With wit, vision, and scholarship, Domingos describes how these scientists are creating programs that allow a computer to teach itself.

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Why are quintessentially geeky places like DARPA and Google suddenly interested in talking about something as profoundly ungeeky as babies? It turns out that understanding babies and young children may be one key to ensuring that the current "AI spring" continues--despite some chilly autumnal winds in the air. In the past, scientists unsuccessfully tried to create artificial intelligence by programming knowledge directly into a computer. Now they rely instead on "machine learning"--techniques that let the computers themselves work out what to do based on the data they see. These techniques have led to amazing breakthroughs.

The future of artificial intelligence depends on designing computers that can think and explore as resourcefully as babies do.

With wit, vision, and scholarship, Domingos decribes how these scientists are creating programs that allow a computer to teach itself.

With wit, vision, and scholarship, Domingos describes how these scientists are creating programs that allow a computer to teach itself.

It may sound trite, but humanity has come to dominate the world using this tool alone. Humans lack natural weapons, have no natural protection from the elements, and enter life as helpless infants. But our unique brains allow us to acquire, use, and communicate knowledge, and this advantage alone has allowed us to create the intricate social and technological reality we now inhabit. Our brains evolved to process, store, retrieve, and integrate sensory data into working knowledge that allows us to navigate reality. Until recently, humans were the only significant force that could translate raw data into accurate, actionable knowledge.

With wit, vision, and scholarship, Domingos describes how these scientists are creating programs that allow a computer to teach itself.

The Master Algorithm is a book about Machine Learning. But while it claims to be about its future, the focus is more on its past. Domingos' gives us a tour of the five different Machine Learning fiefdoms (Evolutionaries, Connectionists, Symbolists, Bayesians, and Analogizers), how they connect, and what their histories are. At times the discussion gets a little too technical. Sentences like "...we can view what SVMs do with kernels, support vectors, and weights as mapping the data to a higher-dimensional space and finding a maximum-margin hyperplane in that space" (pg 196) make it clear Mr. Domingos is no Malcolm Gladwell.

What are the fundamental limitations inherent in machine learning systems? That's the central question of a potential new DARPA program known as the Fundamental Limits of Learning (Fun LoL) which according to the researchers will address how the quest for the ultimate learning machine can be measured and tracked in a systematic and principled way. "It's not easy to put the intelligence in artificial intelligence. Current machine learning techniques generally rely on huge amounts of training data, vast computational resources, and a time-consuming trial and error methodology. Even then, the process typically results in learned concepts that aren't easily generalized to solve related problems or that can't be leveraged to learn more complex concepts. The process of advancing machine learning could no doubt go more efficiently--but how much so? To date, very little is known about the limits of what could be achieved for a given learning problem or even how such limits might be determined," DARPA stated.