The Age of Spiritual Machines: When Computers Exceed Human Intelligence (60 page)
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Authors: Ray Kurzweil
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2
One thousand qu-bits would enable 2
1,000
(approximately 10
300
) calculations to be performed at the same time. If 10
42
of the calculations each second are such quantum calculations, then that is equivalent to 10
42
X 10
300
= 10
342
calculations per second. 10
55
+ 10
342
still equals about 10
342
.
One thousand qu-bits would enable 2
1,000
(approximately 10
300
) calculations to be performed at the same time. If 10
42
of the calculations each second are such quantum calculations, then that is equivalent to 10
42
X 10
300
= 10
342
calculations per second. 10
55
+ 10
342
still equals about 10
342
.
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 2
5
= 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.
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 2
5
= 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 5.6
4
= 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.
4
= 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.
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.
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.
Sources for the timeline include Raymond Kurzweil, The Age of Intelligent Machines (Cambridge, MA: MIT Press, 1990).
Introduction to big bang theory at <
http://www.bowdoin.edu/dept/physics/astro.1997/astro4/bigbang.html
>; Joseph Silk, A Short History
of
the Universe (New York: Scientific American Library, 1994); Joseph Silk, The Big Bang (San Francisco: W H. Freeman and Company, 1980); Robert M. Wald, Space, Time and Gravity (Chicago: The University of Chicago Press, 1977); Stephen W Hawking, A Brief History of Time (New York: Bantam Books, 1988).
http://www.bowdoin.edu/dept/physics/astro.1997/astro4/bigbang.html
>; Joseph Silk, A Short History
of
the Universe (New York: Scientific American Library, 1994); Joseph Silk, The Big Bang (San Francisco: W H. Freeman and Company, 1980); Robert M. Wald, Space, Time and Gravity (Chicago: The University of Chicago Press, 1977); Stephen W Hawking, A Brief History of Time (New York: Bantam Books, 1988).
Evolution and behavior at <
http://ccp.uchicago.edu/~jyin/evolution.html
>; Edward O. Wilson, The Diversity of Life (New York: W W Norton and Company, 1993); Stephen Jay Gould, The Book of Life (New York: W W Norton and Company, 1993); Alexander Hellemans and Bryan Bunch, The Timetable of Science (Simon and Schuster, 1988). “CBN History: Radio/Broadcasting Timeline” at <
http://www.wcbn.org/history/wcbntime.html
>.
http://ccp.uchicago.edu/~jyin/evolution.html
>; Edward O. Wilson, The Diversity of Life (New York: W W Norton and Company, 1993); Stephen Jay Gould, The Book of Life (New York: W W Norton and Company, 1993); Alexander Hellemans and Bryan Bunch, The Timetable of Science (Simon and Schuster, 1988). “CBN History: Radio/Broadcasting Timeline” at <
http://www.wcbn.org/history/wcbntime.html
>.
“Chronology of Events in the History of Microcomputers” at <
http://www3.islandnet.com/~kpolsson/comphist.htm
>.
http://www3.islandnet.com/~kpolsson/comphist.htm
>.
“The Computer Museum History Center” at <
http://www.tcm.org/history/index.html
>.
http://www.tcm.org/history/index.html
>.
1
Picoengineering involves engineering at the level of subatomic particles (e.g., electrons). See note 3 on picoengineering and femtoengineering in chapter 12.
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.
Femtoengineering will involve engineering using mechanisms within a quark. See note 3 on picoengineering and femtoengineering in chapter 12.
1
See “Information Processing in the Human Body,” by Vadim Gerasimov, at <
http://vadim.www.media.mit.edu/MAS862/Project.html
>.
See “Information Processing in the Human Body,” by Vadim Gerasimov, at <
http://vadim.www.media.mit.edu/MAS862/Project.html
>.
2
Marvin Minsky and Seymour A. Papert, Perceptrons: An Introduction to Computational Geometry (Cambridge, MA: MIT Press, 1988).
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.
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 <
http://papert.www.media.mit.edu/people/papert/
>.
http://papert.www.media.mit.edu/people/papert/
>.
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 <
http://minsky.www.media.mit.edu/people/minsky/minskybiog.html
>.
“[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 <
http://minsky.www.media.mit.edu/people/minsky/minskybiog.html
>.
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.
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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