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

For those of us who are not keen on online dating, a more immediately useful notion is to choose which interactions to record and where. If you don’t want your Christmas shopping to leave Amazon confused about your tastes, do it on other sites. (Sorry, Amazon.) If you watch different kinds of videos at home and for work, keep two accounts on YouTube, one for each, and YouTube will learn to make the corresponding recommendations. And if you’re about to watch some videos of a kind that you ordinarily have no interest in, log out first. Use Chrome’s incognito mode not for guilty browsing (which you’d never do, of course) but for when you don’t want the current session to influence future personalization. On Netflix, adding profiles for the different people using your account will spare you R-rated recommendations on family movie night. If you don’t like a company, click on their ads: this will not only waste their money now, but teach Google to waste it again in the future by showing the ads to people who are unlikely to buy the products. And if you have very specific queries that you want Google to answer correctly in the future, take a moment to trawl through the later results pages for the relevant links and click on them. More generally, if a system keeps recommending the wrong things to you, try teaching it by finding and clicking on a bunch of the right ones and come back later to see if it did.

That could be a lot of work, though. What all of these illustrate, unfortunately, is how narrow the communication channel between you and the learner is today. You should be able to tell it as much as you want about yourself, not just have it learn indirectly from what you do. More than that, you should be able to inspect the learner’s model of you and correct it as desired. The learner can still decide to ignore you, if it thinks you’re lying or are low on self-knowledge, but at least it would be able to take your input into account. For this, the model needs to be in a form that humans can understand, such as a set of rules rather than a neural network, and it needs to accept general statements as input in addition to raw data, as Alchemy does. All of which brings us to the question of how good a model of you a learner can have and what you’d want to do with that model.

Take a moment to consider all the data about you that’s recorded on all the world’s computers: your e-mails, Office docs, texts, tweets, and Facebook and LinkedIn accounts; your web searches, clicks, downloads, and purchases; your credit, tax, phone, and health records; your Fitbit statistics; your driving as recorded by your car’s microprocessors; your wanderings as recorded by your cell phone; all the pictures of you ever taken; brief cameos on security cameras; your Google Glass snippets-and so on and so forth. If a future biographer had access to nothing but this “data exhaust” of yours, what picture of you would he form? Probably a quite accurate and detailed one in many ways, but also one where some essential things would be missing. Why did you, one beautiful day, decide to change careers? Could the biographer have predicted it ahead of time? What about that person you met one day and secretly never forgot? Could the biographer wind back through the found footage and say “Ah, there”?

The sobering (or perhaps reassuring) thought is that no learner in the world today has access to all this data (not even the NSA), and even if it did, it wouldn’t know how to turn it into a real likeness of you. But suppose you took all your data and gave it to the-real, future-Master Algorithm, already seeded with everything we could teach it about human life. It would learn a model of you, and you could carry that model in a thumb drive in your pocket, inspect it at will, and use it for everything you pleased. It would surely be a wonderful tool for introspection, like looking at yourself in the mirror, but it would be a digital mirror that showed not just your looks but all things observable about you-a mirror that could come alive and converse with you. What would you ask it? Some of the answers you might not like, but that would be all the more reason to ponder them. And some would give you new ideas, new directions. The Master Algorithm’s model of you might even help you become a better person.

Self-improvement aside, probably the first thing you’d want your model to do is negotiate the world on your behalf: let it loose in cyberspace, looking for all sorts of things for you. From all the world’s books, it would suggest a dozen you might want to read next, with more insight than Amazon could dream of. Likewise for movies, music, games, clothes, electronics-you name it. It would keep your refrigerator stocked at all times, natch. It would filter your e-mail, voice mail, Facebook posts, and Twitter feed and, when appropriate, reply on your behalf. It would take care of all the little annoyances of modern life for you, like checking credit-card bills, disputing improper charges, making arrangements, renewing subscriptions, and filling out tax returns. It would find a remedy for your ailment, run it by your doctor, and order it from Walgreens. It would bring interesting job opportunities to your attention, propose vacation spots, suggest which candidates to vote for on the ballot, and screen potential dates. And, after the match was made, it would team up with your date’s model to pick some restaurants you might both like. Which is where things start to get really interesting.

In this rapidly approaching future, you’re not going to be the only one with a “digital half” doing your bidding twenty-four hours a day. Everyone will have a detailed model of him- or herself, and these models will talk to each other all the time. If you’re looking for a job and company X is looking to hire, its model will interview your model. It will be a lot like a real, flesh-and-blood interview-your model will still be well advised to not volunteer negative information about you, and so on-but it will take only a fraction of a second. You’ll click on “Find Job” in your future LinkedIn account, and you’ll immediately interview for every job in the universe that remotely fits your parameters (profession, location, pay, etc.). LinkedIn will respond on the spot with a ranked list of the best prospects, and out of those, you’ll pick the first company that you want to have a chat with. Same with dating: your model will go on millions of dates so you don’t have to, and come Saturday, you’ll meet your top prospects at an OkCupid-organized party, knowing that you’re also one of their top prospects-and knowing, of course, that their other top prospects are also in the room. It’s sure to be an interesting night.

In the world of the Master Algorithm, “my people will call your people” becomes “my program will call your program.” Everyone has an entourage of bots, smoothing his or her way through the world. Deals get pitched, terms negotiated, arrangements made, all before you lift a finger. Today, drug companies target your doctor, because he decides what drugs to prescribe to you. Tomorrow, the purveyors of every product and service you use, or might use, will target your model, because your model will screen them for you. Their bots’ job is to get your bot to buy. Your bot’s job is to see through their claims, just as you see through TV commercials, but at a much finer level of detail, one that you’d never have the time or patience for. Before you buy a car, the digital you will go over every one of its specs, discuss them with the manufacturer, and study everything anyone in the world has said about that car and its alternatives. Your digital half will be like power steering for your life: it goes where you want to go but with less effort from you. This does not mean that you’ll end up in a “filter bubble,” seeing only what you reliably like, with no room for the unexpected; the digital you knows better than that. Part of its brief is to leave some things open to chance, to expose you to new experiences, and to look for serendipity.