Because the potential impact is so great, it would behoove us to try to invent the Master Algorithm even if the odds of success were low. And even if it takes a long time, searching for a universal learner has many immediate benefits. One is the better understanding of machine learning that a unified view enables. Too many business decisions are made with scant understanding of the analytics underpinning them, but it doesn’t have to be that way. To use a technology, we don’t need to master its inner workings, but we do need to have a good conceptual model of it. We need to know how to find a station on the radio, or change the volume. Today, those of us who aren’t machine-learning experts have no conceptual model of what a learner does. The algorithms we drive when we use Google, Facebook, or the latest analytics suite are a bit like a black limo with tinted windows that mysteriously shows up at our door one night: Should we get in? Where will it take us? It’s time to get in the driver’s seat. Knowing the assumptions that different learners make will help us pick the right one for the job, instead of going with a random one that fell into our lap-and then suffering with it for years, painfully rediscovering what we should have known from the start. By knowing what learners optimize, we can make certain they optimize what we care about, rather than what came in the box. Perhaps most important, once we know how a particular learner arrives at its conclusions, we’ll know what to make of that information-what to believe, what to return to the manufacturer, and how to get a better result next time around. And with the universal learner we’ll develop in this book as the conceptual model, we can do all this without cognitive overload. Machine learning is simple at heart; we just need to peel away the layers of math and jargon to reveal the innermost Russian doll.
These benefits apply in both our personal and professional lives. How do I make the best of the trail of data that my every step in the modern world leaves? Every transaction works on two levels: what it accomplishes for you and what it teaches the system you just interacted with. Being aware of this is the first step to a happy life in the twenty-first century. Teach the learners, and they will serve you; but first you need to understand them. What in my job can be done by a learning algorithm, what can’t, and-most important-how can I take advantage of machine learning to do it better? The computer is your tool, not your adversary. Armed with machine learning, a manager becomes a supermanager, a scientist a superscientist, an engineer a superengineer. The future belongs to those who understand at a very deep level how to combine their unique expertise with what algorithms do best.
But perhaps the Master Algorithm is a Pandora’s box best left closed. Will computers enslave us or even exterminate us? Will machine learning be the handmaiden of dictators or evil corporations? Knowing where machine learning is headed will help us to understand what to worry about, what not, and what to do about it. The Terminator scenario, where a super-AI becomes sentient and subdues mankind with a robot army, has no chance of coming to pass with the kinds of learning algorithms we’ll meet in this book. Just because computers can learn doesn’t mean they magically acquire a will of their own. Learners learn to achieve the goals we set them; they don’t get to change the goals. Rather, we need to worry about them trying to serve us in ways that do more harm than good because they don’t know any better, and the cure for that is to teach them better.
Most of all, we have to worry about what the Master Algorithm could do in the wrong hands. The first line of defense is to make sure the good guys get it first-or, if it’s not clear who the good guys are, to make sure it’s open-sourced. The second is to realize that, no matter how good the learning algorithm is, it’s only as good as the data it gets. He who controls the data controls the learner. Your reaction to the datafication of life should not be to retreat to a log cabin-the woods, too, are full of sensors-but to aggressively seek control of the data that matters to you. It’s good to have recommenders that find what you want and bring it to you; you’d feel lost without them. But they should bring you what you want, not what someone else wants you to have. Control of data and ownership of the models learned from it is what many of the twenty-first century’s battles will be about-between governments, corporations, unions, and individuals. But you also have an ethical duty to share data for the common good. Machine learning alone will not cure cancer; cancer patients will, by sharing their data for the benefit of future patients.
A different theory of everything
Science today is thoroughly balkanized, a Tower of Babel where each subcommunity speaks its own jargon and can see only into a few adjacent subcommunities. The Master Algorithm would provide a unifying view of all of science and potentially lead to a new theory of everything. At first this may seem like an odd claim. What machine learning does is induce theories from data. How could the Master Algorithm itself grow into a theory? Isn’t string theory the theory of everything, and the Master Algorithm nothing like it?
To answer these questions, we have to first understand what a scientific theory is and is not. A theory is a set of constraints on what the world could be, not a complete description of it. To obtain the latter, you have to combine the theory with data. For example, consider Newton’s second law. It says that force equals mass times acceleration, or F = ma. It does not say what the mass or acceleration of any object are, or the forces acting on it. It only requires that, if the mass of an object is m and its acceleration is a, then the total force on it must be ma. It removes some of the universe’s degrees of freedom, but not all. The same is true of all other physical theories, including relativity, quantum mechanics, and string theory, which are, in effect, refinements of Newton’s laws.
The power of a theory lies in how much it simplifies our description of the world. Armed with Newton’s laws, we only need to know the masses, positions, and velocities of all objects at one point in time; their positions and velocities at all times follow. So Newton’s laws reduce our description of the world by a factor of the number of distinguishable instants in the history of the universe, past and future. Pretty amazing! Of course, Newton’s laws are only an approximation of the true laws of physics, so let’s replace them with string theory, ignoring all its problems and the question of whether it can ever be empirically validated. Can we do better? Yes, for two reasons.