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

A company like this could quickly become one of the most valuable in the world. As Alexis Madrigal of the Atlantic points out, today your profile can be bought for half a cent or less, but the value of a user to the Internet advertising industry is more like $1,200 per year. Google’s sliver of your data is worth about $20, Facebook’s $5, and so on. Add to that all the slivers that no one has yet, and the fact that the whole is more than the sum of the parts-a model of you based on all your data is much better than a thousand models based on a thousand slivers-and we’re looking at easily over a trillion dollars per year for an economy the size of the United States. It doesn’t take a large cut of that to make a Fortune 500 company. If you decide to take up the challenge and wind up becoming a billionaire, remember where you first got the idea.

Of course, some existing companies would love to host the digital you. Google, for example. Sergey Brin says that “we want Google to be the third half of your brain,” and some of Google’s acquisitions are probably not unrelated to how well their streams of user data complement its own. But, despite their head start, companies like Google and Facebook are not well suited to being your digital home because they have a conflict of interest. They earn a living by targeting ads, and so they have to balance your interests and the advertisers’. You wouldn’t let the first or second half of your brain have divided loyalties, so why would you let the third?

One possible showstopper is that the government may subpoena your data or even preventively jail you, Minority Report -style, if your model looks like a criminal’s. To forestall that, your data company can keep everything encrypted, with the key in your possession. (These days you can even compute over encrypted data without ever decrypting it.) Or you can keep it all in your hard disk at home, and the company just rents you the software.

If you don’t like the idea of a profit-making entity holding the keys to your kingdom, you can join a data union instead. (If there isn’t one in your neck of the cyberwoods yet, consider starting it.) The twentieth century needed labor unions to balance the power of workers and bosses. The twenty-first needs data unions for a similar reason. Corporations have a vastly greater ability to gather and use data than individuals. This leads to an asymmetry in power, and the more valuable the data-the better and more useful the models that can be learned from it-the greater the asymmetry. A data union lets its members bargain on equal terms with companies about the use of their data. Perhaps labor unions can get the ball rolling, and shore up their membership, by starting data unions for their members. But labor unions are organized by occupation and location; data unions can be more flexible. Join up with people you have a lot in common with; the models learned will be more useful to you that way. Notice that being in a data union does not mean letting other members see your data; it just means letting everyone use the models learned from the pooled data. Data unions can also be your vehicle for telling politicians what you want. Your data can influence the world as much as your vote-or more-because you only go to the polls on election day. On all other days, your data is your vote. Stand up and be counted!

So far I haven’t uttered the word privacy . That’s not by accident. Privacy is only one aspect of the larger issue of data sharing, and if we focus on it to the detriment of the whole, as much of the debate to date has, we risk reaching the wrong conclusions. For example, laws that forbid using data for any purpose other than the originally intended one are extremely myopic. (Not a single chapter of Freakonomics could have been written under such a law.) When people have to trade off privacy against other benefits, as when filling out a profile on a website, the implied value of privacy that comes out is much lower than if you ask them abstract questions like “Do you care about your privacy?” But privacy debates are more often framed in terms of the latter. The European Union’s Court of Justice has decreed that people have the right to be forgotten, but they also have the right to remember, whether it’s with their neurons or a hard disk. So do companies, and up to a point, the interests of users, data gatherers, and advertisers are aligned. Wasted attention benefits no one, and better data makes better products. Privacy is not a zero-sum game, even though it’s often treated like one.

Companies that host the digital you and data unions are what a mature future of data in society looks like to me. Whether we’ll get there is an open question. Today, most people are unaware of both how much data about them is being gathered and what the potential costs and benefits are. Companies seem content to continue doing it under the radar, terrified of a blowup. But sooner or later a blowup will happen, and in the ensuing fracas, draconian laws will be passed that in the end will serve no one. Better to foster awareness now and let everyone make their individual choices about what to share, what not, and how and where.

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.