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

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Basic Books, Sep 22, 2015 - Computers - 352 pages
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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.

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Machine Learning Made Easier (or NOT!): "The Master Algorithm" by Pedro Domingos Published September 22nd 2015. á á How can one become an expert in ML? All one needs is a basic background in ... Read full review

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Most of this book was great because it read like a short summary of what is taught in an introduction to cognitive science class. While spouting about how Bayesian stats decidedly kick frequentist stat's ass, to which I agree, the author showed how to look at the world itself, and everything in it, through a more Bayesian lens. He hammered home the central point that nothing can be understood in isolation and must rather be understood through its connection to the things around it. One more book on my list that belongs on the complexity/emergence/network shelf. Any book in that category, if it's any good at all, will be among my favorites. Adding to that, this author did a great job of covering one of my favorite subjects, Markov chains.
Every time I dictate into my phone and realize that Apple has corrected "thus" to DOS and can recognize any computer programing term but not the science terms I dictate, I laugh to myself at the coders who create the Markov chains. They are seriously biased in favor of programmers. But in the end, that is because they are programmers:)
This book does an exceptional job of explaining Bayes, providing a brief history of how it came to be used, and showing how it is at work naturally, all around us-- neurons, self driving cars, electrons, etc.
In my opinion, he should have stopped before writing the last chapter. He basically ruined his beautiful book by merging it with the Selfish Gene theory and going on a rant about how humans are special. Does he know that any and every time we humans thought we were special, it turned out we were wrong? For some reason, he thinks humans will always control AI. He doesn't seem to be of the opinion that AI itself will be a mashup of human and machine, with no differentiation. He also went on to trash any advances that arise from Moore's Law, which he said was on it's last leg. Tell that to Stephen Hawking who just used tech derived from Moore's Law to create AI nanoships that will literally sail to Alpha Centauri to search for life on the earth-like planet in that system.


The MachineLearning Revolution
The Master Algorithm
Humes Problem of Induction
How Does Your Brain Learn?
Natures Learning Algorithm
In the Church of the Reverend Bayes
You Are What You Resemble
Learning Without a Teacher
The Pieces of the Puzzle Fall into Place
This Is the World on Machine Learning
Further Readings

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About the author (2015)

Pedro Domingos is a professor of computer science at the University of Washington. He is a winner of the SIGKDD Innovation Award, the highest honor in data science. A fellow of the Association for the Advancement of Artificial Intelligence, he lives near Seattle.

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