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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
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.
32 of 35 people found this review helpful