You might think the 2012 election was a fluke: most elections are not close enough for machine learning to be the deciding factor. But machine learning willcause more elections to be close in the future. In politics, as in everything, learning is an arms race. In the days of Karl Rove, a former direct marketer and data miner, the Republicans were ahead. By 2012, theyвЂ™d fallen behind, but now theyвЂ™re catching up again. We donвЂ™t know whoвЂ™ll be ahead in the next election cycle, but both parties will be working hard to win. That means understanding the voters better and tailoring the candidatesвЂ™ pitches-even choosing the candidates themselves-accordingly. The same applies to entire party platforms, during and between election cycles: if detailed voter models, based on hard data, say a partyвЂ™s current platform is a losing one, the party will change it. As a result, major events aside, gaps between candidates in the polls will be smaller and shorter lived. Other things being equal, the candidates with the better voter models will win, and voters will be better served for it.. Another prominent machine-learning skeptic is the linguist Noam Chomsky. Chomsky believes that language must be innate, because the examples of grammatical sentences children hear are not enough to learn a grammar. This only puts the burden of learning language on evolution, however; it does not argue against the Master Algorithm but only against it being something like the brain. Moreover, if a universal grammar exists (as Chomsky believes), elucidating it is a step toward elucidating the Master Algorithm. The only way this is not the case is if language has nothing in common with other cognitive abilities, which is implausible given its evolutionary recency.. Critics like Minsky, Chomsky, and Fodor once had the upper hand, but thankfully their influence has waned. Nevertheless, we should keep their criticisms in mind as we set out on the road to the Master Algorithm for two reasons. The first is that knowledge engineers faced many of the same problems machine learners do, and even if they didnвЂ™t succeed, they learned many valuable lessons. The second is that learning and knowledge are intertwined in surprisingly subtle ways, as weвЂ™ll soon find out. Unfortunately, the two camps often talk past each other. They speak different languages: machine learning speaks probability, and knowledge engineering speaks logic. Later in the book weвЂ™ll see what to do about this.. Most of all, we have to worry about what the Master Algorithm could do in the wrong hands. The first line of defense is to make sure the good guys get it first-or, if itвЂ™s not clear who the good guys are, to make sure itвЂ™s open-sourced. The second is to realize that, no matter how good the learning algorithm is, itвЂ™s only as good as the data it gets. He who controls the data controls the learner. Your reaction to the datafication of life should not be to retreat to a log cabin-the woods, too, are full of sensors-but to aggressively seek control of the data that matters to you. ItвЂ™s good to have recommenders that find what you want and bring it to you; youвЂ™d feel lost without them. But they should bring you whatyou want, not what someone else wants you to have. Control of data and ownership of the models learned from it is what many of the twenty-first centuryвЂ™s battles will be about-between governments, corporations, unions, and individuals. But you also have an ethical duty to share data for the common good. Machine learning alone will not cure cancer; cancer patients will, by sharing their data for the benefit of future patients.. The five tribes of machine learning. The problem is not limited to memorizing instances wholesale. Whenever a learner finds a pattern in the data that is not actually true in the real world, we say that it has overfit the data. Overfitting is the central problem in machine learning. More papers have been written about it than about any other topic. Every powerful learner, whether symbolist, connectionist, or any other, has to worry about hallucinating patterns. The only safe way to avoid it is to severely restrict what the learner can learn, for example by requiring that it be a short conjunctive concept. Unfortunately, that throws out the baby with the bathwater, leaving the learner unable to see most of the true patterns that are visible in the data. Thus a good learner is forever walking the narrow path between blindness and hallucination.. ThereforeвЂ¦?вЂ¦. [РљР°СЂС‚РёРЅРєР°: pic_10.jpg]. A complete model of a cell. Notice that weвЂ™re only saying that fever and cough are independent given that you have the flu, not overall. Clearly, if we donвЂ™t know whether you have the flu, fever and cough are highly correlated, since youвЂ™re much more likely to have a cough if you already have a fever.P(fever, cough) isnot equal toP(fever)Г— P(cough). All weвЂ™re saying is that, if we know you have the flu, knowing whether you have a fever gives us noadditional information about whether you have a cough. Likewise, if you donвЂ™t know the sun is about to rise and you see the stars fade, your expectation that the sky will lighten increases; but if you already know that sunrise is imminent, seeing the stars fade makes no difference.. Markov assumed (wrongly but usefully) that the probabilities are the same at every position in the text. Thus we need to estimate only three probabilities:P(Vowel1 = True),P(Voweli+1 = True | Voweli = True), andP(Voweli+1= True | Voweli = False). (Since probabilities sum to one, from these we can immediately obtainP(Vowel1 = False), etc.) As with NaГЇve Bayes, we can have as many variables as we want without the number of probabilities we need to estimate going through the roof, but now the variables actually depend on each other.. Everything is connected, but not directly. [РљР°СЂС‚РёРЅРєР°: pic_20.jpg]. 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.. Picture two strands of DNA going for a swim in their private pool, aka a bacteriumвЂ™s cytoplasm, two billion years ago. TheyвЂ™re pondering a momentous decision. вЂњIвЂ™m worried, Diana,вЂќ says one. вЂњIf we start making multicellular creatures, will they take over?вЂќ Fast-forward to the twenty-first century, and DNA is still alive and well. Better than ever, in fact, with anincreasing fraction living safely in bipedal organisms comprising trillions of cells. ItвЂ™s been quite a ride for our tiny double-stranded friends since they made their momentous decision. Humans are their trickiest creation yet; weвЂ™ve invented things like contraception that let us have fun without spreading our DNA, and we have-or seem to have-free will. But itвЂ™s still DNA that shapes our notions of fun, and we use our free will to pursue pleasure and avoid pain, which, for the most part, still coincides with whatвЂ™s best for our DNAвЂ™s survival. We may yet be DNAвЂ™s demise if we choose to transmute ourselves into silicon, but even then, itвЂ™s been a great two billion years. The decision we face today is similar: if we start making AIs-vast, interconnected, superhuman, unfathomable AIs-will they take over? Not any more than multicellular organisms took over from genes, vast and unfathomable as we may be to them. AIs are our survival machines, in the same way that we are our genesвЂ™..