Ray Kurzweil, the bold futurist and author of the New York Times best seller The Singularity Is Near, is arguably today’s most influential technological visionary. A pioneering inventor and theorist, he has explored for decades how artificial intelligence can enrich and expand human capabilities. Now, in his much-anticipated How to Create a Mind, he takes this exploration to the next step: reverse-engineering the brain to understand precisely how it works, then applying that knowledge to create vastly intelligent machines. Drawing on the most recent neuroscience research, his own research and inventions in artificial intelligence, and compelling thought experiments, he describes his new theory of how the neocortex (the thinking part of the brain) works: as a self-organizing hierarchical system of pattern recognizers. Kurzweil shows how these insights will enable us to greatly extend the powers of our own mind and provides a road map for the creation of super-intelligence - humankind’s most exciting next venture. We are now at the dawn of an era of radical possibilities in which merging with our technology will enable us to effectively address the world’s grand challenges. How to Create a Mind is certain to be one of the most widely discussed and debated science books in many years - a touchstone for any consideration of the path of human progress.
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Read the Wikipedia article, don't bother with the book
Kurzweil spends more time comparing himself to the great minds of humanity than he does actually discussing his theories. The content is laced mostly with anecdotal fluff, and quotes by other people to make his writing sound more impressive. I'm really happy Audible allows returns because this book is a waste of money. You'd save yourself a lot of time and frustration by just reading a synopsis of the book.
If you don’t know much about the current state of artificial intelligence, brain science, or the philosophy of consciousness, and don’t mind a little bit of technical discussion, Kurzweil does a fine job of articulating the current rapid converge between these areas of understanding. However, if you already do know the basics, this book probably isn’t going to do much to expand your own consciousness.
Speaking as a software engineer who has a fascination with AI, I largely agree with Kurzweil's glowing assessments about the future of machine intelligence, though I'd probably push his timeframe back a few decades and could do with a bit less of his self-promotion. Though there's a lot we still don't understand about how the human brain operates, neuroscience and computer science are starting to form the same fundamental insights about how intelligence "works", whether it's represented as neurons or a mathematical process. In a truly intelligent machine, data from the outside world is taken in by a large, hierarchical array of pattern-recognizers, which gradually rewire themselves to better anticipate the messy-but-hierarchical patterns of the real world (visual squiggles to letters, letters to words, words to syntax, syntax to meanings, meanings to relationships, relationships to concepts, concepts to insights -- and back down again). To some extent, the software world has already made useful progress in this direction.
However, most of the insights Kurzweil offers aren’t anything new. Indeed, most of what he says was explored in Jeff Hawkin’s 2004 book, On Intelligence, and in academia before that. Briefly stated, the hierarchical architecture of the human brain’s neocortex is the major engine of human intelligence, and it seems to start out mostly as a blank slate, a generalized learning machine that builds neural connections through experience, eventually forming a complex inductive model of reality, which constantly makes predictions about what comes next. Kurzweil shares some of his own successes solving certain kinds of problems decades ago, but the new ideas he advances seem somewhat vague and underdeveloped (maybe he’s saving the nuts and bolts for his new job at Google).
Still, there's plenty here for a general audience, when he gets away from the geekery. Kurzweil is passionate and pretty convincing about his belief that even limited gains in awareness of how the human brain works still provide AI researchers with some powerful springboards, and that, conversely, advances (or missteps) in AI teach us more about the brain. As he points out in discussing Watson, the IBM computer system that famously won on Jeopardy after acquiring most of its knowledge from scanning natural-language documents (the sampling of questions it got right is impressive), things have already come a long way. And there's no reason to believe that the rapid convergence won't continue, especially in the post-cloud computing world. After all, the specific, idiosyncratic way our monkey-rat-lizard brains were shaped to think as our ancestors crawled/darted/clambered around undoubtedly isn't the only way an evolutionary process can discover thought.
There’s also a succinct but informative history of the field of AI, with brief overviews of significant thinkers and developments. And Kurzweil wades a little bit into the philosophy of consciousness, exploring some its more paradoxical aspects in light of what science knows about the human brain. For example, it's been shown that the two cerebral hemispheres, in patients with a severed connection, operate almost as two separate brains. Yet, each one still seems to think it has a conscious link to the other. Maybe such individuals are more like two people in one body, but don't realize it? Eerie, huh? His other thought experiments are nothing new, but still fun. Everyone should know what the Chinese Room is.
Finally, there’s a section in which Kurzweil responds to critics, and calls out a few flagrant misunderstandings of his ideas. While it’s debatable how on-target his past predictions about technology have been, as far as I’m concerned, if he was even halfway right, then he’ll be fully right soon enough.
Overall, I think I would recommend this book most to AI neophytes who haven’t read anything by Kurzweil before. His enthusiasm for the topic can be quite inspiring. For other readers, especially those who have read On Intelligence, I don’t think you’re missing anything essential. I’d probably give this one 4 stars for the former audience, 2.5 for the latter, 3.5 overall.