Amazon.com: Customer reviews: The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

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Awesome and Unique Comprehensive Yet Readable Overview of ML

Reviewed in the United States on July 14, 2016

AWESOME OVERVIEW OF ML IDEAS

I have read a few textbooks on machine learning (intro to Statistical Learning by Hastie etc) and so I would say that my knowledge of ML is at the "textbook overview" level. Since I am not a ML practitioner, I may not be the best judge of a book such as this one, it was a fairly difficult read, and I know I need to read the book a second time to get an even greater appreciation and understanding of the concepts covered by the author. That being said, it was a very enjoyable book. The book was very different from any ML book that I've read or checked out either at a bookstore or online. I think one needs to have some knowledge of ML to appreciate the book - concepts like supervised learning, unsupervised learning, Bayesian inference, support vector machines, neural networks, etc .... The book deserves a 5-star rating because it added a lot of value to my understanding of ML, and increased my desire and curiosity to learn more about the field of ML ....

36 people found this helpful

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A. Menon

Good overview of machine learning techniques for non-expert but overall a mixed book

Reviewed in the United States on November 10, 2015

The Master Algorithm is a reasonable overview of the growing field of machine learning. The author explores the idea of there being a master algorithm which could explain everything given enough data. The idea seems a bit over the top when initially phrased but the real purpose of the book is to introduce ideas used in machine learning and how they work and are used to solve problems.

The book is split into multiple chapters which start from discussing abstractly the master algorithm and then move on to some of the philosophical issues associated with using such algorithms. In particular the author discusses at the core of believing in pattern recognition algorithms is belief in inductive reasoning. The author discusses human learning and gets into some neuroscience and how neural networks are constructed. The reader gets a vague sense of Hebbian learning and how neuron weighting are at the core of neural networks. The author spends a lot of time discussing various approaches in machine learning and gives the reader an intuitive feel of Bayesian learning. The author was an originator in a particular algorithm called naïve Bayes which greatly simplified solutions to certain problems and so the author introduces his ideas to the reader. Other machine learning ideas are introduced like genetic programming and multivariable regression. The author also discusses other machine learning algorithms which turn data into a vector and then look for close neighbors of the vector to classify the input. The author also spends some time on how unsupervised learning would look. The book combines computer science ideas and intuition and tries to use a fictitious robot as the means to convey ideas about how a computer would learn. The author finally introduces his own master algorithm called alchemy which combines most of the models described in the book. The reader really gets little actual sense of what's going on in the algorithm as the author qualifies one needs a PhD in computer science.

The Master Algorithm is the first book I have seen which introduces some of the ideas being used in machine learning to a general audience. It does so quite well and most of the ideas are absorbable. At the same time there are a few too many instances where the author is self promoting talking about all of the brilliant ideas he has had which have reshaped the field and how other areas of AI research of the past or Kurzweil and his singularity concept are idiotic. Despite probably being right in much of his analysis its arguing with no one on the other side and unproductive. Also the flavor of the writing is odd - it turns into some fantasy literature at times as though that makes the subject more digestible and in fact makes it more irritating. I enjoyed reading aspects of the book and do think the parts on what different schools of machine learning focus on are well written for a non expert, unfortunately there are many other parts of the book which one wants to get through as quickly as possible.

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From the United States

A. Menon

Good overview of machine learning techniques for non-expert but overall a mixed book

Reviewed in the United States on November 10, 2015

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69 people found this helpful

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Shankar Saikia

Awesome and Unique Comprehensive Yet Readable Overview of ML

Reviewed in the United States on July 14, 2016

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36 people found this helpful

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Thorough survey of trends in machine learning since I got my M.S. in C.S. in 1989

Reviewed in the United States on September 1, 2019

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Part of the reason I bought this book was to see if I can still understand current issues in computer science, 30 years after I earned my M.S. in C.S. Back then the hot book in the field was The Soul of a New Machine, by Tracy Kidder, which I very much enjoyed. But time moves on, and some issues that were hard then, such as the four color problem, have been solved since. Artificial Intelligence, on the other hand, has not. Back in 1989, I fully expected some computer to have passed the Turing Test by now, but doing so has proved more difficult than then expected, although computers now routinely trounce human experts in even the most complex games, such as Chess and Go.

Thus, I chose this book both to see why we aren't there yet, and to see if I can still even follow the current arguments.

Like The Soul of a New Machine, this book is written to both be accessible to a literate general audience and rigorous enough for specialists. In my opinion, it entirely succeeds, although my son is better than me at math, so I skimmed some of the math-heavy discussions, as my interest is more in the philosophical issues and predictions about coming developments. In that, I was completely satisfied, as just when I was thinking of docking a star because the discussion was getting too deep into the weeds of particular implementations of machine learning, the author returned to a very interesting discussion of what it all means for our current and future society, complete with useful predictions about what jobs are still likely to be around in twenty years, and the inevitability of voters eventually approving a guaranteed basic income for those no longer able to be employed due to robots and computers doing ever more of the work previously done by humans.

I was glad to see the author also explored the dark side of all this - the potential for it to control people rather than to free them, and his suggestions on how to make sure the changes achieve good for all rather than for only a few. I was particularly interested in his conclusion that we needn't worry about machines ruling over us because even the best machine learners lack will, doing only what they have been programmed to do. I also like his idea that although machine learning may be the next step in evolution, it will be one taken with rather than instead of us, with humans branching out along with computers in ways not predictable yet, but likely to be beneficial.

Overall, a very good and very hopeful read, and comforting to still be able to follow along after a full career since grad school and years of retirement.

Definitely recommended!

8 people found this helpful

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Dennis Muzza

Great start but got lost halfway through

Reviewed in the United States on December 31, 2018

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I had very high expectations going into this book, and initially it did not disappoint. The introductory chapter provides a general explanation on machine learning that is second to none, the abstract concept of a "master algorithm" works as a clever construct to frame the problem of machine learning and the five different ways to arrive at it. The author also establishes rapport with the reader by sprinkling his insights with humor (albeit professor-type humor) and the passion that drives him also comes through. Having lost his beloved sister to cancer, he presents many cancer-related examples to show machine learning's potential.

Unfortunately, as he gets into the details of each of the five approaches towards the master algorithm it gets more and more difficult to understand him. I left the chapter on Bayesian learning more confused about Bayes than I did going in, and by the time I got to the final chapter that synthesizes them into a whole, I could not get much out of it. I think what makes this book less accessible to the lay reader are the long sections of dense prose, with no supporting graphics, and the author's tendency to define important concepts, such as "inverse deduction", in passing and not too clearly, and then builds on those concepts to explain even more abstract ideas. At times the author gets carried away in his explanations in the same way that some professors do in their excitement about an idea, oblivious to his having lost the majority of the classroom. In conclusion, the book requires a lot of dedication from the lay reader, it is not to be read quickly, and one can only hope that there will be a second edition that will make it a bit more accessible.

8 people found this helpful

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This book is not for complete noobs/beginners

Reviewed in the United States on May 16, 2018

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This is a great book and covers everything but this book is certainly not for someone without prior knowledge of machine learning and good understand of some principles.

At the start of the book, Pedro mentions that this book is written for the layman but it's not.

I gave this book to a friend of mine before I read it and he was like "doode this is not for laymen, I didn't understand a thing".

While the book is great for seasoned programmers and those starting with machine learning and good knowledge of some concepts, this book ain't for someone who wants to know more about machine learning and algorithms.

I still give it 5 because Pedro is a great guy and this book is indepth.

8 people found this helpful

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Jerry B Schutz

Skip Chapters 1-3 and 9-10

Reviewed in the United States on June 16, 2018

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Chapters four through eight were valuable to me as an introduction to machine learning and the five different approaches described by the author. The first three chapters and final two seems to be wishful thinking on the part of the author. The question I kept asking my self was, "if there is a master algorithm and the existing approaches are spanned from it, why hasn't the author been able to trace the branches back to the trunk and found the grand prize? The way he describes it, they should be back traceable, simply, and elegantly.

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Karl Rosaen

This book isn't perfect, but I'm still giving it 5 stars because ...

Reviewed in the United States on October 18, 2015

This book isn't perfect, but I'm still giving it 5 stars because it provides a better overview of the entire field of Machine Learning than any other book I've come across. My background is in computer science and software engineering and I've been interested ML as more of a hobbyist and outside observer for a few years (reading some books, taking Andrew NG's coursera course), just recently dabbling in some applications professionally. What I was still missing before reading this book was as high a level understanding of where all of the models and technique in the field of ML fit. Other books describe the difference between supervised and unsupervised learning, but this book goes further in describing how, say, decisions trees, support vector machines and deep neural networks fit compared to each other and within which subfields statistics play a larger role than others.

The book also puts many techniques in historical perspective that I found very helpful, such as the rise, fall and rise again of deep neural networks with support vector machines taking a lead as the hottest technique in between (while also making clear that SVMs are a useful technique with unique strengths today). Finally, it makes clear that these techniques are not all competing for being the best overall at everything, but that they can be used quite complementary and/or they have unique strengths within certain problem domains. The book accomplishes all of this through a survey of broad subfields of ML, how each has attempted to be *the* master algorithm, has fallen short in some ways, but remains the best at some things and could play a role in the state of the art master algorithm (while acknowledging we're not quite there yet). So while the term 'master algorithm' is somewhat of a gimmick (as he acknowledges), it's a good way to think about what ML is attempting to accomplish as a field: building working, adaptive software systems with less and less human assistance by learning from data, and to see how many specific techniques have played a role in progress.

What I don't know is how accessible this book might be to someone who's less technical. I think the first couple chapters would be a great read for anyone with a general interest, making clear how ML differs from the traditional software / automation that has brought us so far, but it could be that the details within the rest of the chapters that go into more depth would be too in the weeds.

I've also read some other reviews from technical readers that assert the book lacks enough depth to be helpful, but this wasn't the case for me, in fact the level of detail was perfect—just deep enough to match with details I'd skimmed before in previous surveys of the field yet not so deep that I couldn't get through and enjoy the chapters in a casual evening read. The author also explained some concepts better than I've read anywhere else before, such as the debate is between frequentist and bayesian statisticians.

269 people found this helpful

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Great overview but somewhat disjointed

Reviewed in the United States on July 19, 2020

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This book provides a lot of details on machine learning and introduced me to many new terms and ideas. The way machine learning is described is that the typical method for computing is to give the machine an input and write the algorithm and then receive the output, whereas with machine learning you give the computer inputs and outputs and it writes the algorithm.

The main theme is to integrate all of the current methods to form a master algorithm that can do everything. While this sounds daunting, the author builds up his case starting from describing the current (as of 2015) five main approaches: symoblists, connectionists, evolutionaries, bayesians, and analogizers. Each group is broken down into their formal language used, method of evaluation, and method to evaluate and refine. This history is enough to recommend the book and Domingos does a good job of comparing and contrasting the different tribes. He coves the ubiquitous problem of over-fitting and explains why testing on unseen data is the ultimate verification method.

While there are many interesting ideas, I found a lot of the presentation to be unclear and somewhat vague. This could be due to my ignorance of the subject. He also undercuts his entire thesis at the beginning by granting Hume's problem of induction as a given. This demonstrates the importance of the proper philosophical ideas when addressing fundamental topics.

I am actually less optimistic about all of this after reading this. It still seems very far off with some major obstacles. With that being said, I admire the author's passion and interest in the subject and am grateful to have the information he's provided. I noticed he now works in the finance industry and I would be very interested to hear if he has more or less confidence with Machine Learning now versus when the book was written, over 5 years ago. Even with its faults, highly recommend.

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Shevek

Read it - an exercise on understanding the mindset

Reviewed in the United States on October 6, 2017

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Difficult choice based on an unidimensional 5 stars rating system. The author has deep understanding and long experience in the field. No doubts. That makes the book worth reading 100%

However from an outsider to the Silicon Valley and US tribe his conclusions and recommendations are hard to swallow. Example : it is an ethical and moral duty of any cancer patient to share its data... to companies that will create a model to cure cancer. Good in principle, so this company will make millions to sell a magic cancer cure that only the reaches could afford, and even the poorest person in the poorest corner of the world has the moral duty to share its data and not benefit from it?

More like this in a larger review, but the optimism that the author shows only can rise an eyebrow, after the recent news on the failure of the top machine learners companies (Facebook, Google and Twitter), with their algorithms used to swing electoral decisions, spread fake news (the real fake ones, not the other type) and do not stop, some argue encourage, harassment and hate speech... the jury hasn't decide yet, but a lot is amiss for sure.

However read it: the description of the different algorithms is great for a newbie and layman, good historical context and well explained. First and last chapters is where I strongly disagree with the author: and the reason is because he made political statements, so of course you will have different ones. The impact on technology, why citizens of the world should share its data, have a strong Silicon Valley US perspective. He is open about that: this is open for discussion and better to talk about it rather than hide our heads in a hole. Sure! The author has good intentions and some of his far fetched recommendations are off, again my humble opinion. Particularly you can see that when after mentioning the good impact of AI technology in the labor markets and elaborating on a future utopia (a nation will be more advanced the highest the unemployment ratio is - year, interesting) and next section is about the benefits of intelligent (AI) robot full driven wars: yes, two robots armies fighting to the end in an orgy of mindless destruction like WW1, is more human... but he imagines that everybody will have robot armies, and that they will train the ethics model (when to shoot or not) based on the same rules! Extreme: imagine the ISIS trainer teaching ethics (when shoot/not shoot); and if you think they will not get the tech, imagine Assad getting a grip on the tech... and Putin, anyone really: the logic of battle is that you will use your resources to the maximum benefit for you, meaning killing your opponent with less casualties. Do you want to control that? Do not start a war.

The author is convince that humans will keep to do humans things, and he cannot see the contradiction of one faction or economy of the planet having access to a literally overwhelming technology: asymmetry will be impossible to contain.

The biggest mistake is to put to much faith (literally) in that technology will help create a nice utopia (just after describing the benefits of AI apply to war).

As a final is good reading, the author acknowledges that he has not all the answers, that it is open to discussion and that the ostrich tactic does not work: agree, it does not make sense to stop research, and put fictitious barriers to technological advancement.

25 people found this helpful

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John A.

Great read for machine learning students, a bit too complex for laypersons

Reviewed in the United States on August 4, 2020

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Ultimately, this book was able to guide me to a basic understanding of several approaches to machine learning, potential directions similar algorithms may take us in the future, and several surprising insights about how humans learn. I am glad I read it, and I would recommend it to students of machine learning. Domingos is not just an expert in his field; he understands how machine learning draws from and influences many other fields of study: ethics, philosophy, psychology, neuroscience, genetics, economics, politics, etc. His apt quotes and examples bring these connections to life. I especially enjoyed his illustrations in the form of poetry and fantasy.

However, for a lay person looking for a guide to the machine learning revolution, this book seems like it has been mauled by the complexity monster. Each promising "tribe" of machine learning, complete with its backstory, is explained in technical detail far beyond what I was looking for. Domingos does provide illustrations and fitting analogies to mitigate this, and he tells that the scientists of the tribes of machine learning are like the proverbial blind men touching the elephant: "Our aim is to touch each part without jumping to conclusions; and once we’ve touched all of them, we will try to picture the whole elephant." But I didn't really want to feel the whole elephant, especially not the yucky parts.

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