Last but not least, if you have an appetite for wonder, machine learning is an intellectual feast, and youвЂ™re invited-RSVP!. Out in cyberspace, learning algorithms man the nationвЂ™s ramparts. Every day, foreign attackers attempt to break into computers at the Pentagon, defense contractors, and other companies and government agencies. Their tactics change continually; what worked against yesterdayвЂ™s attacks is powerless against todayвЂ™s. Writing code to detect and blockeach one would be as effective as the Maginot Line, and the PentagonвЂ™s Cyber Command knows it. But machine learning runs into a problem if an attack is the first of its kind and there arenвЂ™t any previous examples of it to learn from. Instead, learners build models of normal behavior, of which thereвЂ™s plenty, and flag anomalies. Then they call in the cavalry (aka system administrators). If cyberwar ever comes to pass, the generals will be human, but the foot soldiers will be algorithms. Humans are too slow and too few and would be quickly swamped by an army of bots. We need our own bot army, and machine learning is like West Point for bots.. Not all neuroscientists believe in the unity of the cortex; we need to learn more before we can be sure. The question of just what the brain can and canвЂ™t learn is also hotly debated. But if thereвЂ™s something we know but the brain canвЂ™t learn, it must have been learned by evolution.. As Isaiah Berlin memorably noted, some thinkers are foxes-they know many small things-and some are hedgehogs-they know one big thing. The same is true of learning algorithms. I hope the Master Algorithm is a hedgehog, but even if itвЂ™s a fox, we canвЂ™t catch it soon enough. The biggest problem with todayвЂ™s learning algorithms is not that they are plural; itвЂ™s that, useful as they are, they still donвЂ™t do everything weвЂ™d like them to. Before we can discover deep truths with machine learning, we have to discover deep truths about machine learning.. The deeper problem, however, is that most learners start out knowing too little, and no amount of knob-twiddling will get them to the finish line. Without the guidance of an adult brainвЂ™s worth of knowledge, they can easily go astray. Even though itвЂ™s what most learners do, just assuming you know the form of the truth (for example, that itвЂ™s a small set of rules) is not much to hang your hat on. A strict empiricist would say that thatвЂ™s all a newborn has, encoded in her brainвЂ™s architecture, and indeed children overfit more than adults do, but we would like to learn faster than a child does. (Eighteen years is a long time, and thatвЂ™s not counting college.) The Master Algorithm should be able to start with a large body of knowledge, whether it was provided by humans or learned in previous runs, and use it to guide new generalizations from data. ThatвЂ™s what scientists do, and itвЂ™s as far as it gets from a blank slate. The вЂњdivide and conquerвЂќ rule induction algorithm canвЂ™t do it, but thereвЂ™s another way to learn rules that can.. 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 youвЂ™re for cutting taxes and pro-life, youвЂ™re a Republican.. For the hardest problems-the ones we really want to solve but havenвЂ™t been able to, like curing cancer-pure nature-inspired approaches are probably too uninformed to succeed, even given massive amounts of data. We can in principle learn a complete model of a cellвЂ™s metabolic networks by a combination of structure search, with or without crossover, and parameter learning via backpropagation, but there are too many bad local optima to get stuck in. We need to reason with larger chunks, assembling and reassembling them as needed and using inverse deduction to fill in the gaps. And we need our learning to be guided by the goal of optimally diagnosing cancer and finding the best drugs to cure it.. On the downside, MCMC is often excruciatingly slow to converge, or fools you by looking like itвЂ™s converged when it hasnвЂ™t. Real probability distributions are usually very peaked, with vast wastelands of minuscule probability punctuated by sudden Everests. The Markov chain then converges to the nearest peak and stays there, leading to very biased probability estimates. ItвЂ™s as if the drunkard followed the scent of alcohol to the nearest tavern and stayed there all night, instead of wandering all around the city like we wanted him to. On the other hand, if instead of using a Markov chain we just generated independent samples, like simpler Monte Carlo methods do, weвЂ™d have no scent to follow and probably wouldnвЂ™t even find that first tavern; it would be like throwing darts at a map of the city, hoping they land smack dab on the pubs.. The Bayesian method is not just applicable to learning Bayesian networks and their special cases.
(Conversely, despite their name, Bayesian networks arenвЂ™t necessarily Bayesian: frequentists can learn them, too, as we just saw.) We can put a prior distribution on any class of hypotheses-sets of rules, neural networks, programs-and then update it with the hypothesesвЂ™ likelihood given the data. BayesiansвЂ™ view is that itвЂ™s up to you what representation you choose, but then you have to learn it using BayesвЂ™ theorem. In the 1990s, they mounted a spectacular takeover of the Conference on Neural Information Processing Systems (NIPS for short), the main venue for connectionist research. The ringleaders (so to speak) were David MacKay, Radford Neal, and Michael Jordan. MacKay, a Brit who was a student of John HopfieldвЂ™s at Caltech and later became chief scientific advisor to the UKвЂ™s Department of Energy, showed how to learn multilayer perceptrons the Bayesian way. Neal introduced the connectionists to MCMC, and Jordan introduced them to variational inference. Finally, they pointed out that in the limit you could вЂњintegrate outвЂќ the neurons in a multilayer perceptron, leaving a type of Bayesian model that made no reference to them. Before long, the wordneural in the title of a paper submitted to NIPS became a good predictor of rejection. Some researchers joked that the conference should change its name to BIPS, for Bayesian Information Processing Systems.. [РљР°СЂС‚РёРЅРєР°: pic_24.jpg]. Analogizers learn by hypothesizing that entities with similar known properties have similar unknown ones: patients with similar symptoms have similar diagnoses, readers who bought the same books in the past will do so again in the future, and so on. MLNs can represent similarity between entities with formulas likePeople with the same tastes buy the same books. Then the more of the same books Alice and Bob have bought, the more likely they are to have the same tastes, and (applying the same formula in the opposite direction) the more likely Alice is to buy a book if Bob also did. Their similarity is represented by their probability of having the same tastes. To make this really useful, we can have different weights for different instances of the same rule: if Alice and Bob both bought a certain rare book, this is probably more informative than if they both bought a best seller and should therefore have a higher weight. In this case the properties whose similarity weвЂ™re computing are discrete (bought/not bought), but we can also represent similarity between continuous properties, like the distance between two cities, by letting an MLN have these similarities as features. If the evaluation function is a margin-style score function instead of the posterior probability, the result is a generalization of SVMs, the analogizersвЂ™ master algorithm. A greater challenge for our master learner is reproducing structure mapping, the more powerful type of analogy that can make inferences from one domain (e.g., the solar system) to another (the atom). We can do this by learning formulas that donвЂ™t refer to any of the specific relations in the source domain. For example,Friends of smokers also smoke is about friendship and smoking, butRelated entities have similar properties applies to any relation and property. We can learn it by generalizing fromFriends of friends also smoke,Coworkers of experts are also experts, and other such patterns in a social network and then apply it to, say, the web, with instances likeInteresting pages link to interesting pages, or to molecular biology, with instances likeProteins that interact with gene-regulating proteins also regulate genes. Researchers in my group and others have done all of these things, and more.. The world has parts, and parts belong to classes: combining these two gives us most of what we need to make inference in Alchemy tractable. We can learn the worldвЂ™s MLN by breaking it into parts and subparts, such that most interactions are between subparts of the same part, and then grouping the parts into classes and subclasses. If the world is a Lego toy, we can break it up into individual bricks, remembering which attaches to which, and group the bricks by shape and color. If the world is Wikipedia, we can extract the entities it talks about, group them into classes, and learn how classes relate to each other. Then if someone asks us вЂњIs Arnold Schwarzenegger an action star?вЂќ we can answer yes, because heвЂ™s a star and heвЂ™s in action movies. Step-by-step, we can learn larger and larger MLNs, until weвЂ™re doing what a friend of mine at Google calls вЂњplanetary-scale machine learningвЂќ: modeling everyone in the world at once, with data continually streaming in and answers streaming out.. Last and most, I thank my family for their love and support.. вЂњCancer: The march on malignancyвЂќ (Nature supplement, 2014) surveys the current state of the war on cancer.вЂњUsing patient data for personalized cancer treatments,вЂќ by Chris Edwards (Communications of the ACM, 2014), describes the early stages of what could grow into CanceRx.вЂњSimulating a living cell,вЂќ by Markus Covert (Scientific American, 2014), explains how his group built a computer model of a whole infectious bacterium.вЂњBreakthrough Technologies 2015: Internet of DNA,вЂќ by Antonio Regalado (MIT Technology Review, 2015), reports on the work of the Global Alliance for Genomics and Health. Cancer Commons is described inвЂњCancer: A Computational Disease that AI Can Cure,вЂќ by Jay Tenenbaum and Jeff Shrager (AI Magazine, 2011)..