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Machine learning takes many different forms and goes by many different names: pattern recognition, statistical modeling, data mining, knowledge discovery, predictive analytics, data science, adaptive systems, self-organizing systems, and more. Each of these is used by different communities and has different associations. Some have a long half-life, some less so. In this book I use the termmachine learning to refer broadly to all of them.. Cyberwar is an instance of asymmetric warfare, where one side can’t match the other’s conventional military power but can still inflict grievous damage. A handful of terrorists armed with little more than box cutters can knock down the Twin Towers and kill thousands of innocents. All the biggest threats to US security today are in the realm of asymmetric warfare, and there’s an effective weapon against all of them: information. If the enemy can’t hide, he can’t survive. The good news is that we have plenty of information, and that’s also the bad news.. Here’s one way. Suspend your disbelief and start by assuming that all matches are good. Then try excluding all matches that don’t have some attribute. Repeat this for each attribute, and choose the one that excludes the most bad matches and the fewest good ones. Your definition now looks something like, say, “It’s a good match only if he’s outgoing.” Now try adding every other attribute to that in turn, and choose the one that excludes the most remaining bad matches and fewest remaining good ones. Perhaps the definition is now “It’s a good match only if he’s outgoing and so is she.” Try adding a third attribute to those two, and so on. Once you’ve excluded all the bad matches, you’re done: you have a definition of the concept that includes all the positive examples and excludes all the negative ones. For example: “A couple is a good match only if they’re both outgoing, he’s a dog person, and she’s not a cat person.” You can now throw away the data and keep only this definition, since it encapsulates all that’s relevant for your purposes. This algorithm is guaranteed to finish in a reasonable amount of time, and it’s also the first actual learner we meet in this book!. All philosophers are human.. The symbolists’ core belief is that all intelligence can be reduced to manipulating symbols. A mathematician solves equations by moving symbols around and replacing symbols by other symbols according to predefined rules. The same is true of a logician carrying out deductions. According to this hypothesis, intelligence is independent of the substrate; it doesn’t matter if the symbol manipulations are done by writing on a blackboard, switching transistors on and off, firing neurons, or playing with Tinkertoys. If you have a setup with the power of a universal Turing machine, you can do anything. Softwarecan be cleanly separated from hardware, and if your concern is figuring out how machines can learn, you (thankfully) don’t need to worry about the latter beyond buying a PC or cycles on Amazon’s cloud.. A complete model of a cell. 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.. All models are wrong, but some are useful. The breakthrough came in the early 1980s, when Judea Pearl, a professor of computer science at the University of California, Los Angeles, invented a new representation: Bayesian networks. Pearl is one of the most distinguished computer scientists in the world, his methods having swept through machine learning, AI, and many other fields. He won the Turing Award, the Nobel Prize of computer science, in 2012.. One of the most exciting applications of Bayesian networks is modeling how genes regulate each other in living cells. Billions of dollars have been spent trying to discover pairwise correlations between individual genes and specific diseases, but the yield has been disappointingly low. In retrospect, this is not so surprising: a cell’s behavior is the result of complex interactions among genes and the environment, and a single gene has limited predictive power. But with Bayesian networks, we can uncover these interactions, provided we have the requisite data, and with the spread of DNA microarrays, we increasingly do.. Learning the Bayesian way. All is not lost, however. The first thing we can do is get rid of the irrelevant dimensions. Decision trees do this automatically by computing the information gain of each attribute and using only the most informative ones. For nearest-neighbor, we can accomplish something similar by first discarding all attributes whose information gain is below some threshold and then measuring similarity only in the reduced space. This is quick and good enough for some applications, but unfortunately it precludes learning many concepts, like exclusive-OR: if an attribute only says something about the class when combined with others, but not on its own, it will be discarded. A more expensive but smarter option is to“wrap” the attribute selection around the learner itself, with a hill-climbing search that keeps deleting attributes as long as that doesn’t hurt nearest-neighbor’s accuracy on held-out data. Newton did a lot of attribute selection when he decided that all that matters for predicting an object’s trajectory is its mass-not its color, smell, age, or myriad other properties. In fact, the most important thing about an equation is all the quantities that don’t appear in it: once we know what the essentials are, figuring out how they depend on each other is often the easier part.. [Картинка: pic_25.jpg]. Here’s an interesting experiment. Take the video stream from Robby’s eyes, treat each frame as a point in the space of images, and reduce that set of images to a single dimension. What will you discover? Time. Like a librarian arranging books on a shelf, time places each image next to its most similar ones. Perhaps our perception of it is just a natural result of our brains’ dimensionality reduction prowess. In the road network of memory, time is the main thoroughfare, and we soon find it. Time, in other words, is the principal component of memory.. Three Algorithms for the Scientists under the sky,.