Machine learning is sometimes confused with artificial intelligence (or AI for short). Technically, machine learning is a subfield of AI, but itвЂ™s grown so large and successful that it now eclipses its proud parent. The goal of AI is to teach computers to do what humans currently do better, and learning is arguably the most important of those things: without it, no computer can keep up with a human for long; with it, the rest follows.. 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 yourвЂњhigher brainвЂќ knows that itвЂ™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.. Humans are not immune to overfitting, either. You could even say that itвЂ™s the root cause of a lot of our evils. Consider the little white girl who, upon seeing a Latina baby at the mall, blurted out вЂњLook, Mom, a baby maid!вЂќ (True event.) ItвЂ™s not that sheвЂ™s a natural-born bigot. Rather, she overgeneralized from the few Latina maids she has seen in her shortlife. The world is full of Latinas with other occupations, but she hasnвЂ™t met them yet. Our beliefs are based on our experience, which gives us a very incomplete picture of the world, and itвЂ™s easy to jump to false conclusions. Being smart and knowledgeable doesnвЂ™t immunize you against overfitting, either. Aristotle overfit when he said that it takes a force to keep an object moving. GalileoвЂ™s genius was to intuit that undisturbed objects keep moving without having visited outer space to witness it firsthand.. All humans are mortal.. Learning to cure cancer. HMMs are good for modeling sequences of all kinds, but theyвЂ™re still a far cry from the flexibility of the symbolistsвЂ™IfвЂ¦thenвЂ¦ rules, where anything can appear as an antecedent, and a ruleвЂ™s consequent can in turn be an antecedent in any downstream rule. If we allow such an arbitrary structure in practice, however, the number of probabilities we need to learn blows up. For a long time no one knew how to square this circle, and researchers resorted to ad-hoc schemes, like attaching confidence estimates to rules and somehow combining them. If A implies B with confidence 0.8 and B implies C with confidence 0.7, then perhaps A implies C with confidence 0.8 Г— 0.7.. [РљР°СЂС‚РёРЅРєР°: pic_19.jpg]. We can think of a Bayesian network as aвЂњgenerative model,вЂќ a recipe for probabilistically generating a state of the world: first decide independently whether thereвЂ™s a burglary and/or an earthquake, then based on that decide whether the alarm goes off, and then based on that whether Bob and Claire call. A Bayesian network tells a story: A happened, and it led to B; at the same time, C also happened, and B and C together caused D. To compute the probability of a particular story, we just multiply the probabilities of all of its different strands.. Driverless cars and other robots are a prime example of probabilistic inference in action. As the car drives around, it simultaneously builds up a map of the territory and figures out its location on it with increasing certainty. According to a recent study, London taxi drivers grow a larger posterior hippocampus, a brain region involved in memory and map making, as they learn the layout of the city. Perhaps they use similar probabilistic inference algorithms, with the notable difference that in the case of humans, drinking doesnвЂ™t seem to help.. Generally, the fewer support vectors an SVM selects, the better it generalizes. Any training example that is not a support vector would be correctly classified if it showed up as a test example instead because the frontier between positive and negative examples would still be in the same place. So the expected error rate of an SVM is at most the fraction of examples that are support vectors. As the number of dimensions goes up, this fraction tends to go up as well, so SVMs are not immune to the curse of dimensionality. But theyвЂ™re more resistant to it than most.. We can represent a cluster by its prototypical element: the image of your mother that you see with your mindвЂ™s eye or the quintessential cat, sports car, country house, or tropical beach. Peoria, Illinois, is the average American town, according to marketing lore. Bob Burns, a fifty-three-year-old building maintenance supervisor in Windham, Connecticut, is AmericaвЂ™s most ordinary citizen-at least if you believe Kevin OвЂ™KeefeвЂ™s bookThe Average American. Anything described by numeric attributes-say, peopleвЂ™s heights, weights, girths, shoe sizes, hair lengths, and so on-makes it easy to compute the average member: his height is the average height of all the cluster members, his weight the average of all the weights, and so on. For categorical attributes, like gender, hair color, zip code, or favorite sport, the вЂњaverageвЂќ is simply the most frequent value. The average member described by this set of attributes may or may not be a real person, but either way itвЂ™s a useful reference to have: if youвЂ™re brainstorming how to market a new product, picturing Peoria as the town where youвЂ™re launching it or Bob Burns as your target customer beats thinking of abstract entities like вЂњthe marketвЂќ or вЂњthe consumer.вЂќ. Notice that the network has a separate feature for each pair of people:Alice and Bob both have the flu, Alice and Chris both have the flu, and so on. But we canвЂ™t learn a separate weight for each pair, because we only have one data point per pair (whether itвЂ™s infected or not), and we wouldnвЂ™t be able to generalize to members of the network we havenвЂ™t diagnosed yet (do Yvette and Zach both have the flu?). What we can do instead is learn a single weight for all features of the same form, based on all the instances of it that weвЂ™ve seen. In effect,X and Y have the flu is a template for features that can be instantiated with each pair of acquaintances (Alice and Bob, Alice and Chris, etc.). The weights for all the instances of a template areвЂњtied together,вЂќ in the sense that they all have the same value, and thatвЂ™s how we can generalize despite having only one example (the whole network). In nonrelational learning, the parameters of a model are tied in only one way: across all the independent examples (e.g., all the patients weвЂ™ve diagnosed). In relational learning, every feature template we create ties the parameters of all its instances.. The cure for cancer is a program that inputs the cancerвЂ™s genome and outputs the drug to kill it with. We can now picture what such a program-letвЂ™s call it CanceRx-will look like. Despite its outward simplicity, CanceRx is one of the largest and most complex programs ever built-indeed, so large and complex that it could only have been built with the help of machine learning. It is based on a detailed model of how living cells work, with a subclass for each type of cell in the human body and an overarching model of how they interact. This model, in the form of an MLN or something akin to it, combines knowledge of molecular biology with vast amounts of data from DNA sequencers, microarrays, and many other sources. Some of the knowledge was manually encoded, but most was automatically extracted from the biomedical literature. The model is continually evolving, incorporating the results of new experiments, data sources, and patient histories. Ultimately, it will know every pathway, regulatory mechanism, and chemical reaction in every type of human cell-the sum total of human molecular biology.. Take a moment to consider all the data about you thatвЂ™s recorded on all the worldвЂ™s computers: your e-mails, Office docs, texts, tweets, and Facebook and LinkedIn accounts; your web searches, clicks, downloads, and purchases; your credit, tax, phone, and health records; your Fitbit statistics; your driving as recorded by your carвЂ™s microprocessors; your wanderings as recorded by your cell phone; all the pictures of you ever taken; brief cameos on security cameras; your Google Glass snippets-and so on and so forth. If a future biographer had access to nothing but this вЂњdata exhaustвЂќ of yours, what picture of you would he form? Probablya quite accurate and detailed one in many ways, but also one where some essential things would be missing. Why did you, one beautiful day, decide to change careers? Could the biographer have predicted it ahead of time? What about that person you met one day and secretly never forgot? Could the biographer wind back through the found footage and say вЂњAh, thereвЂќ?. IвЂ™m indebted to the organizations that have funded my research over the years, including ARO, DARPA, FCT, NSF, ONR, Ford, Google, IBM, Kodak, Yahoo, and the Sloan Foundation..