On the other hand, the following is an algorithm for playing tic-tac-toe:. 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.. The first is that, in reality, we never have enough data to completely determine the world. Even ignoring the uncertainty principle, precisely knowing the positions and velocities of all particles in the world at some point in time is not remotely feasible. And because the laws of physics are chaotic, uncertainty compounds over time, and pretty soon they determine very little indeed. To accurately describe the world, we need a fresh batch of data at regular intervals. In effect, the laws of physics only tell us what happens locally. This drastically reduces their power.. Of course, we donвЂ™t have to start from scratch in our hunt for the Master Algorithm. We have a few decades of machine learning research to draw on. Some of the smartest people on the planet have devoted their lives to inventing learning algorithms, and some would even claim that they already have a universal learner in hand. We will stand on the shoulders of these giants, but take such claims with a grain of salt. Which raises the question: how will we know when weвЂ™ve found the Master Algorithm? When the same learner, with only parameter changes and minimal input aside from the data, can understand video and text as well as humans, and make significant new discoveries in biology, sociology, and other sciences. Clearly, by this standard no learner has yet been demonstrated to be the Master Algorithm, even in the unlikely case one already exists.. EinsteinвЂ™s general relativity was only widely accepted once Arthur Eddington empirically confirmed its prediction that the sun bends the light of distant stars. But you donвЂ™t need to wait around for new data to arrive to decide whether you can trust your learner. Rather, you take the data you have and randomly divide it into a training set, which you give to the learner, and a test set, which you hide from it and use to verify its accuracy. Accuracy on held-out data is the gold standard in machine learning. You can write a paper about a great new learning algorithm youвЂ™ve invented, but if your algorithm is not significantly more accurate than previous ones on held-out data, the paper is not publishable.. Socrates is human.. The number of transistors in a computer is catching up with the number of neurons in a human brain, but the brain wins hands down in the number of connections. In a microprocessor, a typical transistor is directly connected to only a few others, and the planar semiconductor technology used severely limits how much better a computer can do. In contrast, a neuron has thousands of synapses. If youвЂ™re walking down the street and come across an acquaintance, it takes you only about a tenth of a second to recognize her. At neuron switching speeds, this is barely enough time for a hundred processing steps, but in those hundred steps your brain manages to scan your entire memory, find the bestmatch, and adapt it to the new context (different clothes, different lighting, and so on). In a brain, each processing step can be very complex and involve a lot of information, consonant with a distributed representation.. Another notable early success of neural networks was learning to drive a car. Driverless cars first broke into the public consciousness with the DARPA Grand Challenges in 2004 and 2005, but a over a decade earlier, researchers at Carnegie Mellon had already successfully trained a multilayer perceptron to drive a car by detecting the road in video images and appropriately turning the steering wheel. Carnegie MellonвЂ™s car managed to drive coast to coast across America with very blurry vision (thirty by thirty-two pixels), a brain
smaller than a wormвЂ™s, and only a few assists from the human copilot. (The project was dubbed вЂњNo Hands Across America.вЂќ) It may not have been the first truly self-driving car, but it did compare favorably with most teenage drivers.. One consequence of crossing over program trees instead of bit strings is that the resulting programs can have any size, making the learning more flexible. The overall tendency is for bloat, however, with larger and larger trees growing as evolution goes on longer (also known asвЂњsurvival of the fattestвЂќ). Evolutionaries can take comfort from the fact that human-written programs are no different (Microsoft Windows: forty-five million lines of code and counting), and that human-made code doesnвЂ™t allow a solution as simple as adding a complexity penalty to the fitness function.. BayesвЂ™ theorem as a foundation for statistics and machine learning is bedeviled not just by computational difficulty but also by extreme controversy. You might be forgiven for wondering why: IsnвЂ™t it a straightforward consequence of the notion of conditional probability, as we saw in the flu example? Indeed, no one has a problem with the formula itself. The controversy is in how Bayesians obtain the probabilities that go into it and what those probabilities mean. For most statisticians, the only legitimate way to estimate probabilities is by counting how often the corresponding events occur. For example, the probability of fever is 0.2 because twenty out of one hundred observed patients had it. This is the вЂњfrequentistвЂќ interpretation of probability, and the dominant school of thought in statistics takes its name from it. But notice that in the sunrise example, and in LaplaceвЂ™s principle of indifference, we did something different: we pulled a probability out of thin air. What exactly justifies assuming a priori that the probability the sun will rise is one-half, or two-thirds, or whatever? BayesiansвЂ™ answer is that a probability is not a frequency but a subjective degree of belief. Therefore itвЂ™s up to you what you make it, and all that Bayesian inference lets you do is update your prior beliefs with new evidence to obtain your posterior beliefs (also known as вЂњturning the Bayesian crankвЂќ). BayesiansвЂ™ devotion to this idea is near religious, enough to withstand two hundred years of attacks and counting. And with the appearance on the stage of computers powerful enough to do Bayesian inference, and the massive data sets to go with it, theyвЂ™re beginning to gain the upper hand.. Markov weighs the evidence. You donвЂ™t need explicit ratings to do collaborative filtering, by the way. If Ken ordered a movie on Netflix, that means he expects to like it. So the вЂњratingsвЂќ can just be ordered/not ordered, and two users are similar if theyвЂ™ve ordered a lot of the same movies. Even just clicking on something implicitly shows interest in it. Nearest-neighbor works with all of the above. These days all kinds of algorithms are used to recommend items to users, but weightedk-nearest-neighbor was the first widely used one, and itвЂ™s still hard to beat.. [РљР°СЂС‚РёРЅРєР°: pic_32.jpg]. Like all phase transitions, this one will eventually taper off too. Overcoming a bottleneck does not mean the sky is the limit; it means the next bottleneck is the limit, even if we donвЂ™t see it yet. Other transitions will follow, some large, some small, some soon, some not for a long time. But the next thousand years could well be the most amazing in the life of planet Earth.. Whether you read this book out of curiosity or professional interest, I hope you will share what youвЂ™ve learned with your friends and colleagues. Machine learning touches the lives of every one of us, and itвЂ™s up to all of us to decide what we want to do with it. Armed with your new understanding of machine learning, youвЂ™re in a much better position to think about issues like privacy and data sharing, the future of work, robot warfare, and the promise and peril of AI; and the more of us have this understanding, the more likely weвЂ™ll avoid the pitfalls and find the right paths. ThatвЂ™s the other big reason I wrote this book. The statistician knows that prediction is hard, especiallyabout the future, and the computer scientist knows that the best way to predict the future is to invent it, but the unexamined future is not worth inventing..