The second goal of this book is thus to enableyou to invent the Master Algorithm. YouвЂ™d think this would require heavy-duty mathematics and severe theoretical work. On the contrary, what it requires is stepping back from the mathematical arcana to see the overarching pattern of learning phenomena; and for this the layman, approaching the forest from a distance, is in some ways better placed than the specialist, already deeply immersed in the study of particular trees. Once we have the conceptual solution, we can fill in the mathematical details; but that is not for this book, and not the most important part. Thus, as we visit each tribe, our goal is to gather its piece of the puzzle and understand where it fits, mindful that none of the blind men can see the whole elephant. In particular, weвЂ™ll see what each tribe can contribute to curing cancer, and also what itвЂ™s missing. Then, step-by-step, weвЂ™ll assemble all the pieces into the solution-or rather,a solution that is not yet the Master Algorithm, but is the closest anyone has come, and hopefully makes a good launch pad for your imagination. And weвЂ™ll preview the use of this algorithm as a weapon in the fight against cancer. As you read the book, feel free to skim or skip any parts you find troublesome; itвЂ™s the big picture that matters, and youвЂ™ll probably get more out of those parts if you revisit them after the puzzle is assembled.. We live in the age of algorithms. Only a generation or two ago, mentioning the wordalgorithm would have drawn a blank from most people. Today, algorithms are in every nook and cranny of civilization. They are woven into the fabric of everyday life. TheyвЂ™re not just in your cell phone or your laptop but in your car, your house, your appliances, and your toys. Your bank is a gigantic tangle of algorithms, with humans turning the knobs here and there. Algorithms schedule flights and then fly the airplanes. Algorithms run factories, trade and routegoods, cash the proceeds, and keep records. If every algorithm suddenly stopped working, it would be the end of the world as we know it.. There has to be a better way.. The larger outcome is that democracy works better because the bandwidth of communication between voters and politicians increases enormously. In these days of high-speed Internet, the amount of information your elected representatives get from you is still decidedly nineteenth century: a hundred bits or so every two years, as much as fits on a ballot. This is supplemented by polling and perhaps the occasional e-mail or town-hall meeting, but thatвЂ™s still precious little. Big data and machine learning change the equation. In the future, provided voter models are accurate, elected officials will be able to ask voters what they want a thousand times a day and act accordingly-without having to pester the actual flesh-and-blood citizens.. All humans are mortal.. [РљР°СЂС‚РёРЅРєР°: pic_12.jpg]. Among the many ironies of the history of the perceptron, perhaps the saddest is that Frank Rosenblatt died in a boating accident in Chesapeake Bay in 1969 and never lived to see the second act of his creation.. A complete model of a cell. In machine learning, as elsewhere in computer science, thereвЂ™s nothing better than getting such a combinatorial explosion to work for you instead of against you. WhatвЂ™s clever about genetic algorithms is that each string implicitly contains an exponential number of building blocks, known as schemas, and so the search is a lot more efficient than it seems. This is because every subset of the stringвЂ™s bits is a schema, representing some potentially fit combination of properties, and a string has an exponential number of subsets. We can represent a schema by replacing the bits in the string that arenвЂ™t part of it with *. For example, the string 110 contains the schemas ***, **0, *1*, 1**, *10, 11*, 1*0, and 110. We get a different schema for every different choice of bits to include; since we have two choices for each bit (include/donвЂ™t include), we have 2n schemas. Conversely, a particular schema may be represented in many different strings in a population, and is implicitly evaluated every time they are. Suppose that a hypothesisвЂ™s probability of surviving into the next generation is proportional to its fitness. Holland showed that, in this case, the fitter a schemaвЂ™s representatives in one generation are compared to the average, the more of them we can expect to see in the next generation. So, while the genetic algorithm explicitly manipulates strings, it implicitly searches the much larger space of schemas. Over time, fitter schemas come to dominate the population, and so unlike the drunkard, the genetic algorithm finds its way home.. The problem only gets worse if we try to learn the structure of a Bayesian network as well as its parameters. We can do this by hill climbing, starting with an empty network (no arrows), adding the arrow that most increases likelihood, and so on until no arrow causes an improvement. Unfortunately, this quickly leads to massive overfitting, with a network that assigns zero probability to all states not appearing in the data. Bayesians can do something much more interesting. They can use the prior distribution to encode expertsвЂ™ knowledge about the problem-their answer to HumeвЂ™s question. For example, we can design an initial Bayesian network for medical diagnosis by interviewing doctors, asking them which symptoms they think depend on which diseases, and adding the corresponding arrows. This is the вЂњprior network,вЂќ and the prior distribution can penalize alternative networks by the number of arrows that they add or remove from it. But doctors are fallible, so weвЂ™ll let the data override them: if the increase in likelihood from adding an arrow outweighs the penalty, we do it.. Bayesians, in turn, point to the brittleness of logic. If I have a rule likeBirds fly, a world with even one flightless bird is impossible. If I try to patch things by adding exceptions, such asBirds fly, unless theyвЂ™re penguins, IвЂ™ll never be done. (What about ostriches? Birds in cages? Dead birds? Birds with broken wings? Soaked wings?) A doctor diagnoses you with cancer, and you decide to get a second opinion. If the second doctor disagrees, youвЂ™re stuck. You canвЂ™t weigh the two opinions; you just have to believe them both. And then a catastrophe happens: pigs fly, perpetual motion is possible, and Earth doesnвЂ™t exist-because in logic everything can be inferred from a contradiction. Furthermore, if knowledge is learned from data, I can never be sure itвЂ™s true. Why do symbolists pretend otherwise? Surely Hume would frown on such insouciance.. Humans do have one constant guide: their emotions. We seek pleasure and avoid pain. When you touch a hot stove, you instinctively recoil. ThatвЂ™s the easy part. The hard part is learning not to touch the stove in the first place. That requires moving to avoid a sharp pain that you have not yet felt. Your brain does this by associating the pain not just with the moment you touch the stove, but with the actions leading up to it. Edward Thorndike called this the law of effect: actions that lead to pleasure are more likely to be repeated in the future; actions that lead to pain, less so. Pleasure travels back through time, so to speak, and actions can eventually become associated with effects that are quite remote from them. Humans can do this kind of long-range reward seeking better than any other animal, and itвЂ™s crucial to our success. In a famous experiment, children were presented with a marshmallow and told that if they resisted eating it for a few minutes, they could have two. The ones who succeeded went on to do better in school and adult life. Perhaps less obviously, companies using machine learning to improve their websites or their business practices face a similar problem. A company may make a change that brings in more revenue in the short term-like selling an inferior product that costs less to make for the same price as the original superior product-but miss seeing that doing this will lose customers in the longer term.. Learning an MLN means discovering formulas that are true in the world more often than random chance would predict, and figuring out the weights for those formulas that cause their predicted probabilities to match their observed frequencies. Once weвЂ™ve learned an MLN, we can use it to answer questions like вЂњWhat is the probability that Bob has the flu, given that heвЂ™s friends with Alice and she has the flu?вЂќ And guess what? It turns out that the probability is given by an S curve applied to the weighted sum of features, much as in a multilayer perceptron. And an MLN with long chains of rules can represent a deep neural network, with one layer per link in the chain.. Companies that host the digital you and data unions are what a mature future of data in society looks like to me. Whether weвЂ™ll get there is an open question. Today, most people are unaware of both how much data about them is being gathered and what the potential costs and benefits are. Companies seem content to continue doing it under the radar, terrified of a blowup. But sooner or later a blowup will happen, and in the ensuing fracas, draconian laws will be passed that in the end will serve no one. Better to foster awareness now and let everyone make their individual choices about what to share, what not, and how and where.. Principal-component analysis is one of the oldest techniques in machine learning and statistics, having been first proposed by Karl Pearson in 1901 in the paperвЂњOn lines and planes of closest fit to systems of points in spaceвЂќ* (Philosophical Magazine). The type of dimensionality reduction used to grade SAT essays was introduced by Scott Deerwester et al. in the paperвЂњIndexing by latent semantic analysisвЂќ* (Journal of the American Society for Information Science, 1990). Yehuda Koren, Robert Bell, and Chris Volinsky explain how Netflix-style collaborative filtering works inвЂњMatrix factorization techniques for recommender systemsвЂќ* (IEEE Computer, 2009). The Isomap algorithm was introduced inвЂњA global geometric framework for nonlinear dimensionality reduction,вЂќ* by Josh Tenenbaum, Vin de Silva, and John Langford (Science, 2000)..