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can learning be used as a noun is kinaesthetic learning

If you’re a citizen or policy maker concerned with the social and political issues raised by big data and machine learning, this book will give you a primer on the technology-what it is, where it’s taking us, what it does and doesn’t make possible-without boring you with all the ins and outs. From privacy to the future of work and the ethics of roboticized warfare, we’ll see where the real issues are and how to think about them.. Enter the learner. Big data is no use if you can’t turn it into knowledge, however, and there aren’t enough scientists in the world for the task. Edwin Hubble discovered new galaxies by poring over photographic plates, but you can bet the half-billion sky objects in the Sloan Digital Sky Survey weren’t identified that way. It would be liketrying to count the grains of sand on a beach by hand. You can write rules to distinguish galaxies from stars from noise objects (such as birds, planes, Superman), but they’re not very accurate. Instead, the SKICAT (sky image cataloging and analysis tool) project used a learning algorithm. Starting from plates where objects were labeled with the correct categories, it figured out what characterizes each one and applied the result to all the unlabeled plates. Even better, it could classify objects that were too faint for humans to label, and these comprise the majority of the survey.. In thePrincipia, along with his three laws of motion, Newton enunciates four rules of induction. Although these are much less well known than the physical laws, they are arguably as important. The key rule is the third one, which we can paraphrase thus:. Accuracy you can believe in. These can be organized into the following decision tree:. Symbolist machine learning is an offshoot of the knowledge engineering school of AI. In the 1970s, so-called knowledge-based systems scored some impressive successes, and in the 1980s they spread rapidly, but then they died out. The main reason they did was the infamous knowledge acquisition bottleneck: extracting knowledge from experts and encoding it as rules is just too difficult, labor-intensive, and failure-prone to be viable for most problems. Letting the computer automatically learn to, say, diagnose diseases by looking at databases of past patients’ symptoms and the corresponding outcomes turned out to be much easier than endlessly interviewing doctors. Suddenly, the work of pioneers like Ryszard Michalski, Tom Mitchell, and Ross Quinlan had a new relevance, and the field hasn’t stopped growing since. (Another important problem was that knowledge-based systems had trouble dealing with uncertainty, of which more in Chapter 6.). 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.. This is an instance of a tension that runs throughout much of science and philosophy: the split between descriptive and normative theories, between“this is how it is” and “this is how it should be.” Symbolists and Bayesians like to point out, however, that figuring out how we should learn can also help us to understand how we do learn because the two are presumably not entirely unrelated-far from it. In particular, behaviors that are important for

survival and have had a long time to evolve should not be far from optimal. We’re not very good at answering written questions about probabilities, but we are very good at instantly choosing hand and arm movements to hit a target. Many psychologists have used symbolist or Bayesian models to explain aspects of human behavior. Symbolists dominated the first few decades of cognitive psychology. In the 1980s and 1990s, connectionists held sway, but now Bayesians are on the rise.. You wake up in a cold sweat. Lying on your lap is a book entitledThe Master Algorithm. Shaking off the nightmare, you resume reading where you had left off.. Notice also that it’s only thanks to Bayes’ theorem that we were able to pull off this trick. If we wanted to directly estimateP(flu | fever, cough, etc.), without first turning it intoP(fever, cough, etc. | flu) using the theorem, we’d still need an exponential number of probabilities, one for each combination of symptoms and flu/not flu.. You’d think that Bayesians and symbolists would get along great, given that they both believe in a first-principles approach to learning, rather than a nature-inspired one. Far from it. Symbolists don’t like probabilities and tell jokes like “How many Bayesians does it take to change a lightbulb? They’re not sure. Come to think of it, they’re not sure the lightbulb is burned out.” More seriously, symbolists point to the high price we pay for probability. Inference suddenly becomes a lot more expensive, all those numbers are hard to understand, we have to deal with priors, and hordes of zombie hypotheses chase us around forever. The ability to compose pieces of knowledge on the fly, so dear to symbolists, is gone. Worst of all, we don’t know how to put probability distributions on many of the things we need to learn. A Bayesian network is a distribution over a vector of variables, but what about distributions over networks, databases, knowledge bases, languages, plans, and computer programs, to name a few? All of these are easily handled in logic, and an algorithm that can’t learn them is clearly not the Master Algorithm.. Evolution, part 2. Case-Based Reasoning,* by Janet Kolodner (Morgan Kaufmann, 1993), is a textbook on the subject.“Using case-based retrieval for customer technical support,”* by Evangelos Simoudis (IEEE Expert, 1992), explains its application to help desks. IPsoft’s Eliza is described in “Rise of the software machines” (Economist, 2013) and on the company’s website. Kevin Ashley explores case-based legal reasoning inModeling Legal Arguments* (MIT Press, 1991). David Cope summarizes his approach to automated music composition in“Recombinant music: Using the computer to explore musical style” (IEEE Computer, 1991). Dedre Gentner proposed structure mapping in“Structure mapping: A theoretical framework for analogy”* (Cognitive Science, 1983).“The man who would teach machines to think,” by James Somers (Atlantic, 2013), discusses Douglas Hofstadter’s views on AI.. “Love, actuarially,” by Kevin Poulsen (Wired, 2014), tells the story of how one man used machine learning to find love on the OkCupid dating site.Dataclysm, by Christian Rudder (Crown, 2014), mines OkCupid’s data for sundry insights.Total Recall, by Gordon Moore and Jim Gemmell (Dutton, 2009), explores the implications of digitally recording everything we do.The Naked Future, by Patrick Tucker (Current, 2014), surveys the use and abuse of data for prediction in our world. Craig Mundie argues for a balanced approach to data collection and use in“Privacy pragmatism” (Foreign Affairs, 2014).The Second Machine Age, by Erik Brynjolfsson and Andrew McAfee (Norton, 2014), discusses how progress in AI will shape the future of work and the economy.“World War R,” by Chris Baraniuk (New Scientist, 2014) reports on the debate surrounding the use of robots in battle.“Transcending complacency on superintelligent machines,” by Stephen Hawking et al. (Huffington Post, 2014), argues that now is the time to worry about AI’s risks. Nick Bostrom’sSuperintelligence (Oxford University Press, 2014) considers those dangers and what to do about them..

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