Although Bayle and I are always surprised when we see how many people are actually reading Neurodudes every day (“you really like us! you really do!”), I think we realized we had hit a new milestone when Ray Kurzweil’s book agent called to give us an advance copy of his new book. Let me be clear here: We will gladly review any AI-/neuro-related books you send us. Free books are great! (Heck, we’ll even do an occasional historical biography, if you send us one.)
There’s a lot to say about Kurzweil”s new book, The Singularity is Near (book website; book on Amazon). This book is similar to his previous books (Age of Intelligent Machines, Age of Spiritual Machines) in style and research but the thesis here is that we are on the precipice of a major change in human civilization: We are soon going to create entities of superior intelligence in all aspects to our own selves. This is the Singularity.
Full book review after the jump
TSIN is based on the same fundamental idea that his previous books emphasize: The acceleration of the rate of innovation. Ray contends that the time between major milestones, specifically with respect to human-created computational ability, is getting faster and that most people don’t realize that this means the “future” will be here faster than one predict.
Unlike the previous books, Ray talks a lot more about biology in this work and, in particular, neuroscience. He sprinkles the book with quotes from well-known researchers talking about, well, stuff they normally don’t talk about like predictions about the future of neuroscience. (Early on, Kurzweil makes a smart remark about how scientists are trained to be very skeptical and pride themselves on underestimation of the impact of new technologies.)
Most of the neuroscience research is not terribly novel for those who regularly read journals or attend conferences, but that is not what you should be reading this book for. Ray, in the best traditions of the multidisciplinary “renaissance scientist” (perhaps an almost extinct species in these times of ultra-over-specialization), excels at assembling many disparate ideas from different disciplines together. That alone can be a recipe for disaster, but Ray does a nice job of combining ideas and technologies with his constant back of the envelope calculations to show the multiplicity of routes to his central thesis.
There are a few chapters specifically on neuroscience and there are some very nice insights in these chapters. A commendable discussion of levels of analysis in neural systems is presented and elaborates on the difficulties of doing simple estimates, based on number of neurons or synapses, of the computational power of the brain. Sure, the connections within a cortical region might be well understood but what about the local connections within a few hundred microns? Similarly, we might model a set of neurons and their connections but how about the extracellular diffusion of neuromodulators near that synapse or local electric fields or countless other influences? It was a nice surprise to see a relatively accessible book bringing up these issues, even if only briefly.
Ray also tackles the important divide of analytic versus neuromorphic methods in computational neuroscience, a question that I doubt many computational neuroscientists have given careful thought to. He sides with the neuromorphic approach and seems to suggest that studying the genetic basis of the brain might be more beneficial than studying the brain itself since the design of the brain and many essential features are captured by this compact representation.
This is not to say that the book is without flaws. There are many contentious ideas that ever-optimistic Ray (I think that’s a good thing, by the way) presents as fact: Reversible computing leads him to believe that eventually all computation will require no energy. Memory might be more than connection patterns and neurotransmitter concentrations, and I mean a lot more. And, as we’ve discussed here before, we are far away from any kind of neuromorphic hippocampus, despite what some may claim. Also, it’s hard to judge how seriously we’re supposed to take some of the time estimates, especially when there’s little justification for the particular date — sending nanobots through the bloodstream to monitor every neuron’s activity noninvasively by 2020? Maybe. (As Ray points out, “there are more than 50,000 neuroscientists in the world, writing articles for more than 300 journals.” Who knows…) Of course, the biggest one is the Singularity itself, which he pins at 2045 based on extrapolating computation per dollar trends. Maybe.
The book also includes several sections on computation and related application-oriented fields (nanotech, robots) that I’ll skip over but the best part of the book might be Ray’s answers to his critics. From the wacky (Penrose’s quantum mechanics in the neural cytoskeleton) to the deeply philosophical (Searle’s Chinese Room argument against strong AI), it is clear that he has thought about the viability of his ideas and is prepared to take on the obvious criticisms that others might lob at him.
I don’t think I would have gone to graduate school in neuroscience if I didn’t believe, like Ray, that the Singularity is near. Just how near, I’m, unfortunately, not sure.