This algorithm has the nice property that it never loses! Of course, itвЂ™s still missing many details, like how the board is represented in the computerвЂ™s memory and how this representation is changed by a move. For example, we could have two bits for each square, with the value 00 if the square is empty, which changes to 01 if it has a naught and 10 if it has a cross. But itвЂ™s precise and unambiguous enough that any competent programmer could fill in the blanks. It also helps that we donвЂ™t really have to specify an algorithm ourselves all the way down to individual transistors; we can use preexisting algorithms as building blocks, and thereвЂ™s a huge number of them to choose from.. Evolution is the ultimate example of how much a simple learning algorithm can achieve given enough data. Its input is the experience and fate of all living creatures that ever existed. (NowthatвЂ™s big data.) On the other hand, itвЂ™s been running for over three billion years on the most powerful computer on Earth: Earth itself. A computer version of it had better be faster and less data intensive than the original. Which one is the better model for the Master Algorithm: evolution or the brain? This is machine learningвЂ™s version of the nature versus nurture debate. And, just as nature and nurture combine to produce us, perhaps the true Master Algorithm contains elements of both.. Evolutionaries believe that the mother of all learning is natural selection. If it made us, it can make anything, and all we need to do is simulate it on the computer. The key problem that evolutionaries solve is learning structure: not just adjusting parameters, like backpropagation does, but creating the brain that those adjustments can then fine-tune. The evolutionariesвЂ™ master algorithm is genetic programming, which mates and evolves computer programs in the same way that nature mates and evolves organisms.. For analogizers, the key to learning is recognizing similarities between situations and thereby inferring other similarities. If two patients have similar symptoms, perhaps they have the same disease. The key problem is judging how similar two things are. The analogizersвЂ™ master algorithm is the support vector machine, which figures out which experiences to remember and how to combine them to make new predictions.. DonвЂ™t give up on machine learning or the Master Algorithm just yet, though. We donвЂ™t care about all possible worlds, only the one we live in. If we know something about the world and incorporate it into our learner, it now has an advantage over random guessing. To this Hume would reply that that knowledge must itself have come from induction and is therefore fallible. ThatвЂ™s true, even if the knowledge was encoded into our brains by evolution, but itвЂ™s a risk weвЂ™ll have to take. We can also ask whether thereвЂ™s a nugget of knowledge so incontestable, so fundamental, that we can build all induction on top of it. (Something like DescartesвЂ™ вЂњI think, therefore I am,вЂќ although itвЂ™s hard to see how to turn that one into
a learning algorithm.) I think the answer is yes, and weвЂ™ll see what that nugget is in Chapter 9.. [РљР°СЂС‚РёРЅРєР°: pic_5.jpg]. You can estimate the bias and variance of a learner by comparing its predictions after learning on random variations of the training set. If it keeps making the same mistakes, the problem is bias, and you need a more flexible learner (or just a different one). If thereвЂ™s no pattern to the mistakes, the problem is variance, and you want to either try a less flexible learner or get more data. Most learners have a knob you can turn to make them more or less flexible, such as the threshold for significance tests or the penalty on the size of the model. Tweaking that knob is your first resort.. Induction is the inverse of deduction. CHAPTER FOUR: How Does Your Brain Learn?. Genetic programmingвЂ™s first success, in 1995, was in designing electronic circuits. Starting with a pile of electronic components such as transistors, resistors, and capacitors, KozaвЂ™s system reinvented a previously patented design for a low-pass filter, a circuit that can be used for things like enhancing the bass on a dance-music track. Since then heвЂ™s made a sport of reinventing patented devices, turning them out by the dozen. The next milestone came in 2005, when the US Patent and Trademark Office awarded a patent to a genetically designed factory optimization system. If the Turing test had been to fool a patent examiner instead of a conversationalist, then January 25, 2005, would have been a date for the history books.. Evolutionaries and connectionists have something important in common: they both design learning algorithms inspired by nature. But then they part ways. Evolutionaries focus on learning structure; to them, fine-tuning an evolved structure by optimizing parameters is of secondary importance. In contrast, connectionists prefer to take a simple, hand-coded structure with lots of connections and let weight learning do all the work. This is machine learningвЂ™s version of the nature versus nurture controversy, and there are good arguments on both sides.. This is a radical departure from the way science is usually done. ItвЂ™s like saying, вЂњActually, neither Copernicus nor Ptolemy was right; letвЂ™s just predict the planetsвЂ™ future trajectories assuming Earth goes round the sun and vice versa and average the results.вЂќ. The city is divided into five sectors, each belonging to one of the five tribes. Each sector stretches down from its Tower of Representation to the cityвЂ™s outer walls, encompassing the tower, a clutch of palaces in the Citadel of Evaluation, and the streets and houses in Optimization Town they overlook. The five sectors and three rings divide the city into fifteen districts, fifteen shapes, fifteen pieces of the puzzle you need to solve:. Markov logic networks. Chapter Seven.