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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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English 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.

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