How to Create a Mind: The Secret of Human Thought Revealed 7/28/13 Edition
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—The New York Times
“Kurzweil writes boldly and with a showman’s flair, expertly guiding the lay reader into deep thickets of neuroscience.”
—Kate Tuttle, Boston Globe
“This book is a breath of fresh air . . . . Kurzweil makes an argument for optimism.”
—Laura Spinney, New Scientist
“A fascinating exercise in futurology.”
“It is rare to find a book that offers unique and inspiring content on every page. How to Create a Mind achieves that and more. Ray has a way of tackling seemingly overwhelming challenges with an army of reason, in the end convincing the reader that it is within our reach to create nonbiological intelligence that will soar past our own. This is a visionary work that is also accessible and entertaining.”
—Rafael Reif, president, MIT
“Kurzweil’s new book on the mind is magnificent, timely, and solidly argued! His best so far!”
—Marvin Minsky, MIT Toshiba Professor of Media Arts and Sciences; cofounder of the MIT Artificial Intelligence Lab; widely regarded as “the father of artificial intelligence”
“If you ever wondered about how your mind works, read this book. Kurzweil’s insights reveal key secrets underlying human thought and our ability to recreate it. This is an eloquent and thought-provoking work.”
—Dean Kamen, physicist; inventor of the first wearable insulin pump, the HomeChoice dialysis machine, and the IBOT mobility system; founder of FIRST; recipient of the National Medal of Technology
“One of the eminent AI pioneers, Ray Kurzweil, has created a new book to explain the true nature of intelligence, both biological and nonbiological. The book describes the human brain as a machine that can understand hierarchical concepts ranging from the form of a chair to the nature of humor. His important insights emphasize the key role of learning both in the brain and in AI. He provides a credible road map for achieving the goal of super-human intelligence, which will be necessary to solve the grand challenges of humanity.”
—Raj Reddy, founding director, Robotics Institute, Carnegie Mellon University; recipient of the Turing Award from the Association for Computing Machinery
“Ray Kurzweil pioneered artificial intelligence systems that could read print in any type style, synthesize speech and music, and understand speech. These were the forerunners of the present revolution in machine learning that is creating intelligent computers that can beat humans in chess, win on Jeopardy!, and drive cars. His new book is a clear and compelling overview of the progress, especially in learning, that is enabling this revolution in the technologies of intelligence. It also offers important insights into a future in which we will begin solving what I believe is the greatest problem in science and technology today: the problem of how the brain works and of how it generates intelligence.”
—Tomaso Poggio, Eugene McDermott Professor, MIT Department of Brain and Cognitive Sciences; director, MIT Center for Biological and Computational Learning; former chair, MIT McGovern Institute for Brain Research; one of the most cited neuroscientists in the world
“This book is a Rosetta stone for the mystery of human thought. Even more remarkably, it is a blueprint for creating artificial consciousness that is as persuasive and emotional as our own. Kurzweil deals with the subject of consciousness better than anyone from Blackmore to Dennett. His persuasive thought experiment is of Einstein quality: It forces recognition of the truth.”
—Martine Rothblatt, chairman and CEO, United Therapeutics; creator of Sirius XM Satellite Radio
“Kurzweil’s book is a shining example of his prodigious ability to synthesize ideas from disparate domains and explain them to readers in simple, elegant language. Just as Chanute’s Progress in Flying Machines ushered in the era of aviation over a century ago, this book is the harbinger of the coming revolution in artificial intelligence that will fulfill Kurzweil's own prophecies about it.”
—Dileep George, AI scientist; pioneer of hierarchical models of the neocortex; cofounder of Numenta and Vicarious Systems
“Ray Kurzweil’s understanding of the brain and artificial intelligence will dramatically impact every aspect of our lives, every industry on Earth, and how we think about our future. If you care about any of these, read this book!”
—Peter H. Diamandis, chairman and CEO, X PRIZE; executive chairman, Singularity University; author of the New York Times bestseller Abundance: The Future Is Better Than You Think
About the Author
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Kurzweil's successful experience with natural language processing software gives him considerable credibility and authority in this attempt to predict computer breakthroughs during the coming decades. Ray Kurzweil is one of the foremost AI geniuses of our times and you probably should read one of his books.
Among these seemingly inexorably rising curves have been the spatial and temporal resolution of the tools we use to image and understand the structure of the brain. So rapid has been the progress that most of the detailed understanding of the brain dates from the last decade, and new discoveries are arriving at such a rate that the author had to make substantial revisions to the manuscript of this book upon several occasions after it was already submitted for publication.
The focus here is primarily upon the neocortex, a part of the brain which exists only in mammals and is identified with “higher level thinking”: learning from experience, logic, planning, and, in humans, language and abstract reasoning. The older brain, which mammals share with other species, is discussed in chapter 5, but in mammals it is difficult to separate entirely from the neocortex, because the latter has “infiltrated” the old brain, wiring itself into its sensory and action components, allowing the neocortex to process information and override responses which are automatic in creatures such as reptiles.
Not long ago, it was thought that the brain was a soup of neurons connected in an intricately tangled manner, whose function could not be understood without comprehending the quadrillion connections in the neocortex alone, each with its own weight to promote or inhibit the firing of a neuron. Now, however, it appears, based upon improved technology for observing the structure and operation of the brain, that the fundamental unit in the brain is not the neuron, but a module of around 100 neurons which acts as a pattern recogniser. The internal structure of these modules seems to be wired up from directions from the genome, but the weights of the interconnections within the module are adjusted as the module is trained based upon the inputs presented to it. The individual pattern recognition modules are wired both to pass information on matches to higher level modules, and predictions back down to lower level recognisers. For example, if you've seen the letters “appl” and the next and final letter of the word is a smudge, you'll have no trouble figuring out what the word is. (I'm not suggesting the brain works literally like this, just using this as an example to illustrate hierarchical pattern recognition.)
Another important discovery is that the architecture of these pattern recogniser modules is pretty much the same regardless of where they appear in the neocortex, or what function they perform. In a normal brain, there are distinct portions of the neocortex associated with functions such as speech, vision, complex motion sequencing, etc., and yet the physical structure of these regions is nearly identical: only the weights of the connections within the modules and the dyamically-adapted wiring among them differs. This explains how patients recovering from brain damage can re-purpose one part of the neocortex to take over (within limits) for the portion lost.
Further, the neocortex is not the rat's nest of random connections we recently thought it to be, but is instead hierarchically structured with a topologically three dimensional “bus” of pre-wired interconnections which can be used to make long-distance links between regions.
Now, where this begins to get very interesting is when we contemplate building machines with the capabilities of the human brain. While emulating something at the level of neurons might seem impossibly daunting, if you instead assume the building block of the neocortex is on the order of 300 million more or less identical pattern recognisers wired together at a high level in a regular hierarchical manner, this is something we might be able to think about doing, especially since the brain works almost entirely in parallel, and one thing we've gotten really good at in the last half century is making lots and lots of tiny identical things. The implication of this is that as we continue to delve deeper into the structure of the brain and computing power continues to grow exponentially, there will come a point in the foreseeable future where emulating an entire human neocortex becomes feasible. This will permit building a machine with human-level intelligence without translating the mechanisms of the brain into those comparable to conventional computer programming. The author predicts “this will first take place in 2029 and become routine in the 2030s.”
Assuming the present exponential growth curves continue (and I see no technological reason to believe they will not), the 2020s are going to be a very interesting decade. Just as few people imagined five years ago that self-driving cars were possible, while today most major auto manufacturers have projects underway to bring them to market in the near future, in the 2020s we will see the emergence of computational power which is sufficient to “brute force” many problems which were previously considered intractable. Just as search engines and free encyclopedias have augmented our biological minds, allowing us to answer questions which, a decade ago, would have taken days in the library if we even bothered at all, the 300 million pattern recognisers in our biological brains are on the threshold of having access to billions more in the cloud, trained by interactions with billions of humans and, perhaps eventually, many more artificial intelligences. I am not talking here about implanting direct data links into the brain or uploading human brains to other computational substrates although both of these may happen in time. Instead, imagine just being able to ask a question in natural language and get an answer to it based upon a deep understanding of all of human knowledge. If you think this is crazy, reflect upon how exponential growth works or imagine travelling back in time and giving a demo of Google or Wolfram Alpha to yourself in 1990.
Ray Kurzweil, after pioneering inventions in music synthesis, optical character recognition, text to speech conversion, and speech recognition, is now a director of engineering at Google.
"How to Create a Mind" is a very interesting book that presents the pattern recognition theory of mind (PRTM), which describes the basic algorithm of the neocortex (the region of the brain responsible for perception, memory, and critical thinking). It is the author's contention that the brain can be reverse engineered due to the power of its simplicity and such knowledge would allow us to create true artificial intelligence. The one and only, futurist, prize-winning scientist and author Ray Kurzweil takes the reader on a journey of the brain and the future of artificial intelligence. This enlightening 352-page book is composed of the following eleven chapters: 1. Thought Experiments on the World, 2. Thought Experiments on Thinking, 3. A Model of the Neocortex: The Pattern Recognition Theory of Mind, 4. The Biological Neocortex, 5. The Old Brain, 6. Transcendent Abilities, 7. The Biologically Inspired Digital Neocortex, 8. The Mind as Computer, 9. Thought Experiments on the Mind, 10. The Law of Accelerating Returns Applied to the Brain, and 11. Objections.
1. Well researched and well-written book. The author's uncanny ability to make very difficult subjects accessible to the masses.
2. A great topic in the "mind" of a great thinker.
3. Great use of charts and diagrams.
4. A wonderful job of describing how thinking works.
5. Thought-provoking questions and answers based on a combination of sound science and educated speculation.
6. The art of recreating brain processes in machines. "There is more parallel between brains and computers than may be apparent." Great stuff!
7. Great information on how memories truly work.
8. Hierarchies of units of functionality in natural systems.
9. How the neocortex must work. The Pattern Recognition Theory of Mind (PRTM). The main thesis of this book. The importance of redundancy. Plenty of details.
10. Evolution...it does a brain good. Legos will never be the same for me again.
11. The neocortex as a great metaphor machine. Projects underway to simulate the human brain such as Markram's Blue Brain Project.
12. Speech recognition and Markov models. Author provides a lot of excellent examples.
13. The four key concepts of the universality and feasibility of computation and its applicability to our thinking.
14. A fascinating look at split-brain patients. The "society of mind." The concept of free will, "We are apparently very eager to explain and rationalize our actions, even when we didn't actually make the decisions that led to them." Profound with many implications indeed.
15. The issue of identity.
16. The brain's ability to predict the future. The author's own predictive track record referenced.
17. The laws of accelerating returns (LOAR), where it applies and why we should train ourselves to think exponentially.
18. The author provides and analyzes objections to his thesis. In defense of his ideas. Going after Allen's "scientist's pessimism."
19. The evolution of our knowledge.
20. Great notes and links beautifully.
1. The book is uneven. That is, some chapters cover certain topics with depth while others suffer from lack of depth. Some of it is understandable as it relates to the limitations of what we currently know but I feel that the book could have been reformatted into smaller chapters or subchapters. The book bogs down a little in the middle sections of the book.
2. Technically I disagree with the notion that evolution always leads to more complexity. Yes on survival but not necessarily on complexity.
3. The author has a tendency to cross-market his products a tad much. It may come across as look at me...
4. A bit repetitive.
5. Sometimes leaves you with more questions than answers but that may not be a bad thing...
6. No formal separate bibliography.
In summary, overall I enjoyed this book. Regardless of your overall stance on the feasibility of artificial intelligence no one brings it like Ray Kurzweil. His enthusiasm and dedication is admirable. The author provides his basic thesis of how the brain works and a path to achieve true artificial intelligence and all that it implies. Fascinating in parts, bogs down in other sections but ultimately satisfying. I highly recommend it!
Further suggestions: "Subliminal: How Your Unconscious Mind Rules Your Behavior" by Leonard Mlodinow, "The Believing Brain: From Ghosts and Gods to Politics and Conspiracies---How We Construct Beliefs and Reinforce Them as Truths" by Michael Shermer, "The Scientific American Brave New Brain: How Neuroscience, Brain-Machine Interfaces, Neuroimaging, Psychopharmacology, Epigenetics, the Internet, and ... and Enhancing the Future of Mental Power" by Judith Horstman, "The Blank Slate: The Modern Denial of Human Nature" by Steven Pinker, "Who's in Charge?: Free Will and the Science of the Brain" and "Human: The Science Behind What Makes Us Unique", by Michael S. Gazzaniga, "Hardwired Behavior: What Neuroscience Reveals about Morality 1st Edition by Tancredi, Laurence published by Cambridge University Press Paperback" by Laurence Tancredi, "Braintrust: What Neuroscience Tells Us about Morality" by Patricia S. Churchland, "The Myth of Free Will" by Cris Evatt, "SuperSense" by Bruce M. Hood and "The Brain and the Meaning of Life" by Paul Thagard.
Top international reviews
A very disappointing read, please don't waste your time with it.
Nevertheless, I did manage to gain some – though very shallow – understanding of what those Silicon Valley dreamers are up to. Ray Kurzweil certainly is a dreamer. He ponders the questions of consciousness, free will, and identity, in order to convince us that, whatever shape or form AI takes when it arrives, it is not going to be fundamentally different from what we are now – just smarter, more efficient, and more durable. He seems to be fully convinced that it will be in a kind of symbiosis with us, or an extension of us, and that it is our destiny to infuse the whole universe with our post-biological human intelligence. I was very glad to see those dreams written down by someone else, someone who actually can and does pursue them actively. I share that dream, but I don’t have as much confidence in it. Probably that’s why Kurzweil devoted his life to it, and I’m just a sceptical observer!
As a software engineer working on pattern recognition systems I bought this book as soon as it was available, the book gave me a lot of ideas and I'm very happy I bought it. The central thesis seems to be the same as Jeff Hawkins' On Intelligence - obviously a big influence for Kurzweil - but with a focus on developments since On Intelligence was published.
Kurzweil got employed at Google very shortly after publishing this book so he could lead a team to create the mind that he's described. He's said in interviews that he left some details out of the book because he didn't want to give too much away.
Overall a good read that will provoke a lot of constructive thought, but don't expect for anyone to actually build a mind based on just this book.
I dream of being uploaded and living forever in a non-biological substrate.
The book starts on a very interesting description of how human brain has evolved by learning to challenge the obstacles put forward, whether for survival or in the form of age old traditions. It describes the mindset of Einstein as to what lead to the discovery of special theory of relativity and other such inventions by other scholars. It also describes the ideology of Alan Turing and his work to build the first computing machine along with the fundamentals it laid down for today's science of AI and computers as a whole. The writer's vast knowledge about computing, its history, understanding and hold on the subject is highly appreciable and a class apart.
It moves on to describe at length the structure of human brain, the pitched theory of its working in the form of algorithms such as PRTM (pattern recognition theory of mind) and LOAR (Law of Accelerated Return).
Further on, it also projects why unsupervised learning aspects of clustering and reinforced learning in the form of HHMM (hierarchical hidden Markov model) as the central algorithm of brain functioning. Uptill this point the book is gripping, and the reader is submissive of the writer description and knowledge.
However, this is the point when you reach the 180 degree of the sine wave. The writer goes ahead in the direction of answering more complex questions like consciousness of brain and links it to vastly different outlook. This is when the book dips to the lows due to loss of flow and repeated axioms (not proofs but only quoted opinions from other different writers, linguistics and philosophers)
Nonetheless, the articulation on faith and the divide between Western World and Eastern world on the subject of faith is an interesting read, and here is where the sine wave tries to come up again. As expected it dies out towards the end, leaving the reader with quite many unanswered questions and thoughts, which may be a reflection of writer's mind also.
If you are looking for a head start on the subject of brain mapping, it's structure, working and algorithmic functioning, then definitely it is worth the read.
But as the writer himself conveys at different places, the human mind is much more than simple algorithms. Machines of the future have to challenge to surpass for them to be bigger than the greatest evolution of nature.
An important point to be noted is that the writer has taken approximations and statistical hypothesis about the neocortex and its constituent neurons to an all together different level. So much of approximation and hypothetical analysis does make the reader skeptical about the truth value of such axioms.