by Ray Kurzweil
2 See Raymond Kurzweil, The 10°/ Solution for a Healthy Life: How to Eliminate Virtually All Risk of Heart Disease and Cancer (New York: Crown Publishers, 1993).
CHAPTER 12: 2099
1 As discussed in chapter 6, “Building New Brains,” and chapter 10, “2019,” human capacity of an estimated 2 X 1016 (neural connection) calculations per second will be achieved in a $1,000 computing device by around the year 2020. Also as noted, the capacity of computing will double every twelve months, or ten times every decade, which is a factor of one thousand (210) every ten years. Thus by the year 2099, $1,000 of computing will be roughly equivalent to 1024 times the computing capacity of the human brain, or 1040 calculations per second. Estimating a trillion virtual persons (hundred times greater than the roughly 10 billion persons in the early twenty-first century), and an estimated $1 million of computing devoted to each person, we get an estimated 1055 calculations per second.
2 One thousand qu-bits would enable 21,000 (approximately 10300) calculations to be performed at the same time. If 1042 of the calculations each second are such quantum calculations, then that is equivalent to 1042 X 10300 = 10342 calculations per second. 1055 + 10342 still equals about 10342.
3 What happened to picoengineering, you’re wondering? Picoengineering refers to engineering at the scale of a picometer, which is one trillionth of a meter. Remember that the author has not spoken to Molly for seventy years. Nanotechnology (technology on the scale of a billionth of a meter) is becoming practical in the decade between 2019 and 2029. Note that in the twentieth century, the Law of Accelerating Returns as applied to computation has been achieved through engineering at ever smaller scales of physical size. Moore’s Law is a good example of this, in that the size of a transistor (in two dimensions) has been decreasing by 50 percent every two years. This means that transistors have been shrinking by a factor of 25 = 32 in ten years. Thus the feature size of a transistor in each dimension has been shrinking by a factor of the square root of 32 = 5.6 every ten years. We are shrinking, therefore, the feature size of components by a factor of about 5.6 in each dimension every decade.
If engineering at the nanometer scale (nanotechnology) is practical in the year 2032, then engineering at the picometer scale should be practical about forty years later (because 5.64 = approximately 1,000), or in the year 2072. Engineering at the femtometer (one thousandth of a trillionth of a meter, also referred to as a quadrillionth of a meter) scale should be feasible, therefore, by around the year 2112. Thus I am being a bit conservative to say that femtoengineering is controversial in 2099.
Nanoengineering involves manipulating individual atoms. Picoengineering will involve engineering at the level of subatomic particles (e.g., electrons). Femtoengineering will involve engineering inside a quark. This should not seem particularly startling, as contemporary theories already postulate intricate mechanisms within quarks.
EPILOGUE: THE REST OF THE UNIVERSE REVISITED
1 We could use the Busy Beaver Function (see note 16 on the Turing machine in chapter 4) as a quantitative measure of the software of intelligence.
TIME LINE
Sources for the timeline include Raymond Kurzweil, The Age of Intelligent Machines (Cambridge, MA: MIT Press, 1990).
Introduction to big bang theory at
Evolution and behavior at
“Chronology of Events in the History of Microcomputers” at < http://www3.islandnet.com/~kpolsson/comphist.htm>.
“The Computer Museum History Center” at
1 Picoengineering involves engineering at the level of subatomic particles (e.g., electrons). See note 3 on picoengineering and femtoengineering in chapter 12.
2 Femtoengineering will involve engineering using mechanisms within a quark. See note 3 on picoengineering and femtoengineering in chapter 12.
HOW TO BUILD AN INTELLIGENT MACHINE IN THREE EASY PARADIGMS
1 See “Information Processing in the Human Body,” by Vadim Gerasimov, at
2 Marvin Minsky and Seymour A. Papert, Perceptrons: An Introduction to Computational Geometry (Cambridge, MA: MIT Press, 1988).
3 The quoted text on the “two daughter sciences” is from Seymour Papert, “One AI or Many,” Daedalus, Winter 1988.
“Dr. Seymour Papert is a mathematician and one of the early pioneers of Artificial Intelligence. Additionally, he is internationally recognized as the seminal thinker about ways in which computers can change learning. Born and educated in South Africa where he participated actively in the anti-apartheid movement, Dr. Papert pursued mathematical research at Cambridge University from 1954 through 1958. He then worked with Jean Piaget at the University of Geneva from 1958 through 1963. It was this collaboration that led him to consider using mathematics in the service of understanding how children can learn and think. In the early 1960s, Papert came to MIT where, with Marvin Minsky, he founded the Artificial Intelligence Laboratory and coauthored their seminal work Perceptrons.” From the web page entitled “Seymour Papert” at
4 “[Marvin] Minsky was ... one of the pioneers of intelligence-based mechanical robotics and telepresence.... In 1951 he built the first randomly wired neural network learning machine (called SNARC, for Stochastic Neural-Analog Reinforcement Computer), based on the reinforcement of simulated synaptic transmission coefficients.... Since the early 1950s, Marvin Minsky has worked on using computational ideas to characterize human psychological processes, as well as working to endow machines with intelligence.” From the brief academic biography of Marvin Minsky at
5 Dr. Raj Reddy is dean of the School of Computer Science at Carnegie Mellon University and the Herbert A. Simon University Professor of Computer Science and Robotics. Dr. Reddy is a leading AI researcher whose research interests include the study of human-computer interaction and artificial intelligence.
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