Книга: The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

A neural network stole my job

How much of your brain does your job use? The more it does, the safer you are. In the early days of AI, the common view was that computers would replace blue-collar workers before white-collar ones, because white-collar work requires more brains. But that’s not quite how things turned out. Robots assemble cars, but they haven’t replaced construction workers. On the other hand, machine-learning algorithms have replaced credit analysts and direct marketers. As it turns out, evaluating credit applications is easier for machines than walking around a construction site without tripping, even though for humans it’s the other way around. The common theme is that narrowly defined tasks are easily learned from data, but tasks that require a broad combination of skills and knowledge aren’t. Most of your brain is devoted to vision and motion, which is a sign that walking around is much more complex than it seems; we just take it for granted because, having been honed to perfection by evolution, it’s mostly done subconsciously. The company Narrative Science has an AI system that can write pretty good summaries of baseball games, but not novels, because-pace George F. Will-there’s a lot more to life than to baseball games. Speech recognition is hard for computers because it’s hard to fill in the blanks-literally, the sounds speakers routinely elide-when you have no idea what the person is talking about. Algorithms can predict stock fluctuations but have no clue how they relate to politics. The more context a job requires, the less likely a computer will be able to do it soon. Common sense is important not just because your mom taught you so, but because computers don’t have it.

The best way to not lose your job is to automate it yourself. Then you’ll have time for all the parts of it that you didn’t before and that a computer won’t be able to do any time soon. (If there aren’t any, stay ahead of the curve and get a new job now.) If a computer has learned to do your job, don’t try to compete with it; harness it. H &R Block is still in business, but tax preparers’ jobs are much less dreary than they used to be, now that computers do most of the grunge work. (OK, perhaps this is not the best example, given that the tax code’s exponential growth is one of the few that can hold its own against computing power’s exponential growth.) Think of big data as an extension of your senses and learning algorithms as an extension of your brain. The best chess players these days are so-called centaurs, half-man and half-program. The same is true in many other occupations, from stock analyst to baseball scout. It’s not man versus machine; it’s man with machine versus man without. Data and intuition are like horse and rider, and you don’t try to outrun a horse; you ride it.

As technology progresses, an ever more intimate mix of human and machine takes shape. You’re hungry; Yelp suggests some good restaurants. You pick one; GPS gives you directions. You drive; car electronics does the low-level control. We are all cyborgs already. The real story of automation is not what it replaces but what it enables. Some professions disappear, but many more are born. Most of all, automation makes all sorts of things possible that would be way too expensive if done by humans. ATMs replaced some bank tellers, but mainly they let us withdraw money any time, anywhere. If pixels had to be colored one at a time by human animators, there would be no Toy Story and no video games.

Still, we can ask whether we’ll eventually run out of jobs for humans. I think not. Even if the day comes-and it won’t be soon-when computers and robots can do everything better, there will still be jobs for at least some of us. A robot may be able to do a perfect impersonation of a bartender, down to the small talk, but patrons may still prefer a bartender they know is human, just because he is. Restaurants with human waiters will have extra cachet, just as handmade goods already do. People still go to the theater, ride horses, and sail, even though we have movies, cars, and motorboats. More importantly, some professionals will be truly irreplaceable because their jobs require the one thing that computers and robots by definition cannot have: the human experience. By that I don’t mean touchy-feely jobs, because touchy-feely is not hard to fake; witness the success of robo-pets. I mean the humanities, whose domain is precisely everything you can’t understand without the experience of being human. We worry that the humanities are in a death spiral, but they’ll rise from the ashes once other professions have been automated. The more everything is done cheaply by machines, the more valuable the humanist’s contribution will be.

Conversely, the long-term prospects of scientists are not the brightest, sadly. In the future, the only scientists may well be computer scientists, meaning computers doing science. The people formerly known as scientists (like me) will devote their lives to understanding the scientific advances made by computers. They won’t be noticeably less happy than before; after all, science was always a hobby to them. And one very important job for the technically minded will remain: keeping an eye on the computers. In fact, this will require more than engineers; ultimately, it may be the full-time occupation of all mankind to figure out what we want from the machines and make sure we’re getting it-more on this later in this chapter.

In the meantime, as the boundary between automatable and non-automatable jobs advances across the economic landscape, what we’ll likely see is unemployment creeping up, downward pressure on the wages of more and more professions, and increasing rewards for the fewer and fewer that can’t yet be automated. This is what’s already happening, of course, but it has much further to run. The transition will be tumultuous, but thanks to democracy, it will have a happy ending. (Hold on to your vote-it may be the most valuable thing you have.) When the unemployment rate rises above 50 percent, or even before, attitudes about redistribution will radically change. The newly unemployed majority will vote for generous lifetime unemployment benefits and the sky-high taxes needed to fund them. These won’t break the bank because machines will do the necessary production. Eventually, we’ll start talking about the employment rate instead of the unemployment one and reducing it will be seen as a sign of progress. (“The US is falling behind. Our employment rate is still 23 percent.”) Unemployment benefits will be replaced by a basic income for everyone. Those of us who aren’t satisfied with it will be able to earn more, stupendously more, in the few remaining human occupations. Liberals and conservatives will still fight about the tax rate, but the goalposts will have permanently moved. With the total value of labor greatly reduced, the wealthiest nations will be those with the highest ratio of natural resources to population. (Move to Canada now.) For those of us not working, life will not be meaningless, any more than life on a tropical island where nature’s bounty meets all needs is meaningless. A gift economy will develop, of which the open-source software movement is a preview. People will seek meaning in human relationships, self-actualization, and spirituality, much as they do now. The need to earn a living will be a distant memory, another piece of humanity’s barbaric past that we rose above.

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