The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
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Machine learning is the automation of discovery—the scientific method on steroids—that enables intelligent robots and computers to program themselves. No field of science today is more important yet more shrouded in mystery. Pedro Domingos, one of the field’s leading lights, lifts the veil for the first time to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He charts a course through machine learning’s five major schools of thought, showing how they turn ideas from neuroscience, evolution, psychology, physics, and statistics into algorithms ready to serve you. Step by step, he assembles a blueprint for the future universal learner—the Master Algorithm—and discusses what it means for you, and for the future of business, science, and society.
If data-ism is today’s rising philosophy, this book will be its bible. The quest for universal learning is one of the most significant, fascinating, and revolutionary intellectual developments of all time. A groundbreaking book, The Master Algorithm is the essential guide for anyone and everyone wanting to understand not just how the revolution will happen, but how to be at its forefront.
better than the plain metal variety. Children raised by robot nannies will have a lifelong soft spot for kindly electronic friends. The “uncanny valley”—our discomfort with robots that are almost human but not quite—will be unknown to them because they grew up with robot mannerisms and maybe even adopted them as cool teenagers. The next step in the insidious progression of AI control is letting them make all the decisions because they’re, well, so much smarter. Beware. They may be smarter, but
occasion when she said yes—same day of week, same type of date, same weather, and same shows on TV—that still doesn’t mean that this time she will say yes. For all you know, her answer is determined by some factor that you didn’t think of or don’t have access to. Or maybe there’s no rhyme or reason to her answers: they’re random, and you’re just spinning your wheels trying to find a pattern in them. Philosophers have debated Hume’s problem of induction ever since he posed it, but no one has come
God, then you can model the universe as a vast Naïve Bayes distribution where everything that happens is independent given God’s will. The catch, of course, is that we can’t read God’s mind, but in Chapter 8 we’ll investigate how to learn Naïve Bayes models even when we don’t know the classes of the examples. It might not seem so at first, but Naïve Bayes is closely related to the perceptron algorithm. The perceptron adds weights and Naïve Bayes multiplies probabilities, but if you take a
size of the species’ population. When the branches are too thick, our only choice is to resort to approximate inference. One solution, left as an exercise by Pearl in his book on Bayesian networks, is to pretend the graph has no loops and just keep propagating probabilities back and forth until they converge. This is known as loopy belief propagation, both because it works on graphs with loops and because it’s a crazy idea. Surprisingly, it turns out to work quite well in many cases. For
apparent is business. Why businesses embrace machine learning Why is Google worth so much more than Yahoo? They both make their money from showing ads on the web, and they’re both top destinations. Both use auctions to sell ads and machine learning to predict how likely a user is to click on an ad (the higher the probability, the more valuable the ad). But Google’s learning algorithms are much better than Yahoo’s. This is not the only reason for the difference in their market caps, of course,