Take online dating. When you use Match.com, eHarmony, or OkCupid (suspend your disbelief, if necessary), your goal is simple: to find the best possible date you can. But chances are it will take a lot of work and several disappointing dates before you meet someone you really like. One hardy geek extracted twenty thousand profiles from OkCupid, did his own data mining, found the woman of his dreams on the eighty-eighth date, and told his odyssey to Wired magazine. To succeed with fewer dates and less work, your two main tools are your profile and your responses to suggested matches. One popular option is to lie (about your age, for example). This may seem unethical, not to mention liable to blow up in your face when your date discovers the truth, but there’s a twist. Savvy online daters already know that people lie about their age on their profiles and adjust accordingly, so if you state your true age, you’re effectively telling them you’re older than you really are! In turn, the learner doing the matching thinks people prefer younger dates than they really do. The logical next step is for people to lie about their age by even more, ultimately rendering this attribute meaningless.
A better way for all concerned is to focus on your specific, unusual attributes that are highly predictive of a match, in the sense that they pick out people you like that not everyone else does, and therefore have less competition for. Your job (and your prospective date’s) is to provide these attributes. The matcher’s job is to learn from them, in the same way that an old-fashioned matchmaker would. Compared to a village matchmaker, Match.com’s algorithm has the advantage that it knows vastly more people, but the disadvantage is that it knows them much more superficially. A naïve learner, such as a perceptron, will be content with broad generalizations like “gentlemen prefer blondes.” A more sophisticated one will find patterns like “people with the same unusual musical tastes are often good matches.” If Alice and Bob both like Beyoncé, that alone hardly singles them out for each other. But if they both like Bishop Allen, that makes them at least a little bit more likely to be potential soul mates. If they’re both fans of a band the learner does not know about, that’s even better, but only a relational algorithm like Alchemy can pick it up. The better the learner, the more it’s worth your time to teach it about you. But as a rule of thumb, you want to differentiate yourself enough so that it won’t confuse you with the “average person” (remember Bob Burns from Chapter 8), but not be so unusual that it can’t fathom you.
Online dating is in fact a tough example because chemistry is hard to predict. Two people who hit it off on a date may wind up falling in love and believing passionately that they were made for each other, but if their initial conversation takes a different turn, they might instead find each other annoying and never want to meet again. What a really sophisticated learner would do is run a thousand Monte Carlo simulations of a date between each pair of plausible matches and rank the matches by the fraction of dates that turned out well. Short of that, dating sites can organize parties and invite people who are each a likely match for many of the others, letting them accomplish in a few hours what would otherwise take weeks.
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.