The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

Pedro Domingos

Language: English

Pages: 352

ISBN: 0465065708

Format: PDF / Kindle (mobi) / ePub

Algorithms increasingly run our lives. They find books, movies, jobs, and dates for us, manage our investments, and discover new drugs. More and more, these algorithms work by learning from the trails of data we leave in our newly digital world. Like curious children, they observe us, imitate, and experiment. And in the world’s top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask.

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.

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occupation of all mankind to figure out what we want from the machines and make sure we’re getting it—more on this later in this chapter. In the meantime, as the boundary between automatable and non-automatable jobs advances across the economic landscape, what we’ll likely see is unemployment creeping up, downward pressure on the wages of more and more professions, and increasing rewards for the fewer and fewer that can’t yet be automated. This is what’s already happening, of course, but it has

many cases. Mathematicians like to say that God can disobey the laws of physics, but even he cannot defy the laws of logic. This may be so, but the laws of logic are for deduction; what we need is something equivalent, but for induction. The five tribes of machine learning Of course, we don’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

“no free lunch” theorem. Then we’ll see the symbolists’ answer to Hume. This leads us to the most important problem in machine learning: overfitting, or hallucinating patterns that aren’t really there. We’ll see how the symbolists solve it, and how machine learning is at heart a kind of alchemy, transmuting data into knowledge with the aid of a philosopher’s stone. For the symbolists, the philosopher’s stone is knowledge itself. In the next four chapters we’ll study the solutions of the other

major in, a college undergraduate deciding whether to go into research, or a seasoned professional considering a career change—my hope is that this book will spark in you an interest in this fascinating field. The world has a dire shortage of machine-learning experts, and if you decide to join us, you can look forward to not only exciting times and material rewards but also a unique opportunity to serve society. And if you’re already studying machine learning, I hope the book will help you get

population, and so unlike the drunkard, the genetic algorithm finds its way home. One of the most important problems in machine learning—and life—is the exploration-exploitation dilemma. If you’ve found something that works, should you just keep doing it? Or is it better to try new things, knowing it could be a waste of time but also might lead to a better solution? Would you rather be a cowboy or a farmer? Start a company or run an existing one? Go steady or play the field? A midlife crisis is

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