I had a number of different but overlapping audiences in mind when writing this book.. The argument from evolution. Unlike the theories of a given field, which only have power within that field, the Master Algorithm has power across all fields. Within field X, it has less power than field XвЂ™s prevailing theory, but across all fields-when we consider the whole world-it has vastly more power than any other theory. The Master Algorithm is the germ of every theory; all we need to add to it to obtain theory X is the minimum amount of data required to induce it. (In the case of physics, that would be just the results of perhaps a few hundred key experiments.) The upshot is that, pound for pound, the Master Algorithm may well be the best starting point for a theory of everything weвЂ™ll ever have.Pace Stephen Hawking, it may ultimately tell us more about the mind of God than string theory.. Plato is human. Plato is mortal.. If youвЂ™re for cutting taxes, pro-choice, and against gun control, youвЂ™re an independent.. Of course, computing the length of a planetвЂ™s year is a very simple problem, involving only multiplication and square roots. In general, program trees can include the full range of programming constructs, such asIfвЂ¦thenвЂ¦ statements, loops, and recursion. A more illustrative example of what genetic programming can do is figuring out the sequence of actions a robot needs to perform to achieve some goal. Suppose I ask my officebot to bring me a stapler from the closet down the hall. The robot has a large set of behaviors available to it, such as moving down a hallway, opening a door, picking up an object, and so on. Each of these can in turn be composed of various sub-behaviors: move the robotвЂ™s hand toward the object, or grasp it at various possible points, for example. Each behavior may be executed or not depending on the results of previous behaviors, may need to be repeated some number of times, and so on. The challenge is to assemble the right structure of behaviors and sub-behaviors, together with the parameters for each, such as how far to move the hand. Starting with the robotвЂ™s вЂњatomicвЂќ behaviors and their allowed combinations, genetic programming can assemble a complex behavior that accomplishes the desired goal. A number of researchers have evolved strategies for robot soccer players in this way.. The crucial question is exactly how the posterior probability should evolve as you see more evidence. The answer is BayesвЂ™ theorem. We can think of it in terms of cause and effect. Sunrise causes the stars to fade and the sky to lighten, but the latter is stronger evidence of daybreak, since the stars could fade in the middle of the night due to, say, fog rolling in. So the probability of sunrise should increase more after seeing the sky lighten than after seeing the stars fade. In mathematical notation, we say thatP(sunrise | lightening-sky), the conditional probability of sunrise given that the sky is lightening, is greater thanP(sunrise | fading-stars), its conditional probability given that the stars are fading. According to BayesвЂ™ theorem, the more likely the effect is given the cause, the more likely the cause is given the effect: ifP(lightening-sky | sunrise) is higher thanP(fading-stars | sunrise), perhaps because some planets are far enough from their sun that the stars still shine after sunrise, thenP(sunrise | lightening sky) is also higher thanP(sunrise | fading-stars).. HMMs are also a favorite tool of computational biologists. A protein is a sequence of amino acids, and DNA is a sequence of bases. If we want to predict, for example, how a protein will fold into a 3-D shape, we can treat the amino acids as the observations and the type of fold at each point as the hidden state. Similarly, we can use an HMM to identify the sites in DNA where gene transcription is initiated and many other properties.. [РљР°СЂС‚РёРЅРєР°: pic_24.jpg]. Decision trees are not immune to the curse of dimensionality either. LetвЂ™s say the concept youвЂ™re trying to learn is a sphere: points inside it are positive, and points outside it are negative. A decision tree can approximate a sphere by the smallest cube it fits inside. Not perfect, but not too bad either: only the corners of the cube get misclassified. But in high dimensions, almost the entire volume of the hypercube lies outside the hypersphere. For every example you correctly classify as positive, you incorrectly classify many negative ones as positive, causing your accuracy to plummet.. 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.. You rack your brains for a solution, but the more you try, the harder it gets. Perhaps unifying logic and probability is just beyond human ability. Exhausted, you fall asleep. A deep growl jolts you awake. The hydra-headed complexity monster pounces on you, jaws snapping, but you duck at the last moment. Slashing desperately at the monster with the sword of learning, the only one that can slay it, you finally succeed in cutting off all its heads. Before it can grow new ones, you run up the stairs.. Computational complexity is one thing, but human complexity is another. If computers are like idiot savants, learning algorithms can sometimes come across like child prodigies prone to temper tantrums. ThatвЂ™s one reason humans who can wrangle them into submission are so highly paid. If you know how to expertly tweak the control knobs until theyвЂ™re just right, magic can ensue, in the form of a stream of insights beyond the learnerвЂ™s years. And, not unlike the Delphic oracle, interpreting the learnerвЂ™s pronouncements can itself require considerable skill. Turn the knobs wrong, though, and the learner may spew out a torrent of gibberish or clam up in defiance. Unfortunately, in this regard Alchemy is no better than most. Writing down what you know in logic, feeding in the data, and pushingthe button is the fun part. When Alchemy returns a beautifully accurate and efficient MLN, you go down to the pub and celebrate. When it doesnвЂ™t-which is most of the time-the battle begins. Is the problem in the knowledge, the learning, or the inference? On the one hand, because of the learning and probabilistic inference, a simple MLN can do the job of a complex program. On the other, when it doesnвЂ™t work, itвЂ™s much harder to debug. The solution is to make it more interactive, able to introspect and explain its reasoning. That will take us another step closer to the Master Algorithm.. Chapter Two. Niles Eldredge and Stephen Jay Gould propose their theory of punctuated equilibria inвЂњPunctuated equilibria: An alternative to phyletic gradualism,вЂќ inModels in Paleobiology, edited by T. J. M. Schopf (Freeman, 1972). Richard Dawkins critiques it in Chapter 9 ofThe Blind Watchmaker (Norton, 1986). The exploration-exploitation dilemma is discussed in Chapter 2 ofReinforcement Learning,* by Richard Sutton and Andrew Barto (MIT Press, 1998). John Holland proposes his solution, and much else, inAdaptation in Natural and Artificial Systems* (University of Michigan Press, 1975)..