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Jobs
How technology is changing the world
Humans Need Not Apply
Burgess COMMENTARY
Peter Burgess
Humans Need Not Apply
https://youtu.be/7Pq-S557XQU
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0:03Every human used to have to hunt or gather to survive. But humans are smart-ly lazy so
0:08we made tools to make our work easier. From sticks, to plows to tractors we’ve gone
0:13from everyone needing to make food to, modern agriculture with almost no one needing to
0:18make food — and yet we still have abundance.
0:20Of course, it’s not just farming, it’s everything. We’ve spent the last several
0:24thousand years building tools to reduce physical labor of all kinds. These are mechanical muscles
0:29— stronger, more reliable, and more tireless than human muscles could ever be.
0:34And that's a good thing. Replacing human labor with mechanical muscles frees people to specialize
0:38and that leaves everyone better off even though still doing physical labor. This is how economies
0:44grow and standards of living rise.
0:46Some people have specialized to be programmers and engineers whose job is to build mechanical
0:51minds. Just as mechanical muscles made human labor less in demand so are mechanical minds
0:56making human brain labor less in demand.
0:59This is an economic revolution. You may think we've been here before, but we haven't.
1:03This time is different.
1:05## Physical Labor
1:07When you think of automation, you probably think of this: giant, custom-built, expensive,
1:11efficient but really dumb robots blind to the world and their own work. There were a
1:16scary kind of automation but they haven't taken over the world because they're only
1:20cost effective in narrow situations.
1:23But they are the old kind of automation, this is the new kind.
1:27Meet Baxter.
1:28Unlike these things which require skilled operators and technicians and millions of
1:32dollars, Baxter has vision and can learn what you want him to do by watching you do it.
1:37And he costs less than the average annual salary of a human worker. Unlike his older
1:41brothers he isn't pre-programmed for one specific job, he can do whatever work is within the
1:46reach of his arms. Baxter is what might be thought of as a general purpose robot and
1:51general purpose is a big deal.
1:53Think computers, they too started out as highly custom and highly expensive, but when cheap-ish
1:58general-purpose computers appeared they quickly became vital to everything.
2:02A general-purpose computer can just as easily calculate change or assign seats on an airplane
2:07or play a game or do anything by just swapping its software. And this huge demand for computers
2:13of all kinds is what makes them both more powerful and cheaper every year.
2:18Baxter today is the computer in the 1980s. He’s not the apex but the beginning. Even
2:23if Baxter is slow his hourly cost is pennies worth of electricity while his meat-based
2:27competition costs minimum wage. A tenth the speed is still cost effective when it's a
2:32hundred times cheaper. And while Baxtor isn't as smart as some of the other things we will
2:36talk about, he's smart enough to take over many low-skill jobs.
2:40And we've already seen how dumber robots than Baxter can replace jobs. In new supermarkets
2:45what used to be 30 humans is now one human overseeing 30 cashier robots.
2:50Or the hundreds of thousand baristas employed world-wide? There’s a barista robot coming
2:54for them. Sure maybe your guy makes your double-mocha-whatever just perfect and you’d never trust anyone
2:59else -- but millions of people don’t care and just want a decent cup of coffee. Oh and
3:05by the way this robot is actually a giant network of robots that remembers who you are
3:09and how you like your coffee no matter where you are. Pretty convenient.
3:13We think of technological change as the fancy new expensive stuff, but the real change comes
3:17from last decade's stuff getting cheaper and faster. That's what's happening to robots
3:22now. And because their mechanical minds are capable of decision making they are out-competing
3:27humans for jobs in a way no pure mechanical muscle ever could.
3:31## Luddite Horses
3:33Imagine a pair of horses in the early 1900s talking about technology. One worries all
3:38these new mechanical muscles will make horses unnecessary.
3:41The other reminds him that everything so far has made their lives easier -- remember all
3:45that farm work? Remember running coast-to-coast delivering mail? Remember riding into battle?
3:50All terrible. These city jobs are pretty cushy -- and with so many humans in the cities there
3:54are more jobs for horses than ever.
3:57Even if this car thingy takes off you might say, there will be new jobs for horses we
4:01can't imagine.
4:02But you, dear viewer, from beyond 2000 know what happened -- there are still working horses,
4:08but nothing like before. The horse population peaked in 1915 -- from that point on it was
4:13nothing but down.
4:14There isn’t a rule of economics that says better technology makes more, better jobs
4:18for horses. It sounds shockingly dumb to even say that out loud, but swap horses for humans
4:24and suddenly people think it sounds about right.
4:27As mechanical muscles pushed horses out of the economy, mechanical minds will do the
4:31same to humans. Not immediately, not everywhere, but in large enough numbers and soon enough
4:37that it's going to be a huge problem if we are not prepared. And we are not prepared.
4:42You, like the second horse, may look at the state of technology now and think it can’t
4:46possibly replace your job. But technology gets better, cheaper, and faster at a rate
4:50biology can’t match.
4:52Just as the car was the beginning of the end for the horse so now does the car show us
4:56the shape of things to come.
4:57## The Shape Of Things to Come
5:01Self-driving cars aren't the future: they're here and they work. Self-driving cars have
5:05traveled hundreds of thousands of miles up and down the California coast and through
5:09cities -- all without human intervention.
5:12The question is not if they'll replaces cars, but how quickly. They don’t need to be perfect,
5:16they just need to be better than us. Humans drivers, by the way, kill 40,000 people a
5:22year with cars just in the United States. Given that self-driving cars don’t blink,
5:26don’t text while driving, don’t get sleepy or stupid, it easy to see them being better
5:30than humans because they already are.
5:33Now to describe self-driving cars as cars at all is like calling the first cars mechanical
5:39horses. Cars in all their forms are so much more than horses that using the name limits
5:44your thinking about what they can even do. Lets call self-driving cars what they really
5:48are:
5:49Autos: the solution to the transport-objects-from-point-A-to-point-B problem. Traditional cars happen to be human
5:55sized to transport humans but tiny autos can work in wear houses and gigantic autos can
5:59work in pit mines. Moving stuff around is who knows how many jobs but the transportation
6:04industry in the United States employs about three million people. Extrapolating world-wide
6:09that’s something like 70 million jobs at a minimum.
6:13These jobs are over.
6:15The usual argument is that unions will prevent it. But history is filled with workers who
6:19fought technology that would replace them and the workers always loose. Economics always
6:24wins and there are huge incentives across wildly diverse industries to adopt autos.
6:30For many transportation companies, the humans are about a third of their total costs. That's
6:34just the straight salary costs. Humans sleeping in their long haul trucks costs time and money.
6:39Accidents cost money. Carelessness costs money. If you think insurance companies will be against
6:44it, guess what? Their perfect driver is one who pays their small premium but never gets
6:48into an accident.
6:50The autos are coming and they're the first place where most people will really see the
6:54robots changing society. But there are many other places in the economy where the same
6:58thing is happening, just less visibly.
7:00So it goes with autos, so it goes for everything.
7:03## Intellectual Labor
7:04### White Collar Work
7:06It's easy to look at Autos and Baxters and think: technology has always gotten rid of
7:10low-skill jobs we don't want people doing anyway. They'll get more skilled and do better
7:15educated jobs -- like they've always done.
7:17Even ignoring the problem of pushing a hundred-million additional people through higher education,
7:22white-collar work is no safe haven either. If your job is sitting in front of a screen
7:27and typing and clicking -- like maybe you're supposed to be doing right now -- the bots
7:31are coming for you too, buddy.
7:32Software bots are both intangible and way faster and cheaper than physical robots. Given
7:37that white collar workers are, from a companies perspective, both more expensive and more
7:41numerous -- the incentive to automate their work is greater than low skilled work.
7:46And that's just what automation engineers are for. These are skilled programmers whose
7:51entire job is to replace your job with a software bot.
7:54You may think even the world's smartest automation engineer could never make a bot to do your
7:58job -- and you may be right -- but the cutting edge of programming isn't super-smart programmers
8:03writing bots it's super-smart programmers writing bots that teach themselves how to
8:08do things the programmer could never teach them to do.
8:11How that works is well beyond the scope of this video, but the bottom line is there are
8:15limited ways to show a bot a bunch of stuff to do, show the bot a bunch of correctly done
8:20stuff, and it can figure out how to do the job to be done.
8:23Even with just a goal and no example of how to do it the bots can still learn. Take the
8:28stock market which, in many ways, is no longer a human endeavor. It's mostly bots that taught
8:33themselves to trade stocks, trading stocks with other bots that taught themselves.
8:37Again: it's not bots that are executing orders based on what their human controllers want,
8:37it's bots making the decisions of what to buy and sell on their own.
8:38As a result the floor of the New York Stock exchange isn't filled with traders doing their
8:42day jobs anymore, it's largely a TV set.
8:44So bots have learned the market and bots have learned to write. If you've picked up a newspaper
8:48lately you've probably already read a story written by a bot. There are companies that
8:53are teaching bots to write anything: Sports stories, TPS reports, even say, those quarterly
8:57reports that you write at work.
8:59Paper work, decision making, writing -- a lot of human work falls into that category
9:03and the demand for human metal labor is these areas is on the way down. But surely the professions
9:09are safe from bots? Yes?
9:14## Professions
9:15When you think 'lawyer' it's easy to think of trials. But the bulk of lawyering is actually
9:19drafting legal documents predicting the likely outcome and impact of lawsuits, and something
9:24called 'discovery' which is where boxes of paperwork gets dumped on the lawyers and they
9:28need to find the pattern or the one out-of-place transaction among it all.
9:32This can all be bot work. Discovery, in particular, is already not a human job in many firms.
9:38Not because there isn't paperwork to go through, there's more of it than ever, but because
9:42clever research bots sift through millions of emails and memos and accounts in hours
9:46not weeks -- crushing human researchers in terms of not just cost and time but, most
9:51importantly, accuracy. Bots don't get sleeping reading through a million emails.
9:56But that's the simple stuff: IBM has a bot named Watson: you may have seen him on TV
10:01destroy humans at Jeopardy — but that was just a fun side project for him.
10:05Watson's day-job is to be the best doctor in the world: to understand what people say
10:09in their own words and give back accurate diagnoses. And he's already doing that at
10:14Slone-Kettering, giving guidance on lung cancer treatments.
10:17Just as Auto don’t need to be perfect -- they just need to make fewer mistakes than humans,
10:21-- the same goes for doctor bots.
10:23Human doctors are by no means perfect -- the frequency and severity of misdiagnosis are
10:28terrifying -- and human doctors are severely limited in dealing with a human's complicated
10:33medical history. Understanding every drug and every drug's interaction with every other
10:37drug is beyond the scope of human knowability.
10:40Especially when there are research robots whose whole job it is to test 1,000s of new
10:45drugs at a time.
10:47Human doctors can only improve through their own experiences. Doctor bots can learn from
10:51the experiences of every doctor bot. Can read the latest in medical research and keep track
10:54of everything that happens to all his patients world-wide and make correlations that would
10:59be impossible to find otherwise.
11:01Not all doctors will go away, but when doctor bots are comparable to humans and they're
11:06only as far away as your phone -- the need for general doctors will be less.
11:10So professionals, white-collar workers and low-skill workers all have something to worry
11:15about.
11:16But perhaps you're still not worried because you're a special creative snowflakes. Well
11:21guess what? You're not that special.
11:24## Creative Labor
11:28Creativity may feel like magic, but it isn't. The brain is a complicated machine -- perhaps
11:32the most complicated machine in the whole universe -- but that hasn't stopped us from
11:36trying to simulate it.
11:38There is this notion that just as mechanical muscles allowed us to move into thinking jobs
11:42that mechanical minds will allow us all to move into creative work. But even if we assume
11:46the human mind is magically creative -- it's not, but just for the sake of argument -- artistic
11:51creativity isn't what the majority of jobs depend on. The number of writers and poets
11:55and directors and actors and artist who actually make a living doing their work is a tiny,
12:00tiny portion of the labor force. And given that these are professions that are dependent
12:04on popularity they will always be a small part of the population.
12:08There is no such thing as a poem and painting based economy.
12:12Oh, by the way, this music in the background that your listening to? It was written by
12:17a bot. Her name is Emily Howel and she can write an infinite amount of new music all
12:21day for free. And people can't tell the difference between her and human composers when put to
12:25a blind test.
12:27Talking about artificial creativity gets weird fast -- what does that even mean? But it's
12:32nonetheless a developing field.
12:33People used to think that playing chess was a uniquely creative human skill that machines
12:37could never do right up until they beat the best of us. And so it goes for all human talent.
12:44## Conclusion
12:46Right: this might have been a lot to take in, and you might want to reject it -- it's
12:51easy to be cynical of the endless, and idiotic, predictions of futures that never are. So
12:55that's why it's important to emphasize again this stuff isn't science fiction. The robots
13:00are here right now. There is a terrifying amount of working automation in labs and wear
13:05houses that is proof of concept.
13:07We have been through economic revolutions before, but the robot revolution is different.
13:12Horses aren't unemployed now because they got lazy as a species, they’re unemployable.
13:17There's little work a horse can do that do that pays for its housing and hay.
13:21And many bright, perfectly capable humans will find themselves the new horse: unemployable
13:26through no fault of their own.
13:28But if you still think new jobs will save us: here is one final point to consider. The
13:33US census in 1776 tracked only a few kinds of jobs. Now there are hundreds of kinds of
13:38jobs, but the new ones are not a significant part of the labor force.
13:42Here's the list of jobs ranked by the number of people that perform them - it's a sobering
13:46list with the transportation industry at the top. Going down the list all this work existed
13:52in some form a hundred years ago and almost all of them are targets for automation. Only
13:58when we get to number 33 on the list is there finally something new.
14:02Don't that every barista and officer worker lose their job before things are a problem.
14:07The unemployment rate during the great depression was 25%.
14:10This list above is 45% of the workforce. Just what we've talked about today, the stuff that
14:17already works, can push us over that number pretty soon. And given that even our modern
14:22technological wonderland new kinds of work are not a significant portion of the economy,
14:28this is a big problem.
14:29This video isn't about how automation is bad -- rather that automation is inevitable. It's
14:34a tool to produce abundance for little effort. We need to start thinking now about what to
14:39do when large sections of the population are unemployable -- through no fault of their
14:44own. What to do in a future where, for most jobs, humans need not apply.
Published on Aug 13, 2014
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## Robots, Etc:
Terex Port automation: http://www.terex.com/port-solutions/e...
Command | Cat MieStar System.: http://www.catminestarsystem.com/capa...
Bosch Automotive Technology: http://www.bosch-automotivetechnology...
Atlas Update: https://www.youtube.com/watch?v=SD6Ok...
Kiva Systems: http://www.kivasystems.com
PhantomX running Phoenix code: https://www.youtube.com/watch?v=rAeQn...
iRobot, Do You: https://www.youtube.com/watch?v=da-5U...
New pharmacy robot at QEHB: https://www.youtube.com/watch?v=_Ql1Z...
Briggo Coffee Experience: http://vimeo.com/77993254
John Deere Autosteer ITEC Pro 2010. In use while cultivating: https://www.youtube.com/watch?v=VAPfI...
The Duel: Timo Boll vs. KUKA Robot: https://www.youtube.com/watch?v=tIIJM...
Baxter with the Power of Intera 3: https://www.youtube.com/watch?v=DKR_p...
Baxter Research Robot SDK 1.0: https://www.youtube.com/watch?v=wgQLz...
Baxter the Bartender: https://www.youtube.com/watch?v=AeTs9...
Online Cash Registers Touch-Screen EPOS System Demonstration: https://www.youtube.com/watch?v=3yA22...
Self-Service Check in: https://www.youtube.com/watch?v=OafuI...
Robot to play Flappy Bird: https://www.youtube.com/watch?v=kHkMa...
e-david from University of Konstanz, Germany: https://vimeo.com/68859229
Sedasys: http://www.sedasys.com/
Empty Car Convoy: http://www.youtube.com/watch?v=EPTIXl...
Clever robots for crops: http://www.crops-robots.eu/index.php?...
Autonomously folding a pile of 5 previously-unseen towels: https://www.youtube.com/watch?v=gy5g3...
LS3 Follow Tight: https://www.youtube.com/watch?v=hNUeS...
Robotic Handling material: https://www.youtube.com/watch?v=pT3Xo...
Caterpillar automation project: http://www.catminestarsystem.com/arti...
Universal Robots has reinvented industrial robotics: https://www.youtube.com/watch?v=UQj-1...
Introducing WildCat: https://www.youtube.com/watch?v=wE3fm...
The Human Brain Project - Video Overview: https://www.youtube.com/watch?v=JqMpG...
This Robot Is Changing How We Cure Diseases: https://www.youtube.com/watch?v=ra0e9...
Jeopardy! - Watson Game 2: https://www.youtube.com/watch?v=kDA-7...
What Will You Do With Watson?: https://www.youtube.com/watch?v=Y_cqB...
## Other Credits
Mandelbrot set: https://www.youtube.com/watch?v=NGMRB...
Moore's law graph: http://en.wikipedia.org/wiki/File:PPT...
Apple II 1977: https://www.youtube.com/watch?v=CxJwy...
Beer Robot Fail m2803: https://www.youtube.com/watch?v=N4Lb_...
All Wales Ambulance Promotional Video: https://www.youtube.com/watch?v=658ai...
Clyde Robinson: https://www.flickr.com/photos/crobj/4...
Time lapse Painting - Monster Spa: https://www.youtube.com/watch?v=ED14i...
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