetitive,
predictable tasks, but they aren't, like we said, as good
at flexible ones, uh and responding to situations that have
dynamic conditions like a human interaction. So, in other words,
like if you've ever had to call into any kind
(16:41):
of of tech support where you get the automated menus.
The automated menus are really good at channeling you to
the specific person who can help you. They're not so
good at being able to handle the actual problem. I
don't know if they're always even as good at the
first thing you make. And I've been like, none of
(17:02):
the options you just listed are what I want. Well,
that that's that is again another limitation on the system, though, right.
I mean, if it were a person that you were
talking to, they could at least respond by saying, oh, well,
then the best place to funnel your problem is to
this person. Uh So that's kind of his point is
saying that, you know, machines are not that great at
(17:24):
those kind of tasks, and there's no reason to believe
that artificial intelligence is going to reach a point that
will make them comparable to human performance in terms of
things like problem solving in common sense, Yeah, common sense,
critical thinking, that kind of thing. That that sort of
stuff is largely going to be in the human domain
for the foreseeable future. Not that machines will never have
(17:48):
a stab at this kind of thing, but that it's
not as uh, it's not as imminent as something like
the automation of routine like the dangerous dirty uh job
that a lot of of robotic experts talk about, you know,
these machines taking over for us. The experts in general
are really divided on how they think all of this
(18:11):
is going to play out. I mean, you know, it's
it's easy for us being relatively uh amateurish in our opinions,
and you know, you know, but but these people who
work in this industry every day can't really agree whether
this is leading to a utopia or a dystopia. It's
a coin flip. Well, yeah, it almost is. Because there
was a Pew survey released in augusteen that pulled almost
(18:33):
two thousand experts in relevant fields like robotics, or economics
or AI about the effects of robotics and AI on
our economies, and the results were that fifty two per
cent of these experts predicted a quote optimistic path, which
entailed quote a future in which robots and digital agents
do not displace more jobs than they create unquote, and
(18:57):
at the same time, forty percent of the x or
it's predicted a pessimistic outcome in which robots quote will
display significant numbers of both blue and white collar workers
and many of the experts expressed concern that this will
lead to vast increases in income inequality, masses of people
who are effectively unemployable, and breakdowns in the social order.
(19:21):
That's that's what we would call a a bad outcome. Yeah,
it would be chaotic, it would be uh and we'll
talk more about that kind of dystopian outcome in the
a little bit later in the episode. Yeah. Now, some people,
I guess would argue that it's not so stark that
a robot will either just completely do your job, like
(19:43):
replace you outright, but the more robots may sort of
enter the workforce in middle roles. You know what I mean.
This is that complementary approach to the idea that we
will have machines that will complement what we do. Yeah,
construction is a really good ample of how this is
working today because you know, we've got a lot of
machines that do the heavy lifting and the precision cutting
(20:06):
and stuff like that. But this this work does in
fact complement, not replace, skilled construction workers who can do
the planning and the physical control in the in the
moment judgments. Right, it's that flexibility we mentioned that the
workers themselves have that the robots don't have I mean,
a robot is really good at doing one thing over
and over, but it can't walk around the site and
(20:28):
you know, figure out what's the next job that somebody
needs help with. Right. Robot surgery is another great example.
The robot surgery tools are tools. They are extensions of
an actual human surgeon. The human surgeon uses a device
that allows him to or her to make control the incisions,
(20:49):
and you know, you have the robotics that are able
to translate those movements into actual actions against the patient.
So you could have larger movements being trans lay it
into very precise movements on the robotic scale. Yeah. Yeah,
Together the machine and the human can be more precise
than either would have been able to be alone. Right,
So the human can make human decisions, the robot can
(21:11):
translate motions into very precise movements, and you get the
best of both worlds. But again, it's it's complementary, it's
not substitution. Now, I guess the question is, but how
long will that be the case, Because as robots get
smarter and more agile and more flexible, they're going to
increasingly be able to do these things that Right now,
(21:34):
we're feeling like few only humans can do them. Well, yeah,
I mean, let's let's go back to the driverless cars
as an example. We talk about that in a positive sense,
about how the cars are able to sense changing conditions
much more quickly than humans are. So, for example, our
our human reaction time means that when something happens, there's
a delay between when we perceive it and when we
(21:55):
can take any action. That delay is much much shorter
for a robotic system that can react almost I mean
to us it seems instantly. Uh, they can react to
changing conditions. So when we look at the example of
the driverless cars, like Google's driverless cars, they've been involved
in two accidents, neither of which were caused by the car,
(22:17):
right and at least not the driverless car system. They
were both human error. I think one was when a
human operator was operating the car under manual control exactly. Yeah, yeah,
neither of them were the actual robotic system. So we
can already see that at least in that in that
kind of test scenario, because of course that's not a
wide rollout, but we can see within that controlled test
(22:40):
scenario that it appears the robots have the edge on
us in that field. Already, so it stands to reason
that we will see this continue in other disciplines over time. Obviously,
some are gonna take a lot longer than others, because
some tasks are more innately human than others. Yeah, So
I think a theme I'm seeing immer ridge and the
question of will robots take our jobs? Is the people
(23:03):
who are optimistic about it aren't usually saying no, robots
will not take most of our jobs. They're saying, yeah,
they will, but it will be okay for one reason
or another. Yeah, it seems to be most people agree. Yeah,
robots are going to take a huge portion of our
jobs that exist today, maybe eventually all of them. And
(23:23):
so if they do take our jobs, the next question
is will this necessarily be a bad thing? And I
want to revisit or earlier I mentioned that Wired article
from twelve by Kevin kelly Um and his whole point
is that though robots and automation might eliminate old jobs,
they will create new jobs, and the new jobs will
(23:43):
be better jobs. So I want to read a quote
from part of his article that I think sums this
up pretty well. He says, in the coming years, robot
driven cars and trucks will become ubiquitous. This automation will
spawn the new human occupation of trip optimizer, a person
who tweaks the traffic system for optimal energy and time usage.
(24:03):
Routine robo surgery will necessitate new skills of keeping machines sterile.
When automatic self tracking of all your activities becomes a
normal thing to do, a new breed of professional analysts
will arise to help you make sense of the data.
And of course we will need a whole army of
robot nanny's dedicated to keeping your personal bots up and running.
(24:25):
Each of these new vocations will in turn be taken
over by robots later. And so this sort of leads
to where he proposes there's like a seven stage cycle
of automation, and then automation anxiety, and then finally automation complacence,
where first it seems like a robot could never do
what you can do. Then you say, well, okay, it
(24:48):
can do some of what I can do, but it
can't do everything I can do. Then it says it
can do everything I can do, but it needs me
to take care of it when it messes up or
breaks down, which all the time. So this this would
be like the copy machine jamming yet again. Yeah. Then
he says, oh, well, okay, so it operates flawlessly on
(25:09):
the routine, but I still need to tell it what
to do. I need to train it for new tasks,
he says. Then after that, okay, can just have that job.
I didn't want it anyway because it's that job was
not a good job for humans to do. And then
the next stage is wow, quote wow, now that robots
are doing my old job, my new job is much
(25:31):
more fun and pays much more. And then finally I'm
so glad a robot and computer cannot possibly do my
new job, and so the whole thing begins again. This
actually reminds me also of what Dr Henrik Christensen said
when uh I did the the Um for Thinking video
episode from Georgia Tech and we talked about Robotics Week
(25:53):
and we looked at the various robots that they have
in their in their labs. We only saw a few
of them. They were really fascinating, and he talked very
much in the same kind of sense, the idea that
that robots are taking over the three DS, the dull,
dangerous and dirty jobs. And then uh, you know, we'll
see that continue and then we'll see it grow into
(26:15):
the next round. But each time it's like we're just
a little bit ahead, like you know, they they the
robots catch up to us, and then we find the
new thing to do. And people might ask, well, what's
the new thing to do? And the honest answer is,
we can't anticipate that. If you had looked back two
hundred years ago, before we got into the real industrial
(26:35):
Revolution and ask people, you know what, what do you foresee,
Like if you explain to them this job that you
have is not going to exist in another decade or so,
they wouldn't have been able to anticipate. Sure. And this
is also part of the problem with advancing technology and
robotics and AI, because it seems that the gap between
the creation of new jobs and the creation of robots
(26:58):
that can do them better than human it's closing or
or shortening at any rate. Yeah, that certainly could be
the case. In fact, in response to that article by
Kelly I was just talking about, Gary Marcus wrote an
interesting response in The New Yorker. It was also in December,
and Marcus argued that these new professions that Kelly claims
will be created by automation or will actually be taken
(27:20):
over by robots just as easily and just as quickly. Uh,
And of course Kelly says eventually they will be. But
I think the idea is that there's no reason to
think they'll lag behind all that long to be taken
up by humans for any significant period of time in
the meantime, you know. So as examples, he points out
that there's already such a thing as the robots sterilization
(27:41):
expert that uh, that you know, Kelly claimed would be
done by humans, and that there are already forms of
automated trip optimization. And this is a quote. With advances
in both hardware and software, the time between the invention
of a job and it's automated replacement is getting shorter, right,
So that we're getting our our machines are getting better faster,
(28:04):
and that eventually there's not going to be much of
a gap between when a new job is created and
when a robot can do it. Pretty well. Yeah, now,
this seems to me to be based upon the assumption
that this this will be a continuously accelerating trend, very
much the way we look at Moore's law. How Moore's
Law has meant that we see a doubling, effectively a
(28:26):
doubling and computer power every eighteen to twenty four months,
depending upon when you consult Moore's law. Um, And I
don't know that it's safe to make that assumption. Software
development is a different It takes a different pathway than uh,
the ability to cram more discrete components onto a square
inch of silicon wafer. But but I do see where
(28:50):
he's getting at. And I it's not that I have
the data right in front of me to to dismiss
it or to disagree it. Just something to me says
that we don't, hite have the evidence to prove that
this this trend we're seeing right now is going to
be continuous. I think that we're I think we'll continue
to see advances. I just don't know that it will
(29:11):
always be at the same accelerated rate. So it maybe
that we're in a golden age of of development right now,
But then things might hit a wall and slow down.
We don't we don't have a way of knowing. Maybe
they won't, maybe they will continue the way he has predicted.
But yeah, yeah, again, like like the incredible features, incredible
and unknowable. Um, so so either way, Uh, we have
(29:35):
a couple of options for for dealing with this potential situation. Right,
So we've been talking a lot in you know, kind
of hypotheticals. But let's say that you are genuinely a
person whose job is going to be eliminated because of automation.
Put yourself in this scenario. You are you come into
work one Monday morning and your boss says, I just
(29:57):
bought a robot that does exactly what you do. Yeah,
you are no longer needed here. We wish you well
on your future opportunities. Um. So, the the options are
really one of two things. You can either go and
look for another job you can do that isn't already
being automated by other devices, which uh, you know, depending
(30:21):
upon your education level, you would you might have some
limitations there. Or you go and pursue further education so
that you can get a better job one of the
one that has not yet been automated. Uh. Those are
really your two and most of the robotics experts that
I've seen talk about this issue, uh tend to say
that that's the path they hope people all take the
(30:44):
idea to to educate yourself to better yourself so that
you can end up getting a better and more fulfilling sure,
which is a lovely idea, except the part where you're
asking someone who's unemployed to somehow pay for education and
also food us get a new degree. It's one of
those things that's really easy to say because you're talking
(31:05):
about it, like, you know, the idea on paper, and
then when you start getting into okay, let's avoid the
whole paper subject. Let's talk about this as if it's
a real event. I've just lost my job, I no
longer have income, I still have outstanding UH bills to pay.
How do I do this thing? You're from? Ye, yes, yes,
(31:28):
some of us are. Some of us may still be
carrying those student loan debts. Um, yeah, it's it's you know,
when you get into those specifics, it gets to be
a real issue. But the thing to keep in mind
is that people on like robots, we are flexible. We
have that capability to switch gears. And then I made
a joke about if your mechanical robot, you might physically
(31:49):
be able to switch gears, but I'm talking the figurative
switching gears. We have the option of being able to say,
you know, this is not working out for me, or
the say is no longer an option for me. I
need to pursue something else. And while it's easy to
say that, I mean, obviously personal circumstances might limit what
(32:10):
your your real opportunities are, but that's still more opportunities
than a robot. A robot is built to do a
specific task. Uh. We don't have any general purpose robots
that can do anything. So and we probably well and
we I don't think we're going to within a century.
I honestly don't think we're going to get quite that far. Um.
(32:31):
I think we will get there, but I think it's
a harder problem than what a lot of the discussion
has kind of led up to. Yeah. Um. The aforementioned
David Autor of of m I T stresses how much
we human laborers need to be training for and supplying
tasks that will be complimented by automation, not substituted by it. Um.
(32:54):
He calls this Polanis paradox, after the mid twentieth century
philosopher Michael Polani, who said, we know more than we
can tell um. And to to to illustrate this to yeah, yeah,
to to fall back on kind of our favorite example
of machine learning here on this podcast, the cat identification problem. Okay,
(33:14):
any toddler can identify a picture of a cat or
most of them at any rate. Um, but it took
a network of sixteen thousand processors to figure out the
same thing in computer terms. Um, So what does the
toddler know that all of those computers had to learn?
And Okay, in this particular example, we've enumerated the answers
(33:35):
multiple times here on this podcast. Um but and it
was a lot a lot of things, was basically the answer.
But in a lot of other cases the answer is
we're not sure. Yeah, yeah, A lot of it has
comes down to just the fact that the wiring of
the brain is so different from the classical computer. And
we've talked about machine learning and neural networks and how
(33:56):
they are a fraction of the complexity of an actual brain.
I mean, they they work on the same principle, but
they are a tiny, tiny, you know, a minuscule example
how a brain works. Because to produce anything that would
operate on a brain's level would require a huge amount
of electricity and a lot of processors. But yeah, it's
(34:20):
it's that's something to keep in mind. So, so it's
a very optimistic view, or a relatively optimistic view compared
to some of the other that that what forty percent
of of naysayers out there who think that things are
going to be potentially terrible? Um, but is it a
possibility that eliminating these jobs for humans would be okay? Yeah,
(34:44):
that's another option we haven't so we've talked about. It
could be bad. It could lead to uh just massive unemployment,
social unrest, and the breakdown of society, dogs and cats
living together. It could be okay in that Well, we'll
just keep creating new jobs people. There will always be
jobs for us even as we replace the old ones.
What if it's the case that we replace all the jobs,
(35:08):
there are no jobs for people, and that's still okay.
So we've eliminated the need for labor. Could it be
possible that we live in a world where labor is
no longer necessary and we still are able to be people? Right,
So there's this idea that you can create more wealth
for everyone. You've probably heard this phrase make the pie higher,
(35:29):
al right, instead of making it bigger. Ringe. Well, okay,
so the idea goes like this, Yes, replacing human workers
with robots puts humans out of the job, but it
could also create just a starkly enormous amount of wealth. Uh,
the reason humans are being replaced by robots is that
(35:51):
the robots are more efficient. They do the job better,
they can do it faster, they can create more of something,
create more product or value you in a shorter amount
of time, and putting aside the cost of maintenance and procurement,
you don't actually have to pay the robots. They're they're
creating new wealth. So if this leads to societies that
(36:12):
are flush with surplus money and value, it could quote
make the pie higher. Everybody's little slice gets bigger without
taking any more from anybody else. Now, if that could happen,
that would be great, but that's not a guaranteed. That's
not something that we oh, sure, if we just eliminate
all the jobs and have lots of robots, everybody will
(36:33):
be happy. Because, for example, the economist Paul Krugman pointed
this out in a New York Times column in December,
where he said, quote smart machines may make higher GDP possible,
so higher gross domestic product, but also reduce the demand
for people, including smart people. So we could be looking
at a society that grows ever richer, but in which
(36:56):
all the gains in wealth accrue to whoever owned the robots.
So that yeah, and by a robot guys. Marcus also
pointed this out in his New Yorker piece, So what
do you do if you have a society that's creating
ridiculous amounts of prosperity but most people can't get work
(37:16):
of any kind or enjoy that prosperity at all. Yeah,
And so here's where I think we might earn some
hate mail, But we're going to have to say it
because I think it's the logical conclusion. Could it be
that advances in automation will make some really major form
of wellthree distribution or socialism necessary in the future if
we want to have a stable society. Yeah, I think
(37:38):
that's possible. And even if you're very, very opposed to
socialism currently, you might not be in this scenario because
I want to highlight a few things. People are usually
opposed to socialism because they believe, for one thing, it
discourages productivity. You know, if you are sure that you
can get help and that you always have a net
(37:59):
to fall act on, it gives you less incentive to
work hard at your job and create wealth. Well, this
doesn't really matter. If you have a robotic workforce, you
don't need an incentive to work harder. There's there's no
there's no where to work harder in the first place, right, right,
What about the argument that it's not fair to take
(38:19):
hard earned money from from workers. Yeah, I mean that
that could make sense to a lot of people today,
But at the same time, it doesn't really make sense
sense anymore if you're imagining a society where more than
of people literally cannot make money through labor, and the
society is replete with extra wealth, so there's just tons
(38:40):
and tons to go around, and most people have no
way of getting it themselves. Yeah, and then on top
of that, let's talk about a world where because we
might imagine a dystopian future in which you've got this
this tiny elite that owners robot owners, they control all
the wealth, and then everybody else if everybody else is
(39:01):
genuinely unemployed, as in there is no way for them
to earn income, then you have no consumers. You have
no customers. Know, when buying all of the products that
the robots are making so industriously right, So there's no
reason for robots to make anything because there's no buying
power to purchase the things. I mean, unless you're making
(39:21):
the three families that the roots trading stuff. So unless
those three families just want to see the rest of
the world whither away. It it makes it's it's not
a supportable system. It ultimately would crumble in on itself.
So at some point some sort of redistribution is absolutely
necessary because otherwise you have no consumption. Yeah, redistribution or
(39:44):
other ideas that make people uncomfortable in the same way,
like a sort of centrally planned economy. Uh, you know,
examples of which would be things like communism. But this
wouldn't be like communism. It would be the fact that well,
I guess we would call it something like we have
collected of ownership of our robot workforce, and the proceeds
(40:04):
of what these robots generate or split up equally among
everybody in the country. Yeah. One other issue that I
thought was interesting was the psychological burden that this place
is on people. The idea that you get some sense
of purpose from your job, and you know, I guess
that that depends upon each individual, right, I mean, it
(40:26):
all depends on who you are and what job you have.
I know, I get a lot of satisfaction out of
my job and I find a lot of purpose in it,
and the thought of not having that, certainly is one
of those where you start to question what would you
do to give yourself purpose? And this is another one
of those areas where I think we would say, you know,
we don't know, we're we're inventive people, and it's very
(40:48):
possible that we would have Uh. Each individual comes up
with his or her own purpose and becomes a you know,
something they define for him or herself, and uh, I
kind of like that idea. I don't know if that's
actually realistic or not. I mean, not being in that world,
it makes it very difficult for me to draw a conclusion. Well,
(41:09):
the only examples that we have of anything this this
stark is science fiction. I mean stuff like Star Trek,
for example, has this kind of utopian society in which
it's a it's a post post economic world at least
post scarcity. Right, we should do an episode all about
(41:31):
Star Trek and the economics. Guys. That should be the
very next episode we do. The Star Trek economy. I
totally agree, and it's really convenient because we already have
notes typed up for it. I guess that wraps up
robots for today. But join us next time where we're
actually going to tackle something that's been asked for multiple
(41:52):
times by fans. Uh, the star trek economy. Now does
it work? And can we put it in place? And
boy was that a fun one to research? So yeah,
this this was a really fascinating topic and I mean
it is one that clearly is important and obviously it
has has a real impact on real people. Uh. And
I think that there's still room for optimism. I I
(42:13):
kind of side with the of that of that Pew study,
but I think it's one of those things that we
do have to keep in mind in order for that
optimism to remain realistic. Absolutely. Yeah, if if we were
replaced by robotic podcasters, would you guys go all letite
on them? I mean, would you destroy those robots? Um?
I mean, to be fair, I've suspected Josh Clark of
(42:35):
being a robot for like the last five years. There
is no robot on earth that could equal the badness
of your puns. I that's true, job is saying that's
that is an extremely human feature. Yet I did make
the entire Have you ever read where the robots tried
to write jokes? They're so bad there not as bad
as yours. The editorial department as a whole groaned at
(42:57):
one of my puns last week. So that was a
proud day for me. People. It was it was great.
I don't remember what it was, but it was beautiful.
I remember specifically. I'll tell you after the podcast. So
we're gonna wrap this up, guys. If you have any
suggestions for future topics. I mean, this was a listener
suggestion and it was so much fun. If you have
a suggestion, send us an email our addresses f W
(43:20):
Thinking at how Stuff Works dot com, or drop us
a line on Facebook, Twitter or Google Plus. At Twitter
and Google Plus, we are f W Thinking at Facebook.
Just type in fw thinking and the search bar will
pop right up. Leave us a message, let's know what
you think, and we will talk to you again really soon.
(43:41):
For more on this topic in the future of technology,
I visit forward thinking dot com, brought to you by Toyota.
Let's go places
Fw:Thinking News
Hosts And Creators
Jonathan Strickland
Joe McCormick
Lauren Vogelbaum
Popular Podcasts
On Purpose with Jay Shetty
I’m Jay Shetty host of On Purpose the worlds #1 Mental Health podcast and I’m so grateful you found us. I started this podcast 5 years ago to invite you into conversations and workshops that are designed to help make you happier, healthier and more healed. I believe that when you (yes you) feel seen, heard and understood you’re able to deal with relationship struggles, work challenges and life’s ups and downs with more ease and grace. I interview experts, celebrities, thought leaders and athletes so that we can grow our mindset, build better habits and uncover a side of them we’ve never seen before. New episodes every Monday and Friday. Your support means the world to me and I don’t take it for granted — click the follow button and leave a review to help us spread the love with On Purpose. I can’t wait for you to listen to your first or 500th episode!
Las Culturistas with Matt Rogers and Bowen Yang
Ding dong! Join your culture consultants, Matt Rogers and Bowen Yang, on an unforgettable journey into the beating heart of CULTURE. Alongside sizzling special guests, they GET INTO the hottest pop-culture moments of the day and the formative cultural experiences that turned them into Culturistas. Produced by the Big Money Players Network and iHeartRadio.
Crime Junkie
Does hearing about a true crime case always leave you scouring the internet for the truth behind the story? Dive into your next mystery with Crime Junkie. Every Monday, join your host Ashley Flowers as she unravels all the details of infamous and underreported true crime cases with her best friend Brit Prawat. From cold cases to missing persons and heroes in our community who seek justice, Crime Junkie is your destination for theories and stories you won’t hear anywhere else. Whether you're a seasoned true crime enthusiast or new to the genre, you'll find yourself on the edge of your seat awaiting a new episode every Monday. If you can never get enough true crime... Congratulations, you’ve found your people. Follow to join a community of Crime Junkies! Crime Junkie is presented by audiochuck Media Company.