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If programmers are minor gods, the complexity monster is the devil himself. Little by little, it’s winning the war.. By taking automation to new heights, the machine-learning revolution will cause extensive economic and social changes, just as the Internet, the personal computer, the automobile, and the steam engine did in their time. One area where these changes are already apparent is business.. Where are we headed?. Therefore Socrates is mortal.. A decision tree is like playing a game of twenty questions with an instance. Starting at the root, each node asks about the value of one attribute, and depending on the answer, we follow one or another branch. When we arrive at a leaf, we read off the predicted concept. Each path from the root to a leaf corresponds to a rule. If this reminds you of those annoying phone menus you have to get through when you call customer service, it’s not an accident: a phone menu is a decision tree. The computer on the other end of the line is playing a game of twenty questions with you to figure out what you want, and each menu is a question.. Because of all this, genetic algorithms are much less likely than backprop to get stuck in a local optimum and in principle better able to come up with something truly new. But they are also much more difficult to analyze. How do we know a genetic algorithm will get somewhere meaningful instead of randomly walking around like the proverbial drunkard? The key is to think in terms of building blocks. Every subset of a string’s bits potentially encodes a useful building block, and when we cross over two strings, those building blocks come together into a larger one, which in turn becomes grist for the mill. Holland likes to use police sketches to illustrate the power of building blocks. In the days before computers, a police artist could quickly put together a portrait of a suspect from eyewitness interviews by selecting a mouth from a set of paper strips depicting typical mouth shapes and doing the same for the eyes, nose, chin, and so on. With only ten building blocks and ten options for each, this system would allow for ten billion different faces, more than there are people on Earth.. P ( A|B ) = P ( A ) P(B|A) / P(B). A Markov network is a set of features and corresponding weights, which together define a probability distribution. A feature can be as simple asThis is a ballad or as elaborate asThis is a ballad by a hip-hop artist, with a saxophone riff and a descending chord progression. Pandora uses a large set of features, which it calls the Music Genome Project, to select songs to play for you. Suppose we plug them into a Markov network. If you like ballads, the weight of the corresponding feature goes up, and you’re more likely to hear ballads when you turn on Pandora. If you also like songs by hip-hop artists, that feature’s weight also goes up. The songs you’re most likely to hear are now ones that have both features, namely ballads by hip-hop artists. If you don’t like ballads or hip-hop artistsper se, but only enjoy them in combination, the more elaborate featureBallad by a hip-hop artist is what you need. Pandora’s features are handcrafted, but in Markov networks we can also learn features using hill climbing, similar to rule induction. Either way, gradient descent is a good way to learn the weights.. Imagine for a moment trying to pull off such a stunt. You sneak into an absent doctor’s office, and before long a patient comes in and tells you all his symptoms. Now you have to diagnose him, except you know nothing about medicine. All you have is a cabinet full of patient files: their symptoms, diagnoses, treatments undergone, and so on. What do you do? The easiest way out is to look in the files for the patient whose symptoms most closely resemble your current one’s and make the same diagnosis. If your bedside manner is as convincing as Abagnale’s, that might just do the trick. The same idea applies well beyond medicine. If you’re a young president faced with a world crisis, as Kennedy was when a US spy plane revealed Soviet nuclear missiles being deployed in Cuba, chances are there’s no script ready to follow. Instead, you look for historical analogs of the current situation and try to learn from them. The Joint Chiefs of Staff urged an attack on Cuba, butKennedy, having just readThe Guns of August, a best-selling account of the outbreak of World War I, was keenly aware of how easily that could escalate into all-out war. So he opted for a naval blockade instead, perhaps saving the world from nuclear war.. Notice how reinforcement learners face the same exploration-exploitation dilemma we met in Chapter 5: to maximize your rewards, you’ll naturally want to always pick the action leading to the highest-value state, but that prevents you from potentially discovering even higher rewards elsewhere. Reinforcement learners solve this by sometimes choosing the best action and sometimes a random one. (The brain even seems to have a “noise generator” for this purpose.) Early on, when there’s much to learn, it makes sense to explore a lot. Once you know the territory, it’s best to concentrate on exploiting it. That’s what humans do over their lifetimes: children explore, and adults exploit (except for scientists, who areeternal children). Children’s play is a lot more serious than it looks; if evolution made a creature that is helpless and a heavy burden on its parents for the first several years of its life, that extravagant cost must be for the sake of an even bigger benefit. In effect, reinforcement learning is a kind of speeded-up evolution-trying, discarding, and refining actions within a single lifetime instead of over generations-and by that standard it’s extremely efficient.. To learn is to get better with practice. You may barely remember it now, but learning to tie your shoelaces was really hard. At first you couldn’t do it at all, despite your five years of age. Then your laces probably came undone faster than you could tie them. But little by little you learned to tie them faster and better until it became completely automatic. The same happens with lots of other things, like crawling, walking, running, riding a bike, and driving a car; reading, writing, and arithmetic; playing an instrument and practicing a sport; cooking and using a computer. Ironically, you learn the most when it’s most painful: early on, when every step is difficult, you keep failing, and even when you succeed, the results arenot very pretty. After you’ve mastered your golf swing or tennis serve, you can spend years perfecting it, but all those years make less difference than the first few weeks did. You get better with practice, but not at a constant rate: at first you improve quickly, then not so quickly, then very slowly. Whether it’s playing games or the guitar, the curve of performance improvement over time-how well you do something or how long it takes you to do it-has a very specific form:. This type of curve is called a power law, because performance varies as time raised to some negative power. For example, in the figure above, time to completion is proportional to the number of trials raised to minus two (or equivalently, one over the number of trials squared). Pretty much every human skill follows a power law, with different powers for different skills. (In contrast, Windows never gets faster with practice-something for Microsoft to work on.). Most of all, though, Alchemy addresses the problems that each of the five tribes of machine learning has worked on for so long. Let’s look at each of them in turn.. 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.. In the Land of Learning where the Data lies..