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.
l'enfer c'est les autres
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).
Would recommend to anyone who has the will power to tolerate the poor narration in order to benefit from the excellent content.
The content is fantastic - Pedro Domingos has written an excellent overview of the field.
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!
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.
Great topic. Text is dense. Narrator sounded like and infomercial announcer. Not learned or academic like the author is.
pretty amazing that the author could teach artificial intelligence techniques so well in an audiobook skip the Stanford masters degree and listen to this book instead.
I hate to call out the performer here but he started of a little bland and it was a tad bit hard to follow.
However I really feel like he picked up his game toward the end of the book and made the listening experience better.
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!
I like the overview of the current state of the art of Machine Learning. I do not care for the hand waving dismissals of people like Chomsky at the beginning or the handwaving dismissal of some of the ethical issues at the end. The pace of the speech was very slow and plodding. But I used the app to speed it up 1.5x and it was mostly fine.
Covers the history of machine learning and explores its future potential. Informative and well presented.
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