I recently finished The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World, by acclaimed computer science professor Pedro Domingos of the University of Washington. The Master Algorithm is aimed at a broad but well-educated audience and attempts to serve as an intermediate between dense, technical textbooks and simple, overly-hyped 800-word “AI IS GOING TO DESTROY PEOPLE” newspaper articles. The main hypothesis is that there exists a universal algorithm for solving general machine learning problems: the master algorithm. Or, as Domingos humorously puts it, one algorithm to rule them all. For background information, Domingos provides a historical overview of five “tribes” of machine learning that we must unify and understand to have a chance at unlocking the master algorithm. In general, I think his master algorithm idea has merit, and his explanation of the five areas of machine learning are the most important and valuable parts of the book. Nonetheless, as there are fundamental limitations on learning a technical subject like machine learning in a book with just 300 (non-mathematical) pages, this book is bound to disappoint a few readers. The explanation of the hypothetical master algorithm is also limited since it relies almost entirely on Domingos’ decade-old research. In addition, some of the implications on society seem far fetched.
I was familiar with most of the material in this book beforehand, but as stated earlier, I still found Domingos’ organization of machine learning into five tribes to be immensely useful for understanding the field. His five tribes are: symbolists, connectionists, evolutionaries, Bayesians, and analogizers. I would never have thought about organizing machine learning this way, because I have limited experience with evolutionaries (i.e., people who use genetic programming) and because whenever I try to study logic and reasoning (i.e., what the symbolists do) I struggle to avoid falling asleep. I have been able to study logic, though, as shown in this long blog post.
I am most familiar with the other three areas Domingos lists. The connectionists are dominating machine learning nowadays because they use neural networks. (Ever heard of “deep learning,” anyone?) The Bayesians used to dominate; these are the people who use graphical models. The analogizers use the nearest neighbor and support vector machine algorithms. For all three of these tribes, I have covered corresponding topics in previous blog entries (e.g., see this post for a discussion on SVMs).
Given the constraints of 30 pages per tribe, Domingos explains each of them remarkably well. Having studied these in mathematical detail, I find it enjoyable to just relax, avoid the math, and understand the history: who invented which algorithm, what were competing trends, and similar stories. In addition to the five tribes, Domingos goes over other important concepts in machine learning and artificial intelligence, including Expectation-Maximization and reinforcement learning. Domingos then describes his thoughts on a universal algorithm, invoking his own research (Markov logic networks) in the progress. Finally, he discusses the implications of improved machine learning on our lives.
For the sake of a general audience, Domingos avoids mathematical details and relies on examples and analogies. This is fine, and both Domingos and I agree that it is possible to understand some machine learning without math. I probably do not agree with that statement as much as Domingos does, though; I really need to go over all the math details for me to understand an algorithm, and other readers with my mindset can get disappointed.
I also think his attempts at explaining the master algorithm at the end fall prey to excessive storytelling. After his long, adventure-related metaphor, I was (and still am) confused about how to effectively build upon his Markov logic network work to get a real master algorithm. Regarding the implications of the master algorithm on human life, that chapter felt more fantasty than reality to me, and I can’t see the kind of stuff Domingos says happening in the near future. For instance, Domingos says that since AI might take over human labor, we will need to use a “universal basic income,” but that would not necessarily be the best thing to do and I can’t see how this will be politically viable (it’s possible, though, but I’m probably thinking on a longer time horizon than Domingos).
I don’t mean for the last two paragraphs to sound overly critical. I think The Master Algorithm is interesting and I would recommend it to those who would like to learn more about computer science. I would not, however, say it is among my top-tier favorite books. It’s a decent, solid, but not once-in-a-generation, and I think that’s a fair characterization.