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Even if by some miracle we managed to finish coding up all the necessary pieces, our troubles would be just beginning. Over the years, a number of research groups have attempted to build complete intelligent agents by putting together algorithms for vision, speech recognition, language understanding, reasoning, planning, navigation, manipulation, and so on. Without a unifying framework, these attempts soon hit an insurmountable wall of complexity: too many moving parts, too many interactions, too many bugs for poor human software engineers to cope with. Knowledge engineers believe AI is just an engineering problem, but we have not yet reached the point where engineering can take us the rest of the way. In 1962, when Kennedy gave his famous moon-shot speech, going to the moon was an engineering problem. In 1662, it wasn’t, and that’s closer to where AI is today.. Another potential source of objections to the Master Algorithm is the notion, popularized by the psychologist Jerry Fodor, that the mind is composed of a set of modules with only limited communication between them. For example, when you watch TV your“higher brain” knows that it’s only light flickering on a flat surface, but your visual system still sees three-dimensional shapes. Even if we believe in the modularity of mind, however, that does not imply that different modules use different learning algorithms. The same algorithm operatingon, say, visual and verbal information may suffice.. How complex will the Master Algorithm be? Thousands of lines of code? Millions? We don’t know yet, but machine learning has a delightful history of simple algorithms unexpectedly beating very fancy ones. In a famous passage of his bookThe Sciences of the Artificial, AI pioneer and Nobel laureate Herbert Simon asked us to consider an ant laboriously making its way home across a beach. The ant’s path is complex, not because the ant itself is complex but because the environment is full of dunelets to climb and pebbles to get around. If we tried to model the ant by programming in every possible path, we’d be doomed. Similarly, in machine learning the complexity is in the data; all theMaster Algorithm has to do is assimilate it, so we shouldn’t be surprised if it turns out to be simple. The human hand is simple-four fingers, one opposable thumb-and yet it can make and use an infinite variety of tools. The Master Algorithm is to algorithms what the hand is to pens, swords, screwdrivers, and forks.. The problem is not limited to memorizing instances wholesale. Whenever a learner finds a pattern in the data that is not actually true in the real world, we say that it has overfit the data. Overfitting is the central problem in machine learning. More papers have been written about it than about any other topic. Every powerful learner, whether symbolist, connectionist, or any other, has to worry about hallucinating patterns. The only safe way to avoid it is to severely restrict what the learner can learn, for example by requiring that it be a short conjunctive concept. Unfortunately, that throws out the baby with the bathwater, leaving the learner unable to see most of the true patterns that are visible in the data. Thus a good learner is forever walking the narrow path between blindness and hallucination.. Learning to cure cancer. If you’re against cutting taxes, you’re a Democrat.. [Картинка: pic_12.jpg]. However, that doesn’t mean their chemical behavior is similar. Methane is a gas, while methanol is an alcohol. The second part of analogical reasoning is figuring out what we can infer about the new object based on similar ones we’ve found. This can be very simple or very complex. In nearest-neighbor or SVMs, it just consists of predicting the new object’s class based on the classes of the nearest neighbors or support vectors. But in case-based reasoning, another type of analogical learning, the output can be a complex structure formed by composing parts of the retrieved objects. Suppose your HP printer is spewing out gibberish, and you call up their help desk. Chances are they’ve seen your problem many times before, so a good strategy is to find those records and piece together a potential solution for your problem from them. This is not just a matter of finding complaints with many similar attributes to yours: for example, whether you’re using your printer with Windows or Mac OS X may cause very different settings of the system and the printer to become relevant. And once you’ve found the most relevant cases, the sequence of steps needed to solve your problem may be a combination of steps from different cases, with some further tweaks specific to yours.. The question, of course, is what algorithm should be running in Robby’s brain at birth. Researchers influenced by child psychology look askance at neural networks because the microscopic workings of a neuron seem a million miles from the sophistication of even a child’s most basic behaviors, like reaching for an object, grasping it, and inspecting it with wide, curious eyes. We need to model the child’s learning at a higher level of abstraction, lest we miss the planet for the trees. Above all, even though children certainly get plenty of help from their parents, they learn mostly on their own, without supervision, and that’s what seems most miraculous. None of the algorithms we’ve seen so far can do it, but we’re about to see several that can-bringing us one step closer to the Master Algorithm.. Principal-component analysis (PCA), as this process is known, is one of the key tools in the scientist’s toolkit. You could say PCA is to unsupervised learning what linear regression is to the supervised variety. The famous hockey-stick curve of global warming, for example, is the result of finding the principal component of various temperature-related data series (tree rings, ice cores, etc.) and assuming it’s the temperature. Biologists use PCA to summarize the expression levels of thousands of different genes into a few pathways. Psychologists have found that personality boils down to five dimensions-extroversion, agreeableness, conscientiousness, neuroticism, and openness to experience-which they can infer from your tweets and blog posts. (Chimps supposedly have one more dimension-reactivity-but Twitter data for them is not available.) Applying PCA to congressional votes and poll data shows that, contrary to popular belief, politics is not mainly about liberals versus conservatives. Rather, people differ along two main dimensions: one for economic issues and one for social ones. Collapsing these into a single axis mixes together populists and libertarians, who are polar opposites, and creates the illusion of lots of moderates in the middle. Trying to appeal to them is an unlikely winning strategy. On the other hand, if liberals and libertarians overcame their mutual aversion, they could ally themselves on social issues, where both favor individual freedom.. Of reinforcement learning’s founders, Rich Sutton is the most gung ho. For him, reinforcement learning is the Master Algorithm and solving it is tantamount to solving AI. Chris Watkins, on the other hand, is dissatisfied. He sees many things children can do that reinforcement learners can’t: solve problems, solve them better after a few attempts, make plans, acquire increasingly abstract knowledge. Luckily, we also have learning algorithms for these higher-level abilities, the most important of which is chunking.. The neatest trick a relational learner can do is to turn a sporadic teacher into an assiduous one. For an ordinary classifier, examples without classes are useless. If I’m given a patient’s symptoms, but not the diagnosis, that doesn’t help me learn to diagnose. But if I know that some of the patient’s friends have the flu, that’s indirect evidence that he may have the flu as well. Diagnosing a few people in a network and then propagating those diagnosesto their friends, and their friends’ friends, is the next best thing to diagnosing everyone. The inferred diagnoses may be noisy, but the overall statistics of how symptoms correlate with the flu will probably be a lot more accurate and complete than if I had only a handful of isolated diagnoses to draw on. Children are very good at making the most of the sporadic supervision they get (provided they don’t choose to ignore it). Relational learners share some of that ability.. One for the Dark AI on its dark throne,. The third and perhaps biggest worry is that, like the proverbial genie, the machines will give us what we ask for instead of what we want. This is not a hypothetical scenario; learning algorithms do it all the time. We train a neural network to recognize horses, but it learns instead to recognize brown patches, because all the horses in its training set happened to be brown. You just bought a watch, so Amazon recommends similar items: other watches, which are now the last thing you want to buy. If you examine all the decisions that computers make today-who gets credit, for example-you’ll find that they’re often needlessly bad. Yours would be too, if your brain was a support vector machine and all your knowledge of credit scoring came from perusing one lousy database. People worry that computers will get too smart and take over the world, but the real problem is that they’re too stupid and they’ve already taken over the world.. The history of attempts to combine probability and logic is surveyed in a 2003 special issue* of theJournal of Applied Logic devoted to the subject, edited by Jon Williamson and Dov Gabbay.“From knowledge bases to decision models,”* by Michael Wellman, John Breese, and Robert Goldman (Knowledge Engineering Review, 1992), discusses some of the early AI approaches to the problem..