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.

A Concise Introduction to Languages and Machines (Undergraduate Topics in Computer Science)

The Handbook of Brain Theory and Neural Networks (2nd Edition)

Computational Aspects of Cooperative Game Theory (Synthesis Lectures on Artificial Inetlligence and Machine Learning)

Database Systems: The Complete Book (GOAL Series)

Formal Languages and Compilation (2nd Edition) (Texts in Computer Science)


















can achieve given enough data. Its input is the experience and fate of all living creatures that ever existed. (Now that’s big data.) On the other hand, it’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 learning’s version of the nature versus nurture debate. And,

human intuition.” In fact, it’s the other way around: human intuition can’t replace data. Intuition is what you use when you don’t know the facts, and since you often don’t, intuition is precious. But when the evidence is before you, why would you deny it? Statistical analysis beats talent scouts in baseball (as Michael Lewis memorably documented in Moneyball), it beats connoisseurs at wine tasting, and every day we see new examples of what it can do. Because of the influx of data, the boundary

right every time; in fact, errors are the rule, not the exception. But it’s OK, because we discard the misses and build on the hits, and the cumulative result is what matters. Once we acquire a new piece of knowledge, it becomes a basis for inducing yet more knowledge. The only question is where to begin. Priming the knowledge pump In the Principia, along with his three laws of motion, Newton enunciates four rules of induction. Although these are much less well known than the physical laws,

function is a no-brainer. If we need a program that can diagnose a patient, one that correctly diagnoses 60 percent of the patients in our database is better than one that only gets it right 55 percent of the time, and thus a possible fitness function is the fraction of correctly diagnosed cases. In this regard, genetic algorithms are a lot like selective breeding. Darwin opened The Origin of Species with a discussion of it, as a stepping-stone to the more difficult concept of natural selection.

surface, reinforcement learning continually orchestrates and fine-tunes this prodigious symphony of motion. Snippets of reinforcement learning, also known as habits, make up most of what you do. You feel hungry, walk to the fridge, and grab a snack. As Charles Duhigg shows in The Power of Habit, understanding and controlling this cycle of cue, routine, and reward is key to success, not just for individuals but for businesses and even whole societies. Of reinforcement learning’s founders, Rich

Download sample