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During a break you check on your mutual funds. Most of them use learning algorithms to help pick stocks, and one of them is completely run by a learning system. At lunchtime you walk down the street, smart phone in hand, looking for a place to eat. Yelp’s learning system helps you find it. Your cell phone is chock-full of learning algorithms. They’re hard at work correcting your typos, understanding your spoken commands, reducing transmission errors, recognizing bar codes, and much else. Your phone can even anticipate what you’re going to do next and advise you accordingly. For example, as you’re finishing lunch, it discreetly alerts you that your afternoon meeting with an out-of-town visitor will have to start late because her flight has been delayed.. After a point, there just aren’t enough programmers and consultants to do all that’s needed, and the company inevitably turns to machine learning. Amazon can’t neatly encode the tastes of all its customers in a computer program, and Facebook doesn’t know how to write a program that will choose the best updates to show to each of its users. Walmart sells millions of products and has billions of choices to make every day; if the programmers at Walmart tried to write a program to make all of them, they would never be done. Instead, what these companies do is turn learning algorithms loose on the mountains of data they’ve accumulated and let them divine what customers want.. In the same way that a bank without databases can’t compete with a bank that has them, a company without machine learning can’t keep up with one that uses it. While the first company’s experts write a thousand rules to predict what its customers want, the second company’s algorithms learn billions of rules, a whole set of them for each individual customer. It’s about as fair as spears against machine guns. Machine learning is a cool new technology, but that’s not why businesses embrace it. They embrace it because they have no choice.. If your credit card was used to purchase one dollar of gas, it was stolen.. [Картинка: pic_5.jpg]. According to the decision tree above, you’re either a Republican, a Democrat, or an independent; you can’t be more than one, or none of the above. Sets of concepts with this property are called sets of classes, and the algorithm that predicts them is a classifier. A single concept implicitly defines two classes: the concept itself andits negation. (For example, spam and nonspam.) Classifiers are the most widespread form of machine learning.. Hebb’s rule, as it has come to be known, is the cornerstone of connectionism. Indeed, the field derives its name from the belief that knowledge is stored in the connections between neurons. Donald Hebb, a Canadian psychologist, stated it this way in his 1949 bookThe Organization of Behavior:“When an axon of cellA is near enough cellB and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such thatA’s efficiency, as one of the cells firingB, is increased.” It’s often paraphrased as “Neurons that fire together wire together.”. On the one hand, evolution has produced many amazing things, none more amazing than you. With or without crossover, evolving structure is an essential part of the Master Algorithm. The brain can learn anything, but it can’t evolve a brain. If we thoroughly understood its architecture, we could just implement it in hardware, but we’re very far from that; getting an assist from computer-simulated evolution is a no-brainer. What’s more, we also want to evolve the brains of robots, systems with arbitrary sensors,and super-AIs. There’s no reason to stick with the design of the human brain if there are better ones for those tasks. On the other hand, evolution is excruciatingly slow. The entire life of an organism yields only one piece of information about its genome: its fitness, reflected in the organism’s number of offspring. That’s a colossal waste of information, which neural learning avoids by acquiring the information at the point of use (so to speak). As connectionists like Geoff Hinton like to point out, there’s no advantage to carrying around in the genome information that we can readily acquire from the senses. When a newborn opens his eyes, the visual world comes flooding in; the brain just has to organize it. What does need to be specified in the genome, however, is the architecture of the machine that does the organizing.. Optimal learning is the Bayesians’ central goal, and they are in no doubt that they’ve figured out how to reach it. This way, please…. One of the greatest mathematicians of all time, Laplace is perhaps best known for his dream of Newtonian determinism:. [Картинка: pic_20.jpg]. The most popular option, however, is to drown our sorrows in alcohol, get punch drunk, and stumble around all night. The technical term for this isMarkov chain Monte Carlo, or MCMC for short. The“Monte Carlo” part is because the method involves chance, like a visit to the eponymous casino, and the “Markov chain” part is because it involves taking a sequence of steps, each of which depends only on the previous one. The idea in MCMC is to do a random walk, like the proverbial drunkard, jumping from state to state of the network in such a way that, in the long run, the number of times each state is visited is proportional to its probability. We can then estimate the probability of a burglary, say, as the fraction of times we visited a state where there was a burglary. A “well-behaved” Markov chain converges to a stable distribution, so after a while it always gives approximately the same answers. For example, when you shuffle a deck of cards, after a while all card orders are equally likely, no matter the initial order; so you know that if there aren possible orders, the probability of each one is 1/n. The trick in MCMC is to design a Markov chain that converges to the distribution of our Bayesian network. One easy option is to repeatedly cycle through the variables, sampling each one according to its conditional probability given the state of its neighbors. People often talk about MCMC as a kind of simulation, but it’s not: the Markov chain does not simulate any real process; rather, we concocted it to efficiently generate samples from a Bayesian network, which is itself not a sequential model.. A bigger issue is thatk-means only works if the clusters are easy to tell apart: each cluster is roughly a spherical blob in hyperspace, the blobs are far from each other, and they all have similar volumes and include a similar number of objects. If any of these fails, ugly things can happen: an elongated cluster is split into two different ones, a smaller cluster is absorbed into a larger one nearby, and so on. Luckily, there’s a better option.. As technology progresses, an ever more intimate mix of human and machine takes shape. You’re hungry; Yelp suggests some good restaurants. You pick one; GPS gives you directions. You drive; car electronics does the low-level control. We are all cyborgs already. The real story of automation is not what it replaces but what it enables. Some professions disappear, but many more are born.Most of all, automation makes all sorts of things possible that would be way too expensive if done by humans. ATMs replaced some bank tellers, but mainly they let us withdraw money any time, anywhere. If pixels had to be colored one at a time by human animators, there would be noToy Story and no video games.. Judea Pearl’s pioneering work on Bayesian networks appears in his bookProbabilistic Reasoning in Intelligent Systems* (Morgan Kaufmann, 1988).“Bayesian networks without tears,”* by Eugene Charniak (AI Magazine, 1991), is a largely nonmathematical introduction to them.“Probabilistic interpretation for MYCIN’s certainty factors,”* by David Heckerman (Proceedings of the Second Conference on Uncertainty in Artificial Intelligence, 1986), explains when sets of rules with confidence estimates are and aren’t a reasonable approximation to Bayesian networks. “Module networks: Identifying regulatory modules and their condition-specific regulators from gene expression data,” by Eran Segal et al. (Nature Genetics, 2003), is an example of using Bayesian networks to model gene regulation.“Microsoft virus fighter: Spam may be more difficult to stop than HIV,” by Ben Paynter (Fast Company, 2012), tells how David Heckerman took inspiration from spam filters and used Bayesian networks to design a potential AIDS vaccine. The probabilistic or“noisy” OR is explained in Pearl’s book.* “Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base,” by M. A. Shwe et al. (Parts I and II,Methods of Information in Medicine, 1991), describes a noisy-OR Bayesian network for medical diagnosis. Google’s Bayesian network for ad placement is described in Section 26.5.4 of Kevin Murphy’sMachine Learning* (MIT Press, 2012). Microsoft’s player rating system is described in “TrueSkillTM: A Bayesian skill rating system,”* by Ralf Herbrich, Tom Minka, and Thore Graepel (Advances in Neural Information Processing Systems 19, 2007)..