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

3.76  ·  Rating details ·  5,672 ratings  ·  551 reviews
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
Hardcover, 352 pages
Published September 22nd 2015 by Basic Books (first published September 8th 2015)
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Average rating 3.76  · 
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Oct 24, 2015 rated it it was ok
Review moved to gwern.net. ...more
Brian Clegg
I am really struggling to remember a book that has irritated me as much as this one, which is a shame because it's on a very interesting and significant subject. Pedro Domingos takes us into the world of computer programs that solve problems through learning, exploring everything from back propagating neural networks to Bayesian algorithms, looking for the direction in which we might spot the computing equivalent of the theory of everything, the master algorithm that can do pretty much anything ...more
Maria Espadinha
Mar 05, 2018 rated it really liked it
Remembering Sophia...

While reading this book, the image of Sophia was constantly assaulting my mental screen!
I'm sure most of you remember that gorgeous social robot, that could blink, smile, raise an eyebrow, and was capable of 59 more facial expressions.

She has been a media darling, showing herself in magazines, newspapers, tv news, talk shows,... spreading her charm all over the world.
She could handle a clever conversation, make eye contact and even show some sense of humour!

Sophia dreams abo
Simon Clark
Sep 14, 2018 rated it really liked it
This review is a combination of 3- and 5 star reviews, so on average a 4 star rating.
I give these two ratings depending on who is reading this review. If you are a total novice in the world of computer science, or science in general for that matter, then this will likely be a 5 star book. It does a great job of introducing not just the concepts in machine learning, but also statistical ideas like variance, over-fitting, and even principal components. The key word there however is concepts. If yo
Mario the lone bookwolf
Dec 09, 2018 rated it really liked it
An algorithm to quantify them, find them all, drive them into social media and tie them as customers.

Please note that I have put the original German text to the end of this review. Just if you might be interested.

The advantage of the best algorithm will lie in its autonomous development and improvement, to which nothing can catch up with. Just as a beginner has no chance against a professional with decades of training. However, the AI takes only moments or maybe days to surpass the level of a hu
Gary  Beauregard Bottomley
Oct 13, 2015 rated it it was amazing
The author states that "intuition is what you use when you don't have enough data". The author will show heuristically how intuition is slowly being taken out of analyzing big data and being replaced with algorithms which teach themselves how to make the data speak for themselves. "All learning starts with some knowledge" (a quote from Hume, that the author invokes), and from Hume we know that there is a problem with induction, no matter what the particular can not prove the universal. The trick ...more
Jun 24, 2019 rated it really liked it  ·  review of another edition
This book is filled with complex computer science concepts. I got halfway before getting lost in most of the content. It is hard to grasp unless you've studied computer science. Essentially, this book is about the quest to create 'The Master Algorithm,' which can create all other algorithms. There won't be a need for a human to create specific applications for each desired need, like an app that is only capable of playing chess. The Master Algorithm will be able to learn how to create other apps ...more
Sep 15, 2016 rated it it was ok
I like Pedro Domingos. He has some very nice accessible papers, and he seems like a nice guy (having done an online course, being open source fan, etc etc).

But, this book is a pile of crap. Despite his best efforts, Domingos isn't a novelist, which makes the writing a bit cheesy. Putting that aside, I think that the book has several problems:

- The entire premise of the book is that a master algorithm exist. I don't think that we have any idea about that yet.
- The separation of machine learning p
Nov 02, 2016 rated it did not like it
Inane verbiage with no educational content. Just constant fawning and endless lists of potential and current applications of learning algorithms. The writing is beyond tiresome. Here's just one paragraph:

"You’ve reached the final stage of your quest. You knock on the door of the Tower of Support Vectors. A menacing-looking guard opens it, and you suddenly realize that you don’t know the password. “Kernel,” you blurt out, trying to keep the panic from your voice. The guard bows and steps aside. R
Jan 02, 2016 rated it liked it
Good overview of machine learning. The master algorithm seems like an overwhelming concept at first, but the book is very accessible for anyone who has a basic education in comp sci. However it's pretty clear that the intended audience is nerdy high school boys considering a career in machine learning, despite the author saying this book is written for everyone/anyone.

Also, I can't help but find his depiction of the post-master algorithm world creepy. Call me old fashioned.
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 ...more
Brian Nwokedi
Oct 19, 2015 rated it liked it
From a content standpoint, The Master Algorithm by Pedro Domingos is a great crash course for anyone who is interested in learning more about machine learning. But from an “ease of comprehension” standpoint, this book is far from the layman’s journey that Domingos claims it is.

I found myself able to follow roughly 70% or so of the technical content of this book, and there were definitely some times that it was a bit too technical for me to completely grasp what he was trying to say. The writing
Roxanne Russell
Jul 04, 2016 rated it really liked it
Shelves: biz, pop, science
Bill Gates put this book on his list of recommended reads this year. It interested me because of my work on an ed tech tool to help young adults read better and enjoy their reading experience more. To get the tool right, we have had to integrate artificial intelligence and to make it better we will need machine learning. We have experts on the team for that, but I like to know what's going on around me. I can't pretend I now fully understand machine learning, but Domingos did an excellent job su ...more
Oliver Sampson
Jun 27, 2016 rated it it was amazing
While coached as a guidebook to help find "The Master Algorithm," the one AI algorithm "that will rule them all" (his words, not mine), this book is much, much more. At times written whimsically, and at times treating very advance material in a way that non-sophisticated readers can understand, the book is part history lesson, part cultural commentary, and part description of the scientific process. I work exactly in the field of Artificial Intelligence and Machine Learning, and I am definitely ...more
Sep 29, 2015 rated it liked it
I found the Master Algorithm both enlightening and frustrating. Domingos does an excellent job explaining the 5 basic approaches to machine learning, but later in attempting to unify these fields he quickly lost me. He references information from earlier chapters as if the reader is an expert or professional, not as a novice newly introduced to the topic. My personal experience in computer science includes several college level software coding, computer hardware design, and computing mathematics ...more
Alex Zakharov
May 05, 2017 rated it really liked it
I have been doing a bit of ML professionally and of course also following the avalanche of AI hype that has been sweeping through media and industry for a handful of years, and the noise is only getting louder. In fairness a decent chunk of that hype is deserved – data science is eating the world rejuvenating UBI discussions and mitochondria alike. Sure, Kurzweil is a bit crazy and Hawkins is a bit paranoid but Chinese have been mapping out IQ at a genetic level and just opened the first nationa ...more
Jul 30, 2016 rated it it was amazing
Shelves: non-fiction
"The statistician knows that prediction is hard, especially about the future, and the computer scientist knows that the best way to predict the future is to invent it, but the unexamined future is not worth inventing."

"... The greatest benefit of machine learning may ultimately be not what the machines learn but what we learn by teaching them."

Although I didn't agree with all of the points about the present and future of AI in this book and there were a lot of fanciful metaphors being tossed aro
Dec 13, 2016 rated it really liked it
I've finally came around to finishing this book after I started reading it more than a year ago. The Master Algorithm attempts to present a high-level overview of machine learning for the non-technical reader. The author describes the five different 'tribes' of machine learning (analogizers, evolutionaries, Bayesians, connectionists, and symbolists). The author also talks about unsupervised learning and attempts (although in a very superficial way) to combine the five different tribes into one t ...more
Oct 06, 2016 rated it really liked it  ·  review of another edition
Shelves: read_2017
I definitely enjoyed and appreciated this a lot more on my second read. The key difference this time was that I've finally been digging into the machine learning world a bit and had more context on which to connect the ideas. ...more
Mar 29, 2019 rated it did not like it
I have an interest in this topic. I would love to "crack the code" on something but this was repetitious and unreadable. If you are considering this and want more detail on my opinion, please review the other 1-star reviews as I agree with nearly all of them. Could not finish. ...more
☘Misericordia☘ ⚡ϟ⚡⛈⚡☁ ❇️❤❣
A good learner is forever walking the narrow path between blindness and hallucination. (c)

No hallucination this time. Just a fun & very high-level escourse into the realm of coulda-woulda and what we have already in terms of algorithmification, generalized machine intelligence and the realms of their possible uses.
Tiffany C.
Mar 27, 2016 rated it it was amazing
This book was an Amazon recommended read (thanks AMZ algorithm!), and I'm so grateful. It's a fascinating read about the various AI philosophies and a prediction for the future of/with machine learning. I laughed; I pondered; I learned about learning. It was a bit heavy at times on (typically mathematical) concepts that would be more easily understood by a CS major, but overall Domingo's writing style actually enabled me to understand more than I had expected. He has such a captivating and persu ...more
Semen Frish
Oct 09, 2017 rated it it was amazing
In short it's totally inspiring :) some parts on statistics and probabilities are kind of not too active and in general math made as simple to understand as it possible. The book is much more on philosophy, computer science core concepts and people learning than on artificial intelligence and machine learning. Indeed everything to know to start and go further and no to be stuck with ML is here :) Strongly recommended! #AI ...more
May 19, 2018 rated it did not like it
Shelves: computer-science
I took Prof. Domingos's data mining course at the UW night school in 2001, and I was wondering whether this book can give a good overview of the progress of the machine learning since then. It doesn't. It is plainly badly written. If I were a layman, I wouldn't have understood anything. ...more
DeAnna Knippling
May 27, 2021 rated it it was amazing
A pop-science primer on machine learning.

I liked this quite a bit; it was enough of an explanation to make online articles on more recent AI/ML developments make sense. A smattering of technical details are provided for context, but can be skimmed. This is no Goedel, Escher, Bach--but not entirely unike it, either. Reading this makes me want to take another stab at GEB.

Highly recommended if you're interested in the AI/ML field.
May 17, 2016 rated it really liked it  ·  review of another edition
Finally, a factual account of machine learning to help balance out the crazy headlines that have more to do with "The Terminator" than reality. It doesn't get everything right, but I think that it does a solid job of piercing the veil on machine learning (at least to a degree) and should attract some outsiders to join the machine learning ranks. I'd say anyone who is spending their free time preparing to welcome their new robot overlords should read this book.

Domingos sets out to describe the va
Dec 06, 2015 rated it really liked it
Machine learning is a fascinating subject, the stuff of sci-fi legend and something that is often misunderstood and feared by many. Computers are getting more and more intelligent, aided by man, yet nonetheless they are playing an increasingly important part in our lives, whether it is getting a film recommendation from Netflix or the development of driverless cars.

We are not yet there with machine learning perfection. Boffins are still seeking the most powerful algorithm of all, the so-called M
Dec 02, 2015 rated it really liked it
The Master Algorithm (2015) by Pedro Domingos looks at machine learning and describes the possible impact of machine learning on society and provides a survey for layman of major methods used in machine learning. Domingos is a Professor at the University of Washing in machine learning who also has an online course for learning more about Machine Learning on Coursera.
The book is perhaps a bit too keen in boosting machine learning, but it may be that the impact of machine learning is going to be a
May 26, 2019 rated it liked it
Shelves: non-fiction, science
'The Master Algorithm' (not the book) is a computer science equivalent to 'the theory of everything' of the universe.
One algorithm that does it all.
It is a very nice book. But at times it will throw at you some basic machine learning (ML) jargon, which, if you are not familiar with the field, you will need to Google. The author starts with examples of how ML is already part of our lives, and lists out problems still needed to be solved. Problems so complex that can only be solved by powerful ML
Paige McLoughlin
A very good introduction to machine learning and the neural networks that support it. Incorporating Bayes learning in the connections made while going over data in a practice set to do things like categorizing ambiguous or vague data. Using evolutionary algorithms with the best solutions surviving to compete and reproduce even an algorithmic form of sex mixing successful programs hoping to get hardy hybrids. An excellent book to learn the contours of the field even if you don't code. Interesting ...more
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