Pedro Domingos - The Master Algorithm - How the Quest for the Ultimate Learning Machine Will Remake Our World

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.

Soldiering is harder to automate than science, but it will be as well. One of the prime uses of robots is to do things that are too dangerous for humans, and fighting wars is about as dangerous as it gets. Robots already defuse bombs, and drones allow a platoon to see over the hill. Self-driving supply trucks and robotic mules are on the way. Soon we will need to decide whether robots are allowed to pull the trigger on their own. The argument for doing this is that we want to get humans out of harm’s way, and remote control is not viable in fast-moving, shoot-or-be-shot situations. The argument against is that robots don’t understand ethics, and so can’t be entrusted with life-or-death decisions. But we can teach them. The deeper question is whether we’re ready to.

It’s not hard to state general principles like military necessity, proportionality, and sparing civilians. But there’s a gulf between them and concrete actions, which the soldier’s judgment has to bridge. Asimov’s three laws of robotics quickly run into trouble when robots try to apply them in practice, as his stories memorably illustrate. General principles are usually contradictory, if not self-contradictory, and they have to be lest they turn all shades of gray into black and white. When does military necessity outweigh sparing civilians? There is no universal answer and no way to program a computer with all the eventualities. Machine learning, however, provides an alternative. First, teach the robot to recognize the relevant concepts, for example with data sets of situations where civilians were and were not spared, armed response was and was not proportional, and so on. Then give it a code of conduct in the form of rules involving these concepts. Finally, let the robot learn how to apply the code by observing humans: the soldier opened fire in this case but not in that case. By generalizing from these examples, the robot can learn an end-to-end model of ethical decision making, in the form of, say, a large MLN. Once the robot’s decisions agree with a human’s as often as one human agrees with another, the training is complete, meaning the model is ready for download into thousands of robot brains. Unlike humans, robots don’t lose their heads in the heat of combat. If a robot malfunctions, the manufacturer is responsible. If it makes a wrong call, its teachers are.

The main problem with this scenario, as you may have already guessed, is that letting robots learn ethics by observing humans may not be such a good idea. The robot is liable to get seriously confused when it sees that humans’ actions often violate their ethical principles. We can clean up the training data by including only the examples where, say, a panel of ethicists agrees that the soldier made the right decision, and the panelists can also inspect and tweak the model post-learning to their satisfaction. Agreement may be hard to reach, however, particularly if the panel includes all the different kinds of people it should. Teaching ethics to robots, with their logical minds and lack of baggage, will force us to examine our assumptions and sort out our contradictions. In this, as in many other areas, the greatest benefit of machine learning may ultimately be not what the machines learn but what we learn by teaching them.