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I’ve been a machine-learning researcher for more than twenty years. My interest in it was sparked by a book with an odd title I saw in a bookstore when I was a senior in college:Artificial Intelligence. It had only a short chapter on machine learning, but on reading it, I immediately became convinced that learning was the key to solving AI and that the state of the art was so primitive that maybe I could contribute something. Shelving plans for an MBA, I entered the PhD program at the University of California, Irvine. Machine learning was then a small, obscure field, and UCI had one of the few sizable research groups anywhere. Some of my classmates dropped out because they didn’t see much of a future in it, but I persisted. To me nothing could have more impact than teaching computers to learn: if we could do that, we would get a leg up on every other problem. By the time I graduated five years later, the data-mining explosion was under way, and so was my path to this book. My doctoral dissertation unified symbolic and analogical learning. I’ve spent much of the last ten years unifying symbolism and Bayesianism, and more recently those two with connectionism. It’s time to go the next step and attempt a synthesis of all five paradigms.. One if by land, two if by Internet. All knowledge-past, present, and future-can be derived from data by a single, universal learning algorithm.. In the early days of AI, machine learning seemed like the obvious path to computers with humanlike intelligence; Turing and others thought it was theonly plausible path. But then the knowledge engineers struck back, and by 1970 machine learning was firmly on the back burner. For a moment in the 1980s, it seemed like knowledge engineering was about to take over the world, with companies and countries making massive investments in it. But disappointment soon set in, and machine learning began its inexorable rise, at first quietly, and then riding a roaring wave of data.. Crucially, the Master Algorithm is not required to start from scratch in each new problem. That bar is probably too high forany learner to meet, and it’s certainly very unlike what people do. For example, language does not exist in a vacuum; we couldn’t understand a sentence without our knowledge of the world it refers to. Thus, when learning to read, the Master Algorithm can rely on having previously learned to see, hear, and control a robot. Likewise, a scientist does not just blindly fit models to data; he can bring all his knowledge of the field to bear on the problem. Therefore, when making discoveries in biology, the Master Algorithm can first read all the biology it wants, relying on having previously learned to read. The Master Algorithm is not just a passive consumer of data; it can interact with its environment and actively seek the data it wants, like Adam, the robot scientist, or like any child exploring her world.. Of course, computing the length of a planet’s year is a very simple problem, involving only multiplication and square roots. In general, program trees can include the full range of programming constructs, such asIf…then… statements, loops, and recursion. A more illustrative example of what genetic programming can do is figuring out the sequence of actions a robot needs to perform to achieve some goal. Suppose I ask my officebot to bring me a stapler from the closet down the hall. The robot has a large set of behaviors available to it, such as moving down a hallway, opening a door, picking up an object, and so on. Each of these can in turn be composed of various sub-behaviors: move the robot’s hand toward the object, or grasp it at various possible points, for example. Each behavior may be executed or not depending on the results of previous behaviors, may need to be repeated some number of times, and so on. The challenge is to assemble the right structure of behaviors and sub-behaviors, together with the parameters for each, such as how far to move the hand. Starting with the robot’s “atomic” behaviors and their allowed combinations, genetic programming can assemble a complex behavior that accomplishes the desired goal. A number of researchers have evolved strategies for robot soccer players in this way.. Another disturbing example is what happens with our good old friend, the normal distribution, aka a bell curve. What a normal distribution says is that data is essentially located at a point (the mean of the distribution), but with some fuzz around it (given by the standard deviation). Right? Not in hyperspace. With a high-dimensional normal distribution, you’re more likely to get a sample far from the mean than close to it. A bell curve in hyperspace looks more like a doughnut than a bell. And when nearest-neighbor walks into this topsy-turvy world, it gets hopelessly confused. All examples look equally alike, and at the same time they’re too far from each other to make useful predictions. If you sprinkle examples uniformly at random inside a high-dimensional hypercube, most are closer to a face of the cube than to their nearest neighbor. In medieval maps, uncharted areas were marked with dragons, sea serpents, and other fantastical creatures, or just with the phrasehere be dragons. In hyperspace, the dragons are everywhere, including at your front door. Try to walk to your next-door neighbor’s house, and you’ll never get there; you’ll be forever lost in strange lands, wondering where all the familiar things went.. [Картинка: pic_32.jpg]. The notion that not all states have rewards (positive or negative) but every state has a value is central to reinforcement learning. In board games, only final positions have a reward (1, 0, or−1 for a win, tie, or loss, say). Other positions give no immediate reward, but they have value in that they can lead to rewards later. A chess position from which you can force checkmate in some number of moves is practically as good as a win and therefore has high value. We can propagate this kind of reasoning all the way to good and bad opening moves, even if at that distance the connection is far from obvious. In video games, the rewards are usually points, and the value of a state is the number of points you can accumulate starting from that state. In real life, a reward now is better than a reward later, so future rewards can be discounted by some rate of return, like investments. Of course, the rewards depend on what actions you choose, and the goal of reinforcement learning is to always choose the action that leads to the greatest rewards. Should you pick up the phone and ask your friend for a date? It could be the start of a beautiful relationship or just the route to a painful rejection. Even if your friend agrees to go on a date, that date may turn out well or not. Somehow, you have to abstract over all the infinite paths the future could take and make a decision now.Reinforcement learning does that by estimating the value of each state-the sum total of the rewards you can expect to get starting from that state-and choosing the actions that maximize it.. Out of many models, one. Metalearning is remarkably successful, but it’s not a very deep way to combine models. It’s also expensive, requiring as it does many runs of learning, and the combined models can be quite opaque. (“I believe you have prostate cancer because the decision tree, the genetic algorithm, and Naïve Bayes say so, although the multilayer perceptron and the SVM disagree.”) Moreover, all the combined models are really just one big, messy model. Can’t we have a single learner that does the same job? Yes we can.. In the coming decades, machine learning will affect such a broad swath of human life that one chapter of one book cannot possibly do it justice. Nevertheless, we can already see a number of recurring themes, and it’s those we’ll focus on, starting with what psychologists call theory of mind-the computer’s theory of your mind, that is.. Chapter Two. Sebastian Seung’sConnectome (Houghton Mifflin Harcourt, 2012) is an accessible introduction to neuroscience, connectomics, and the daunting challenge of reverse engineering the brain.Parallel Distributed Processing,* edited by David Rumelhart, James McClelland, and the PDP research group (MIT Press, 1986), is the bible of connectionism in its 1980s heyday.Neurocomputing,* edited by James Anderson and Edward Rosenfeld (MIT Press, 1988), collates many of the classic connectionist papers, including: McCulloch and Pitts on the first models of neurons; Hebb on Hebb’s rule; Rosenblatt on perceptrons; Hopfield on Hopfield networks; Ackley, Hinton, and Sejnowski on Boltzmann machines; Sejnowski and Rosenberg on NETtalk; and Rumelhart, Hinton, and Williams on backpropagation. “Efficient backprop,”* by Yann LeCun, Léon Bottou, Genevieve Orr, and Klaus-Robert Müller, inNeural Networks: Tricks of the Trade, edited by Genevieve Orr and Klaus-Robert Müller (Springer, 1998), explains some of the main tricks needed to make backprop work.. Model Ensembles: Foundations and Algorithms,* by Zhi-Hua Zhou (Chapman and Hall, 2012), is an introduction to metalearning. The original paper on stacking is“Stacked generalization,”* by David Wolpert (Neural Networks, 1992). Leo Breiman introduced bagging in“Bagging predictors”* (Machine Learning, 1996) and random forests in“Random forests”* (Machine Learning, 2001). Boosting is described in“Experiments with a new boosting algorithm,” by Yoav Freund and Rob Schapire (Proceedings of the Thirteenth International Conference on Machine Learning, 1996)..