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Publisher's Summary

Under the aegis of machine learning in our data-driven machine age, computers are programming themselves and learning about - and solving - an extraordinary range of problems, from the mundane to the most daunting. Today it is machine learning programs that enable Amazon and Netflix to predict what users will like, Apple to power Siri's ability to understand voices, and Google to pilot cars. These programs are already helping us fight the war on cancer and predict the movements of the stock market, and they are making great headway with instant language translation and discovering new laws of nature.

But machine learning is incomplete, and its practitioners across the globe are seeking the most powerful algorithm of all. The Master Algorithm will not be limited to solving particular problems but will be able to learn anything and solve any problem, however difficult, and Pedro Domingos, a trailblazing computer scientist, is at the very forefront of the search for it. With the Master Algorithm in hand and data as its fuel, machine learning - essentially the automation of discovery, a kind of scientific method on steroids - will become the most powerful technology humanity has ever devised. And The Master Algorithm will be its bible.

©2015 Pedro Domingos (P)2015 Brilliance Audio, Inc.

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  • Overall
    3 out of 5 stars
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    3 out of 5 stars
  • Gary
  • Las Cruces, NM, United States
  • 10-16-15

Let the Data Speak for themselves

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 is to get from the data (the particular) to the universal and the author explains in detail the five general ways we learn and shows how they work in practice. The five ways are Symbolic (think: rational thought), Connective (modeling like the Network in the brain), Bayesian (nothing is certain and all is contingent), Evolutionary (see "The Selfish Gene" by Dawkins), and by Analogy.

The key is to use some variations of the ways ('tribes') and have the method (algorithm) use the data to exploit the information that is within the data set and do it recursively (and as Douglas Hofstadter says "I am a Strange Loop"). The computers are becoming faster, cheaper and can manipulate ever larger and more easily accessible data sets, and the methods have become more refined and usable. For example, brute force Bayesian methods are not used since the whole decision tree necessary for learning complex solutions are never practical and are now replaced by naive Bayesian techniques (only some of the dependent states need to be computed) giving only a small loss in overall accuracy.

The overall point of the book is to show that there is evolutionary thinking going on in writing smart algorithms which are able to let the data speak for themselves and the computer scientists have a tool box of techniques which enable real objective knowledge to be extracted from the data.

I like the TV show Person of Interest. Everything that "The Machine" does on that show can be explained by the techniques discussed in this book. This author doesn't think the computer will ever be able to think or have its own "will". I think this book would be an excellent lead in to the Nick Bostrom book "Superintelligence: Paths, Dangers and Strategies". That book does think super AI will happen and a computer will develop a 'will'. This book, "Master Algorithm" is an excellent primer for someone who believes the "singularity is near" even though the author disagrees (It's odd this author thinks the super AI is not possible because the way he starts off the book by explaining the P=NP problem and how solving that could create a master algorithm which in my way of thinking would lead to a super AI).




30 of 31 people found this review helpful

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Great book, irritating narration

Would you recommend this audiobook to a friend? If so, why?

Would recommend to anyone who has the will power to tolerate the poor narration in order to benefit from the excellent content.

What did you like best about this story?

The content is fantastic - Pedro Domingos has written an excellent overview of the field.

What didn’t you like about Mel Foster’s performance?

The narration is by someone who sounds like they are straight out of an ad from the 1950's. He has that sort of "gee whiz, golly gee" tone, with every sentence seeming to end with an exclamation mark, and pronounces the word "computer" like my grandmother used to while she was still alive: sort of like "commuter" (I'm 50, so my grandmother was born in the 1800's -- you get the picture).

I suggest that audible use a learning algorithm next to choose narrators for each content genre that will resonate properly with the audience -- this one is WAY off base!

40 of 44 people found this review helpful

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Yet another book that presents machine learning as magic solution to take us nirvana.

I welcome any book that tries to dispel the myths and break down the complexity into something that accessible...This is not that book.

Machine learning is a great idea, fire your software engineers and have an algorithm the train itself on your data to give you better results.

Unfortunately it doesn't work very well. It takes a highly trained PhD data scientist to select and tune the algorithm to achieve this magic.

Fortunately, this author has the solution. The master algorithm! Well he doesn't have the master algorithm but spends half the book arguing that it would be really great idea if someone would find it. Oh, and then he presents his ideas that kind of get some way towards that algorithm but don't quite work very well.

While there are many parts of the book that are enlightening and informative, the book is let down by grandiose posturing and over complication of the inner working of machine learning.

I really did not need another lecture on the moral guidance for how to live in a world ruled by machine learning. Let's leave that to the science-fiction writers of the 1940s and 50s who frankly did a much better job.

34 of 38 people found this review helpful

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Not a master story teller

Great topic. Text is dense. Narrator sounded like and infomercial announcer. Not learned or academic like the author is.

6 of 7 people found this review helpful

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Unending repetition

After listening to the first 4 chapters, I can't go on. I'm a computer science student, and I really wanted to listen and learn from this book, but the author spends 2/3 of the time restating what they already said.

It just got annoying to hear the same point in 23 slightly different ways.

I would not suggest listening to this book.

2 of 2 people found this review helpful

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Great listening

Great listening. If you want to get deeper, the author is going to offer a Machine Learning course on Coursera.
Suggestion: It would be great if the book pictures were made available to the listener, in special those of the chapter 9 -- then I would give it 5 stars!

8 of 11 people found this review helpful

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Just way too high level and full of fluff

This frustrated me because I invested hours of time waiting for it to get deeper and more technical, but eventually got exhausted waiting. I was looking for another deep book after finishing Superintelligence.

1 of 1 people found this review helpful

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    3 out of 5 stars
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Not the best book for an audio recording

The ending of this book was good. But the larger chunk of this book was very dry and very hard to follow.

The analogies used to explain the concepts needed to be better for such a subject where the reader has no background in the subject matter. The concepts in the book came together over time though. But I think I'd have to read it again to get everything. (Which I have no plans on doing)

I would recommend the book , I guess . Only if you have strong interest in machine learning. And some time to devote to the concepts in the book.

1 of 1 people found this review helpful

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lackluster

I couldn't finish the book , it's​ just running around in circles . couldn't get a useful piece of information for a good while

1 of 1 people found this review helpful

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Not much meat to this.

I was hoping for a deeper dive, but it never went there. This never went beyond a summary of a summary. And, the reading was awkward.

1 of 1 people found this review helpful