Not all learning algorithms work the same, and the differences have consequences. Take AmazonвЂ™s and NetflixвЂ™s recommenders, for example. If each were guiding you through a physical bookstore, trying to determine whatвЂ™s вЂњright for you,вЂќ Amazon would be more likely to walk you over to shelves youвЂ™ve frequented previously; Netflix would take you to unfamiliar and seemingly odd sections of the store but lead you to stuff youвЂ™d end up loving. In this book weвЂ™ll see the different kinds of algorithms that companies like Amazon and Netflix use. NetflixвЂ™s algorithm has a deeper (even if still quite limited) understanding of your tastes than AmazonвЂ™s, but ironically that doesnвЂ™t mean Amazon would be better off using it. NetflixвЂ™s business model depends on driving demand into the long tail of obscure movies and TV shows, which cost it little, and away from the blockbusters, which your subscription isnвЂ™t enough to pay for. Amazon has no such problem; although itвЂ™s well placed to take advantage of the long tail, itвЂ™s equally happy to sell you more expensive popular items, which also simplify its logistics. And we, as customers, are more willing to take a chance on an odd item if we have a subscription than if we have to pay for it separately.. Another line of argument for the unity of the cortex comes from what might be called the poverty of the genome. The number of connections in your brain is over a million times the number of letters in your genome, so itвЂ™s not physically possible for the genome to specify in detail how the brain is wired.. ItвЂ™s true that some things are predictable and some arenвЂ™t, and the first duty of the machine learner is to distinguish between them. But the goal of the Master Algorithm is to learn everything thatcan be known, and thatвЂ™s a vastly wider domain than Taleb and others imagine. The housing bust was far from a black swan; on the contrary, it was widely predicted. Most banksвЂ™ models failed to see it coming, but that was due to well-understood limitations of those models, not limitations of machine learning in general. Learning algorithms are quite capable of accurately predicting rare, never-before-seen events; you could even say that thatвЂ™s what machine learning is all about. WhatвЂ™s the probability of a black swan if youвЂ™ve never seen one? How about itвЂ™s the fraction of known species that belatedly turned out to have black specimens? This is only a crude example; weвЂ™ll see many deeper ones in this book.. 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.. And so on.. More generally, inverse deduction is a great way to discover new knowledge in biology, and doing that is the first step in curing cancer. According to the Central Dogma, everything that happens in a living cell is ultimately controlled by its genes, via the proteins whose synthesis they initiate. In effect, a cell is like a tiny computer, and DNA is the program running on it: change the DNA, and a skin cell can become a neuron or a mouse cell can turn into a human one. In a computer program, all bugs are the programmerвЂ™s fault. But in a cell, bugs can arise spontaneously, when radiation or a copying error changes a gene into a different one, a gene is accidentally copied twice, and so on. Most of the time these mutations cause the cell to die silently, but sometimes the cell starts to grow and divide uncontrollably and a cancer is born.. If all inputs have a weight of one and the threshold is half the number of inputs, then the perceptron fires if more than half its inputs fire. In other words, the perceptron is a like a tiny parliament where the majority wins. (Or perhaps not so tiny, considering it can have thousands of members.) ItвЂ™s not altogether democratic, though, because in general not everyone has an equal vote. A neural network is more like a social network, where a few close friends count for more than thousands of Facebook ones. And itвЂ™s the friends you trust most that influence you the most. If a friend recommends a movie and you go see it and like it, next time around youвЂ™ll probably follow her advice again. On the other hand, if she keeps gushing about movies you didnвЂ™t enjoy, you will start to ignore her opinions (and perhaps your friendship even wanes a bit).. One of HollandвЂ™s more remarkable students was John Koza. In 1987, while
flying back to California from a conference in Italy, he had a lightbulb moment. Instead of evolving comparatively simple things likeIfвЂ¦thenвЂ¦ rules and gas pipeline controllers, why not evolve full-blown computer programs? And if thatвЂ™s the goal, why stick with bit strings as the representation? A program is really a tree of subroutine calls, so better to directly cross over those subtrees than to shoehorn them into bit strings andrun the risk of destroying perfectly good subroutines when you cross them over at a random point.. The molecular biology of living cells is such a mess that molecular biologists often quip that only people who donвЂ™t know any of it could believe in intelligent design. The architecture of the brain may well have similar faults-the brain has many constraints that computers donвЂ™t, like very limited short-term memory-and thereвЂ™s no reason to stay within them. Moreover, we know of many situations where humans seem to consistently do the wrong thing, as Daniel Kahneman illustrates at length in his bookThinking, Fast and Slow.. [РљР°СЂС‚РёРЅРєР°: pic_24.jpg]. RISE was a step toward the Master Algorithm because it combined symbolic and analogical learning. It was only a small step, however, because it doesnвЂ™t have the full power of either of those paradigms, and itвЂ™s still missing the other three. RISEвЂ™s rules canвЂ™t be chained together in different ways; each rule just predicts the class of an example directly from its attributes. Also, the rules canвЂ™t talk about more than one entity at a time; for example, RISE canвЂ™t express a rule likeIf A has the flu and B was in contact with A, B may have the flu as well. On the analogical side, RISE just generalizes the simple nearest-neighbor algorithm; it canвЂ™t learn across domains using structure mapping or some such strategy. At the time I finished my PhD, I didnвЂ™t see a way to bring together in one algorithm the full power of all the five paradigms, and I set the problem aside for a while. But as I applied machine learning to problems like word-of-mouth marketing, data integration, programming by example, and website personalization, I kept seeing how each of the paradigms provided only part of the solution. There had to be a better way.. Organizing the world into objects and categories is second nature to an adult but not to an infant, and even less to Robby the robot. We could endow him with a visual cortex in the form of a multilayer perceptron and show him labeled examples of all the objects and categories in the world-hereвЂ™s Mommy close up, hereвЂ™s Mommy far away-but weвЂ™d never be done. What we need is an algorithm that will spontaneously group together similar objects, or different images of the same object. This is the problem of clustering, and itвЂ™s one of the most intensively studied in machine learning.. A company like this could quickly become one of the most valuable in the world. As Alexis Madrigal of theAtlantic points out, today your profile can be bought for half a cent or less, but the value of a user to the Internet advertising industry is more like $1,200 per year. GoogleвЂ™s sliver of your data is worth about $20, FacebookвЂ™s $5, and so on. Add to that all the slivers that no one has yet, and the fact that the whole is more than the sum of the parts-a model of you based on all your data is much better than a thousand models based on a thousand slivers-and weвЂ™relooking at easily over a trillion dollars per year for an economy the size of the United States. It doesnвЂ™t take a large cut of that to make a Fortune 500 company. If you decide to take up the challenge and wind up becoming a billionaire, remember where you first got the idea.. War is not for humans. вЂњThe unreasonable effectiveness of data,вЂќ by Alon Halevy, Peter Norvig, and Fernando Pereira (IEEE Intelligent Systems, 2009), argues for machine learning as the new discovery paradigm. BenoГ®t Mandelbrot explores the fractal geometry of nature in the eponymous book* (Freeman, 1982). James GleickвЂ™sChaos (Viking, 1987) discusses and depicts the Mandelbrot set. The Langlands program, a research effort that seeks to unify different subfields of mathematics, is described inLove and Math, by Edward Frenkel (Basic Books, 2014).The Golden Ticket, by Lance Fortnow (Princeton University Press, 2013), is an introduction to NP-completeness and the P = NP problem.The Annotated Turing,* by Charles Petzold (Wiley, 2008), explains Turing machines by revisiting TuringвЂ™s original paper on them..