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The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World by [Pedro Domingos]

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Pedro Domingos

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A spell-binding quest for the one algorithm capable of deriving all knowledge from data, including a cure for cancer

Society is changing, one learning algorithm at a time, from search engines to online dating, personalized medicine to predicting the stock market. But learning algorithms are not just about Big Data - these algorithms take raw data and make it useful by creating more algorithms. This is something new under the sun: a technology that builds itself. In The Master Algorithm, Pedro Domingos reveals how machine learning is remaking business, politics, science and war. And he takes us on an awe-inspiring quest to find 'The Master Algorithm' - a universal learner capable of deriving all knowledge from data.

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322 pages
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English
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Penguin
Publication date

22 Sept. 2015
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1671 KB
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"Pedro Domingos demystifies machine learning and shows how wondrous and exciting the future will be."--Walter Isaacson, author of Steve Jobs and The Innovators

"An impressive and wide-ranging work that covers everything from the history of machine learning to the latest technical advances in the field."--Daily Beast

"Domingos writes with verve and passion."--New Scientist

"Unlike other books that proclaim a bright future, this one actually gives you what you need to understand the changes that are coming."--Peter Norvig, Director of Research, Google and coauthor of Artificial Intelligence: A Modern Approach

"Domingos is the perfect tour guide from whom you will learn everything you need to know about this exciting field, and a surprising amount about science and philosophy as well."--Duncan Watts, Principal Researcher, Microsoft Research, and author of Six Degrees and Everything Is Obvious *Once You Know the Answer

"[The Master Algorithm] does a good job of examining the field's five main techniques.... The subject is meaty and the author...has a knack for introducing concepts at the right moment."--The Economist

"Domingos is a genial and amusing guide, who sneaks us around the backstage areas of the science in order to witness the sometimes personal (and occasionally acrimonious) tenor of research on the subject in recent decades."--Times Higher Education

"An exhilarating venture into groundbreaking computer science." --Booklist, starred review

"[An] enthusiastic but not dumbed-down introduction to machine learning... lucid and consistently informative... With wit, vision, and scholarship, Domingos describes how these scientists are creating programs that allow a computer to teach itself. Readers...will discover fascinating insights."
--Kirkus Reviews

"This book is a must have to learn machine learning without equation. It will help you get the big picture of the several learning paradigms. Finally, the provocative idea is not only intriguing, but also very well argued."--Data Mining Research --This text refers to an alternate kindle_edition edition.

About the Author

Pedro Domingos is a professor of computer science at the University of Washington. He is a winner of the SIGKDD Innovation Award, the highest honour in data science. A fellow of the Association for the Advancement of Artificial Intelligence, he lives near Seattle. --This text refers to an alternate kindle_edition edition.

ASIN ‏ : ‎ B0147SEZ92
Publisher ‏ : ‎ Penguin; 1st edition (22 Sept. 2015)
Language ‏ : ‎ English
File size ‏ : ‎ 1671 KB
Text-to-Speech ‏ : ‎ Enabled
Screen Reader ‏ : ‎ Supported
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Enabled
Word Wise ‏ : ‎ Enabled
Print length ‏ : ‎ 322 pages

Best Sellers Rank: 203,639 in Kindle Store (See Top 100 in Kindle Store)
- 24 in Programming Algorithms
- 140 in Algorithmic Programming
- 233 in History & Philosophy on Science & Nature

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SimpleSimon

Poorly edited but very valuable book

Reviewed in the United Kingdom on 11 December 2020

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Like: The core chapters are a good introduction to five machine learning approaches and from that the structural elements of learning. The author's description of how he and team abstracted and combined these elements into a synthesized algorithm, Alchemy, is told in a lively, convincing and interesting way.

Dislike: First, it's not clear who the target audience is. The 'handwaving' opening and closing chapters seem aimed at the non expert. However, the core chapters demand a basic appreciation of Markov, Bayes and probability, which the average reader won't have. Second the language is idiosyncratic - particularly where the author refers to 'learning a model', by which he means 'training' or 'teaching' it logic and structure. While I recognize the lack of a good word for this, 'learning' doesn't seem right at all. I wondered if this was caused by translation from Portugese, but either way I think its the fault of Basic Books not doing their job as editors. The lack of good editing is a serious detraction and makes key sections far too difficult to read. This is noted by other reviewers who take a harsher line, but on the whole this detraction didn't diminish my overall rating.

3 people found this helpful

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kazordoon

The current state of Machine Learning

Reviewed in the United Kingdom on 28 April 2019

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The book describes the actual state of Machine Learning, explaining the main 5 styles in the field, and a little history about them.

It isn’t a technical textbook, so it doesn’t go into deep detail, but it can get a little too abstract, or sometimes some mathematical terms are explained in such a superficial way, that if you didn’t know them, you won’t understand his explanations, e.g. Markov Chains, Bayes Theorem, etc.

However, you get a general idea of the main differences between the five styles in machine learning, and the ways their algorithms try to find solutions to different problems. You won’t be able to program a neural network after reading the book, though!

The main point of the book is, like the title says, to find a Master Algorithm, since in his opinion, those 5 styles of Machine Learning are not enough to solve all the problems. They have a field where they shine, but they are not general solution finders. He proposes to combine the 5 styles to create one final style that will solve any problem. I was a little disappointed he didn’t propose a total new paradigm, but instead he proposes to combine and patch different parts of the other 5 styles, like a Frankenstein monster that will do the job.

2 people found this helpful

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Fluff

Had been really looking forward to this book, but once I'd started, I just wanted to get it over with. Very frustrating read.

Reviewed in the United Kingdom on 27 November 2015

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Pedro Domingos has to be given huge credit for being the first person to attempt a 'popular science' type book on the subject of machine learning. This is by no means an easy task; there is a lot of material to cover in this area and some of it is complex. That said, I think this book could have been a lot better. I did actually like the author's writing generally, but I felt the book was poorly structured, the technical explanations in particular were poor, and after the final chapters I was left wondering if the entire book was less “impartial” than the prologue and early chapters might have lead me to believe.

Whilst I think there's stuff in the book that could be improved, it's hard for an author to make those kind of improvements without feedback. I can't help but think that the input of editors/agents/publishers should have made this a much better read. It seems to me that this book was crying out for a ghost-writer/co-author experienced in 'popular science' writing. More importantly, I think it desperately needed proof reading by people unfamiliar with machine learning. My guess is that drafts of this book were read primarily by people highly experienced in this subject, i.e. people who already understood the material being presented. I'm really struggling to see how someone at Basic Books could have read the book prior to publication and thought, “Yep, that was relatively easy to follow”.

The prologue claims the book is intended for a wide range of readers, pretty much from novice to expert. Personally, I would suggest that the only people who won't struggle with this book are experienced industry based practitioners and academics/researchers/post-docs in this area (and I guess also, readers who are happy to just skim sections they don't understand). To give some perspective, I have a grasp of ML basics, I currently work in a ML group (although my research is not explicitly ML), I've taken introductory and advanced masters units in the subject and have implemented some of the algorithms myself: Yet I struggled to understand much of this book – both the specific details and the broader overview.

I may reread some of this book in a couple of years when I have a bit more understanding and experience under my belt. Perhaps then I will appreciate the details and the author's perspective better. At my current level of experience, I found the book very hard going, a frustrating read and I don't feel like I learnt very much from it. For the time being, I will stick to machine learning text books.

Specific comments in no particular order (mostly negative - sorry):
- Key fundamentals of the subject are either, not explained (e.g. 'hypothesis' as an ML term), poorly explained (e.g genetic algorithms are explained devoid of any mention of 'selection'), or left till way too late in the book (e.g. supervised v unsupervised learning left till p203). ---This latter example is a real shame in my opinion, as I found this to be one of the best written chapters of the book.
- Many other areas did not get the foundational explanations needed prior to developing ideas further. Example: “The most important question in any analogical learner is how to measure similarity.” This crucial question is raised, then frustratingly left unaddressed because the subsequent text lacks any specific coverage of how this may be actually achieved.
- I felt that 'hypothesis', 'feature'/'attribute', 'label', 'example' should have been explicitly defined, in the context of what they mean for ML, right at the start of the book. This would have given the reader a much firmer footing before seeing further explanations using these terms routinely. Non ML readers will not appreciate, for example, the very specific use of 'hypothesis' wrt discussing ML algorithms.
- The first few pages of the prologue has been described by another reviewer as (IIRC) evangelistic nonsense – It's a harsh comment but it's pretty much on the nail. The author is right to highlight the role ML already plays in our world, but he's overstating the case (for most people) almost to the point of ridiculousness. Most people's lives aren't like this (even in computer science). It's not an inspiring start to the book.
- In the first 3 chapters 'very little' happens. I think this needs some significant condensing (or better, replacing with a chapter that covers fundamentals, gives some sense of supervised vs. unsupervised learning, and talks about how to measure similarity). As it is I had to wait to page 93 before we got started on ML proper.
- The book is crying out for some diagrams. Of the few that are used, some could have easily been better. The important diagram illustrating the “five tribes” is neither intuitive nor informative. And why, when illustrating SVMs, would you not show the margin in the diagram? – Thus immediately giving an intuitive idea of the role of the support vectors and also differentiating SVM from the simpler classifier diagram.
- There are places in the book where, if you're anything like me you'll be tearing your hair out for want of an explanation. In describing how nearest neighbour works, simply (and only) saying that: “It consists of doing exactly nothing” - is incredibly unhelpful, especially when the next few pages go into the algorithm in more detail, in the absence of a foundational explanation of how it works.
- Whilst in general, I liked the writing style, at times I found phrases and expressions used by the author to be pointlessly obscure – thus confusing the reader further, rather than clarifying. Two examples: i) the heading “One if by land, two if by Internet” is probably baffling to the majority of people (who won't be aware of the original American War of Independence phrase it's derived from---and especially so for readers outside of the US) and so it doesn't help bring focus, or coherence or clarity to the subsequent text that it introduces. ii) “As Isaiah Berlin memorably noted, some thinkers are foxes---they know many small things---and some are hedgehogs---they know one big thing”. Metaphors are a great way of using an analogy to re-frame a difficult idea in familiar terms. They can thus render something complex or intractable as immediately intuitive. This doesn't work if the chosen metaphor is as unfamiliar or obscure as the concept it is supposed to explain!
- “S curve” ...this term used throughout the whole book. In a book at this level, what's wrong with just calling it a sigmoid?
- “We routinely learn MLNs...” ---It's a trivial point and I imagine I'll be accused of being a grammar pedant, but this occurs more than once in the book and I am sure other people will cringe at it. The correct word, if you don't want to use “teach”, is surely “train” ( or alternatively rearrange along the lines of “we let the algorithms learn” or “the algorithms learned”).
- Chapter 9. i) I wasn't particularly enthralled by the storytelling/fable approach the author used here. That's just my personal view---others may like it. ii) What particularly bothered me here was that suddenly it seemed that the book was no longer a general introduction/overview of machine learning and more a way to promote the author's own area of research. There is nothing wrong with an author presenting a partisan perspective, but I would have liked to have had a better sense that this was what was happening from the start, as the book till then had seemed like a (relatively) impartial broad coverage of the field (more experienced readers may have a different take on this). iii) The failure to convey an intuitive sense or simple understanding of the various algorithms in earlier chapters of the book meant that when they all were all bundled together in one chapter, I became totally lost. For me this chapter was a total train wreck.
- Chapter 10 presents an interesting but somewhat rose-tinted view of an ML future. The author is clearly much better informed than I, in this area, but I still think that this could have been a little bit more 'balanced'. I enjoyed the ideas, but the whole chapter neatly sidesteps the important “elephant in the room”--- that advanced MLs are inevitably likely to end up in the hands of the 'most powerful' (at least initially) – rather than the 'most benevolent' or 'philanthropic'. I also felt that there were a few occasions in this chapter where he was presenting his personal opinion as fact-–a personal bugbear of mine. Examples i)…. technology develops as an 'S-curve' (aaargh) rather than exponentially, ii) ...that we are at the end of Moore's law (again).
- I particularly liked the adapted quotes presented in this chapter and the subsequent epilogue: “any sufficiently advanced AI is indistinguishable from god” and “the unexamined future is not worth inventing” ... nice touches (but only in those cases where it doesn't confuse, if the reader doesn't 'get' the reference!)
- The book has a good reading list at the back (albeit with a few surprising absences), however, frustratingly the book does not cite sources in the main text for when specific topics (scientific work) are being discussed.
- The index is excellent.

116 people found this helpful

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Martin Bodocky

Great summary where we are right now with AI overtaking the world.

Reviewed in the United Kingdom on 21 November 2016

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This is not read for entry level machine learning practitioner. I have been working and reading about Artificial Intelligence for more than 5 years and I'm just ready to understand what the author has mentioned there. So it can feel quite frustrating to reader who believed this is book without math, but it's book with logic and connections among many machine learning algorithm and practices.

I enjoyed author's jokes and small little stories it does enrich and fresh discussion about not always super cool topics (yes I speak about Bayesian). Overall I will recommend it to advance AI practitioners who want to design and share next episode of AI understanding world.
I will copy one of great thought from this book.

"People worry that computers will get too smart and take over the world, but the real problem is that they are too stupid and they have already taken over the world."

This is solely my impression, because author sometimes goes into great details. Nevertheless,amazing summary about current stage of AI.

7 people found this helpful

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