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An algorithm is not just any set of instructions: they have to be precise and unambiguous enough to be executed by a computer. For example, a cooking recipe is not an algorithm because it doesn’t exactly specify what order to do things in or exactly what each step is. Exactly how much sugar is a spoonful? As everyone who’s ever tried a new recipe knows, following it may result in something delicious or a mess. In contrast, an algorithm always produces the same result. Even if a recipe specifies precisely half an ounce of sugar, we’re still not out of the woods because the computer doesn’t know what sugar is, or an ounce. If we wanted to program a kitchen robot to make a cake, we would have to tell it how to recognize sugar from video, how to pick up a spoon, and so on. (We’re still working on that.) The computer has to know how to execute the algorithm all the way down to turning specific transistors on and off. So a cooking recipe is very far from an algorithm.. 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?. Bayesians are concerned above all with uncertainty. All learned knowledge is uncertain, and learning itself is a form of uncertain inference. The problem then becomes how to deal with noisy, incomplete, and even contradictory information without falling apart. The solution is probabilistic inference, and the master algorithm is Bayes’ theorem and its derivates. Bayes’ theorem tells us how to incorporate new evidence into our beliefs, and probabilistic inference algorithms do that as efficiently as possible.. Rationalists believe that the senses deceive and that logical reasoning is the only sure path to knowledge. Empiricists believe that all reasoning is fallible and that knowledge must come from observation and experimentation. The French are rationalists; the Anglo-Saxons (as the French call them) are empiricists. Pundits, lawyers, and mathematicians are rationalists; journalists, doctors, and scientists are empiricists.Murder, She Wrote is a rationalist TV crime show;CSI: Crime Scene Investigation is an empiricist one. In computer science, theorists and knowledge engineers are rationalists; hackers and machine learners are empiricists.. Despite the popularity of decision trees, inverse deduction is the better starting point for the Master Algorithm. It has the crucial property that incorporating knowledge into it is easy-and we know Hume’s problem makes that essential. Also, sets of rules are an exponentially more compact way to represent most concepts than decision trees. Converting a decision tree to a set of rules is easy: each path from the root to a leaf becomes a rule, and there’s no blowup. On the other hand, in the worst case converting a set of rules into a decision tree requires converting each rule into a mini-decision tree, and then replacing each leaf of rule 1’s tree with a copy of rule 2’s tree, each leaf of each copy of rule 2 with a copy of rule 3, and so on, causing a massive blowup.. [Картинка: pic_22.jpg]. The points on the line are at the same distance from the two capitals; points to the left of the line are closer to Positiville, so nearest-neighbor assumes they’re part of Posistan and vice versa. Of course, it would be a lucky day if that was the exact border, but as an approximation it’s probably a lot better than nothing. It’s when we know a lot of towns on both sides of the border, though, that things get really interesting:. The curse of dimensionality. A face has only about fifty muscles, so fifty numbers should suffice to describe all possible expressions, with plenty of room to spare. The shape of the eyes, nose, mouth, and so on-the features that let you tell one person from another-shouldn’t take more than a few dozen numbers, either. After all, with only ten choices for each facial feature, a police artist can put together a sketch of a suspect that’s good enough to recognize him. You can add a few more numbers to specify lighting and pose, but that’s about it. So if you giveme a hundred numbers or so, that should be enough to re-create a picture of a face. Conversely, Robby’s brain should be able to take in a picture of a face and quickly reduce it to the hundred numbers that really matter.. The first step accomplished, you hurry on to the Bayesian district. Even from a distance, you can see how it clusters around the Cathedral of Bayes’ Theorem. MCMC Alley zigzags randomly along the way. This is going to take a while. You take a shortcut onto Belief Propagation Street, but it seems to loop around forever. Then you see it: the Most Likely Avenue, rising majestically toward the Posterior Probability Gate. Rather than average over all models, you can head straight for the most probable one, confident that the resulting predictions will be almost the same. And you can let genetic search pick the model’s structure and gradient descent its parameters. With a sigh of relief, you realize that’s all the probabilistic inference you’ll need, at least until it’s time to answer questions using the model.. P =ew•n /Z. Recall that a Markov network is defined by a weighted sum of features, much like a perceptron. Suppose we have a collection of photos of people. We pick a random one and compute features of it likeThe person has gray hair, The person is old, The person is a woman, and so on. In a perceptron, we pass the weighted sum of these features through a threshold to decide whether, say, the person is your grandmother or not. In a Markov network, we do something very different (at least at first sight): we exponentiate the weighted sum, turning it into a product of factors, and this product is the probability of choosing that particular picture from the collection, regardless of whether your grandmother is in it. If you have many pictures of old people, the weight of that feature goes up. If most of them are of men, the weight ofThe person is a woman goes down. The features can be anything we want, making Markov networks a remarkably flexible way to represent probability distributions.. Connectionists’ models are inspired by the brain, with networks of S curves that correspond to neurons and weighted connections between them corresponding to synapses. In Alchemy, two variables are connected if they appear together in some formula, and the probability of a variable given its neighbors is an S curve. (Although I won’t show why, it’s a direct consequence of the master equation we saw in the previous section.) The connectionists’ master algorithm is backpropagation, which they use to figure out which neurons are responsible for which errors and adjust their weights accordingly. Backpropagation is a form of gradient descent, which Alchemy uses to optimize the weights of a Markov logic network.. So far I haven’t uttered the wordprivacy. 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 ofFreakonomics 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.. An early list of examples of machine learning’s impact on daily life can be found in “Behind-the-scenes data mining,” by George John (SIGKDD Explorations, 1999), which was also the inspiration for the“day-in-the-life” paragraphs of the prologue. Eric Siegel’s bookPredictive Analytics (Wiley, 2013) surveys a large number of machine-learning applications. The termbig data was popularized by the McKinsey Global Institute’s 2011 reportBig Data: The Next Frontier for Innovation, Competition, and Productivity. Many of the issues raised by big data are discussed inBig Data: A Revolution That Will Change How We Live, Work, and Think, by Viktor Mayer-Schönberger and Kenneth Cukier (Houghton Mifflin Harcourt, 2013). The textbook I learned AI from isArtificial Intelligence,* by Elaine Rich (McGraw-Hill, 1983). A current one isArtificial Intelligence: A Modern Approach, by Stuart Russell and Peter Norvig (3rd ed., Prentice Hall, 2010). Nils Nilsson’sThe Quest for Artificial Intelligence (Cambridge University Press, 2010) tells the story of AI from its earliest days..