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

by Pedro Domingos

A thought-provoking and wide-ranging exploration of machine learning and the race to build computer intelligences as flexible as our own
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. In The Master Algorithm , Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.

Genres: Nonfiction, Science, Artificial Intelligence, Technology, Computer Science, Business, Programming

352 pages, Hardcover

First published September 8, 2015

About the author

I'm a professor emeritus of computer science and engineering at the University of Washington and the author of 2040 and The Master Algorithm. I'm a winner of the SIGKDD Innovation Award and the IJCAI John McCarthy Award, two of the highest honors in data science and AI. I'm a Fellow of the AAAS and AAAI, and I've received an NSF CAREER Award, a Sloan Fellowship, a Fulbright Scholarship, an IBM Faculty Award, several best paper awards, and other distinctions. I received an undergraduate degree (1988) and M.S. in Electrical Engineering and Computer Science (1992) from IST, in Lisbon, and an M.S. (1994) and Ph.D. (1997) in Information and Computer Science from the University of California at Irvine. I'm the author or co-author of over 200 technical publications in machine learning, data science, and other areas. I'm a member of the editorial board of the Machine Learning journal, co-founder of the International Machine Learning Society, and past associate editor of JAIR. I was program co-chair of KDD-2003 and SRL-2009, and I've served on the program committees of AAAI, ICML, IJCAI, KDD, NIPS, SIGMOD, UAI, WWW, and others. I've written for the Wall Street Journal, Spectator, Scientific American, Wired, and others. I helped start the fields of statistical relational AI, data stream mining, adversarial learning, machine learning for information integration, and influence maximization in social networks.

Reviews

[E-K]

읽을만한 기계학습 알고리즘 입문서다. 배경철학과 접근방법에 따라 알고리즘을 다섯 가지 종족(기호주의자, 연결주의자, 베이즈주의자, 유추주의자, 진화주의자)으로 나누어 소개하고 비교한다. 이 다섯 종족을 통합한 궁극의 기계학습 마스터 알고리즘을 찾기 위해 해온 노력을 간략하게 소개하고 있다. 마지막 장들은 알고리즘 보다는 데이터와 응용분야에 관한 이야기를 하고 있지만 정리가 깔끔하지 않다.

[Frank]

너무 장황해서 쉽게 읽지 못하겠다. 머신 러닝에 대한 편견만 심해졌다.