Part 31 (2/2)

60. 60. 5 i 10 5 i 1050 cps is equivalent to 5 i 10 cps is equivalent to 5 i 1021 (5 billion trillion) human civilizations (each requiring 10 (5 billion trillion) human civilizations (each requiring 1029 cps). cps).

61. 61. Ten billion (10 Ten billion (1010) humans at 1016 cps each is 10 cps each is 1026 cps for human civilization. So 5 i 10 cps for human civilization. So 5 i 1050 cps is equivalent to 5 i 10 cps is equivalent to 5 i 1024 (5 trillion trillion) human civilizations. (5 trillion trillion) human civilizations.

62. 62. This estimate makes the conservative a.s.sumption that we've had ten billion humans for the past ten thousand years, which is obviously not the case. The actual number of humans has been increasing gradually over the past to reach about 6.1 billion in 2000. There are 3 i 10 This estimate makes the conservative a.s.sumption that we've had ten billion humans for the past ten thousand years, which is obviously not the case. The actual number of humans has been increasing gradually over the past to reach about 6.1 billion in 2000. There are 3 i 107 seconds in a year, and 3 i 10 seconds in a year, and 3 i 1011 seconds in ten thousand years. So, using the estimate of 10 seconds in ten thousand years. So, using the estimate of 1026 cps for human civilization, human thought over ten thousand years is equivalent to certainly no more than 3 i 10 cps for human civilization, human thought over ten thousand years is equivalent to certainly no more than 3 i 1037 calculations. The ultimate laptop performs 5 i 10 calculations. The ultimate laptop performs 5 i 1050 calculations in one second. So simulating ten thousand years of ten billion humans' thoughts would take it about 10 calculations in one second. So simulating ten thousand years of ten billion humans' thoughts would take it about 1013 seconds, which is one ten-thousandth of a nanosecond. seconds, which is one ten-thousandth of a nanosecond.

63. 63. Anders Sandberg, ”The Physics of the Information Processing Superobjects: Daily Life Among the Jupiter Brains,” Anders Sandberg, ”The Physics of the Information Processing Superobjects: Daily Life Among the Jupiter Brains,” Journal of Evolution & Technology Journal of Evolution & Technology 5 (December 22, 1999), /volume5/Brains2.pdf. 5 (December 22, 1999), /volume5/Brains2.pdf.

64. 64. See note 62 above; 10 See note 62 above; 1042 cps is a factor of 10 cps is a factor of 108 less than 10 less than 1050 cps, so one ten-thousandth of a nanosecond becomes 10 microseconds. cps, so one ten-thousandth of a nanosecond becomes 10 microseconds.

65. 65. See e-drexler.com/p/04/04/0330drexPubs.html for a list of Drexler's publications and patents. See e-drexler.com/p/04/04/0330drexPubs.html for a list of Drexler's publications and patents.

66. 66. At the rate of $10 At the rate of $1012 and 10 and 1026 cps per thousand dollars ($10 cps per thousand dollars ($103), we get 1035 cps per year in the mid-2040s. The ratio of this to the 10 cps per year in the mid-2040s. The ratio of this to the 1026 cps for all of the biological thinking in human civilization is 109 (one billion). cps for all of the biological thinking in human civilization is 109 (one billion).

67. 67. In 1984 Robert A. Freitas proposed a logarithmic scale of ”sentience quotient” (SQ) based on the computational capacity of a system. In a scale that ranges from 70 to 50, human brains come out at 13. The Cray 1 supercomputer comes out at 9. Freitas's sentience quotient is based on the amount of computation per unit ma.s.s. A very fast computer with a simple algorithm would come out with a high SQ. The measure I describe for computation in this section builds on Freitas's SQ and attempts to take into consideration the usefulness of the computation. So if a simpler computation is equivalent to the one actually being run, then we base the computational efficiency on the equivalent (simpler) computation. Also in my measure, the computation needs to be ”useful.” Robert A. Freitas Jr., ”Xenopsychology,” a.n.a.log 104 (April 1984): 4153, fAstro/Xeno psychology.htm#SentienceQuotient. In 1984 Robert A. Freitas proposed a logarithmic scale of ”sentience quotient” (SQ) based on the computational capacity of a system. In a scale that ranges from 70 to 50, human brains come out at 13. The Cray 1 supercomputer comes out at 9. Freitas's sentience quotient is based on the amount of computation per unit ma.s.s. A very fast computer with a simple algorithm would come out with a high SQ. The measure I describe for computation in this section builds on Freitas's SQ and attempts to take into consideration the usefulness of the computation. So if a simpler computation is equivalent to the one actually being run, then we base the computational efficiency on the equivalent (simpler) computation. Also in my measure, the computation needs to be ”useful.” Robert A. Freitas Jr., ”Xenopsychology,” a.n.a.log 104 (April 1984): 4153, fAstro/Xeno psychology.htm#SentienceQuotient.

68. 68. As an interesting aside, engravings on the side of small rocks did in fact represent an early form of computer storage. One of the earliest forms of written language, cuneiform, which was developed in Mesopotamia circa 3000 B.C., used pictorial markings on stones to store information. Agricultural records were maintained as cuneiform markings on stones placed in trays, and organized in rows and columns. These marked stones were essentially the first spreadsheet. One such cuneiform stone record is a prized artifact in my collection of historical computers. As an interesting aside, engravings on the side of small rocks did in fact represent an early form of computer storage. One of the earliest forms of written language, cuneiform, which was developed in Mesopotamia circa 3000 B.C., used pictorial markings on stones to store information. Agricultural records were maintained as cuneiform markings on stones placed in trays, and organized in rows and columns. These marked stones were essentially the first spreadsheet. One such cuneiform stone record is a prized artifact in my collection of historical computers.

69. 69. One thousand (10 One thousand (103) bits is less than the theoretical capacity of the atoms in the stone to store information (estimated at 1027 bits) by a factor of 10 bits) by a factor of 1024.

70. 70. 1 cps (100 cps) is less than the theoretical computing capacity of the atoms in the stone (estimated at 10 1 cps (100 cps) is less than the theoretical computing capacity of the atoms in the stone (estimated at 1042 cps) by a factor of 10 cps) by a factor of 1042.

71. 71. Edgar Buckingham, ”Jet Propulsion for Airplanes,” NACA report no. 159, in Edgar Buckingham, ”Jet Propulsion for Airplanes,” NACA report no. 159, in Ninth Annual Report of NACA-1923 Ninth Annual Report of NACA-1923 (Was.h.i.+ngton, D.C.: NACA, 1924), pp. 7590. See naca.larc.nasa.gov/reports/1924/naca-report-159/. (Was.h.i.+ngton, D.C.: NACA, 1924), pp. 7590. See naca.larc.nasa.gov/reports/1924/naca-report-159/.

72. 72. Belle Dume, ”Microscopy Moves to the Picoscale,” Belle Dume, ”Microscopy Moves to the Picoscale,” PhysicsWeb PhysicsWeb, June 10, 2004, physicsweb.org/artide/news/8/6/6, referring to Stefan Hembacher, Franz J. Giessibl, and Iochen Mannhart, ”Force Microscopy with Light-Atom Probes,” Science Science 305.5682 (July 16, 2004): 38083. This new ”higher harmonic” force microscope, developed by University of Augsburg physicists, uses a single carbon atom as a probe and has a resolution that is at least three times better than that of traditional scanning tunneling microscopes. How it works: as the tungsten tip of the probe is made to oscillate at subnanometer amplitudes, the interaction between the tip atom and the carbon atom produces higher harmonic components in the underlying sinusoidal-wave pattern. The scientists measured these signals to obtain an ultrahigh-resolution image of the tip atom that showed features just 77 picometers (thousandths of a nanometer) across. 305.5682 (July 16, 2004): 38083. This new ”higher harmonic” force microscope, developed by University of Augsburg physicists, uses a single carbon atom as a probe and has a resolution that is at least three times better than that of traditional scanning tunneling microscopes. How it works: as the tungsten tip of the probe is made to oscillate at subnanometer amplitudes, the interaction between the tip atom and the carbon atom produces higher harmonic components in the underlying sinusoidal-wave pattern. The scientists measured these signals to obtain an ultrahigh-resolution image of the tip atom that showed features just 77 picometers (thousandths of a nanometer) across.

73. 73. Henry Fountain, ”New Detector May Test Heisenberg's Uncertainty Principle,” Henry Fountain, ”New Detector May Test Heisenberg's Uncertainty Principle,” New York Times New York Times, July 22, 2003.

74. 74. Mitch Jacoby, ”Electron Moves in Attoseconds,” Mitch Jacoby, ”Electron Moves in Attoseconds,” Chemical and Engineering News Chemical and Engineering News 82.25 (June 21, 2004): 5, referring to Peter Abbamonte et al., ”Imaging Density Disturbances in Water with a 41.3-Attosecond Time Resolution,” 82.25 (June 21, 2004): 5, referring to Peter Abbamonte et al., ”Imaging Density Disturbances in Water with a 41.3-Attosecond Time Resolution,” Physical Review Letters Physical Review Letters 92.23 (June 11,2004): 237401. 92.23 (June 11,2004): 237401.

75. 75. S. K. Lamoreaux and 1. R. Torgerson, ”Neutron Moderation in the Oklo Natural Reactor and the Time Variation of Alpha,” S. K. Lamoreaux and 1. R. Torgerson, ”Neutron Moderation in the Oklo Natural Reactor and the Time Variation of Alpha,” Physical Review Physical Review D 69 (2004): 1217016, scitation.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=PRVDAQ000069000012121701000001&idtype=cvips&gifs=yes; Eugenie S. Reich, ”Speed of Light May Have Changed Recently,” D 69 (2004): 1217016, scitation.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=PRVDAQ000069000012121701000001&idtype=cvips&gifs=yes; Eugenie S. Reich, ”Speed of Light May Have Changed Recently,” New Scientist New Scientist, June 30, 2004, !news/news.jsp?id=ns99996092.

76. 76. Charles Choi, ”Computer Program to Send Data Back in Time,” UPI, October 1, 2002, /view.efm?StoryID=20021001-125805-3380r; Todd Brun, ”Computers with Closed Timelike Curves Can Solve Hard Problems,” Charles Choi, ”Computer Program to Send Data Back in Time,” UPI, October 1, 2002, /view.efm?StoryID=20021001-125805-3380r; Todd Brun, ”Computers with Closed Timelike Curves Can Solve Hard Problems,” Foundation of Physics Letters Foundation of Physics Letters 16 (2003): 24553. Electronic edition, September 11,2002, arxiv.org/PS_cache/gr-qc/pdf/0209/0209061.pdf. 16 (2003): 24553. Electronic edition, September 11,2002, arxiv.org/PS_cache/gr-qc/pdf/0209/0209061.pdf.

Chapter Four: Achieving the Software of Human Intelligence:.

How to Reverse Engineer the Human Brain 1. 1. Lloyd Watts, ”Visualizing Complexity in the Brain,” in D. Fogel and C. Robinson, eds., Lloyd Watts, ”Visualizing Complexity in the Brain,” in D. Fogel and C. Robinson, eds., Computational Intelligence: The Experts Speak Computational Intelligence: The Experts Speak (Piscataway, N.J.: IEEE Press/Wiley, 2003), /wcci.pdf. (Piscataway, N.J.: IEEE Press/Wiley, 2003), /wcci.pdf.

2. 2. J. G. Taylor, B. Horwitz, and K. J. Friston, ”The Global Brain: Imaging and Modeling,” J. G. Taylor, B. Horwitz, and K. J. Friston, ”The Global Brain: Imaging and Modeling,” Neural Networks Neural Networks 13, special issue (2000): 827. 13, special issue (2000): 827.

3. 3. Neil A. Busis, ”Neurosciences on the Internet,” ; ”Neuroscientists Have Better Tools on the Brain,” Neil A. Busis, ”Neurosciences on the Internet,” ; ”Neuroscientists Have Better Tools on the Brain,” Bio IT Bulletin Bio IT Bulletin, /news/041503_report2345.html; ”Brain Projects to Reap Dividends for Neurotech Firms,” Neurotech Reports Neurotech Reports, /pages/brainprojects.html.

4. 4. Robert A. Freitas Jr., Robert A. Freitas Jr., Nanomedicine Nanomedicine, vol. 1, Basic Capabilities Basic Capabilities, section 4.8.6, ”Noninvasive Neuroelectric Monitoring” (Georgetown, Tex.: Landes Bioscience, 1999), pp. 11516, /NMI/4.8.6.htm.

5. 5. Chapter 3 a.n.a.lyzed this issue; see the section ”The Computational Capacity of the Human Brain.” Chapter 3 a.n.a.lyzed this issue; see the section ”The Computational Capacity of the Human Brain.”

6. 6. Speech-recognition research and development, Kurzweil Applied Intelligence, which I founded in 1982, now part of ScanSoft (formerly Kurzweil Computer Products). Speech-recognition research and development, Kurzweil Applied Intelligence, which I founded in 1982, now part of ScanSoft (formerly Kurzweil Computer Products).

7. 7. Lloyd Watts, U.S. Patent Application, U.S. Patent and Trademark Office, 20030095667, May 22, 2003, ”Computation of Multi-sensor Time Delays.” Abstract: ”Determining a time delay between a first signal received at a first sensor and a second signal received at a second sensor is described. The first signal is a.n.a.lyzed to derive a plurality of first signal channels at different frequencies and the second signal is a.n.a.lyzed to derive a plurality of second signal channels at different frequencies. A first feature is detected that occurs at a first time in one of the first signal channels. A second feature is detected that occurs at a second time in one of the second signal channels. The first feature is matched with the second feature and the first time is compared to the second time to determine the time delay.” See also Nabil H. Farhat, U.S. Patent Application 20040073415, U.S. Patent and Trademark Office, April 15, 2004, ”Dynamical Brain Model for Use in Data Processing Applications.” Lloyd Watts, U.S. Patent Application, U.S. Patent and Trademark Office, 20030095667, May 22, 2003, ”Computation of Multi-sensor Time Delays.” Abstract: ”Determining a time delay between a first signal received at a first sensor and a second signal received at a second sensor is described. The first signal is a.n.a.lyzed to derive a plurality of first signal channels at different frequencies and the second signal is a.n.a.lyzed to derive a plurality of second signal channels at different frequencies. A first feature is detected that occurs at a first time in one of the first signal channels. A second feature is detected that occurs at a second time in one of the second signal channels. The first feature is matched with the second feature and the first time is compared to the second time to determine the time delay.” See also Nabil H. Farhat, U.S. Patent Application 20040073415, U.S. Patent and Trademark Office, April 15, 2004, ”Dynamical Brain Model for Use in Data Processing Applications.”

8. 8. I estimate the compressed genome at about thirty to one hundred million bytes (see note 57 for chapter 2); this is smaller than the object code for Microsoft Word and much smaller than the source code. See Word 2003 system requirements, October 20, 2003, /office/word/prodinfo/sysreq.mspx. I estimate the compressed genome at about thirty to one hundred million bytes (see note 57 for chapter 2); this is smaller than the object code for Microsoft Word and much smaller than the source code. See Word 2003 system requirements, October 20, 2003, /office/word/prodinfo/sysreq.mspx.

9. 9. Wikipedia, en.wikipedia.org/wiki/Epigenetics. Wikipedia, en.wikipedia.org/wiki/Epigenetics.

10. 10. See note 57 in chapter 2 for an a.n.a.lysis of the information content in the genome, which I estimate to be 30 to 100 million bytes, therefore less than 10 See note 57 in chapter 2 for an a.n.a.lysis of the information content in the genome, which I estimate to be 30 to 100 million bytes, therefore less than 109 bits. See the section ”Human Memory Capacity” in chapter 3 (p. 126) for my a.n.a.lysis of the information in a human brain, estimated at 10 bits. See the section ”Human Memory Capacity” in chapter 3 (p. 126) for my a.n.a.lysis of the information in a human brain, estimated at 1018 bits. bits.

11. 11. Marie Gustafsson and Christian Balkenius, ”Using Semantic Web Techniques for Validation of Cognitive Models against Neuroscientific Data,” AILS04 Workshop, SAIS/SSLS Workshop (Swedish Artificial Intelligence Society; Swedish Society for Learning Systems), April 1516, 2004, Lund, Sweden, puter.org/comp/proceedings/icac/2004/2114/00121140312.pdf; ”About IBM Autonomic Computing,” /autonomic/about.shtml; and Ric Telford, ”The Autonomic Computing Architecture,” April 14, 2004, /html/section/aristotl_philosophy.asp.

20. 20. E. D. Adrian, E. D. Adrian, The Basis of Sensation: The Action of Sense Organs The Basis of Sensation: The Action of Sense Organs (London: Christophers, 1928). (London: Christophers, 1928).

21. 21. A. L. Hodgkin and A. F. Huxley, ”Action Potentials Recorded from Inside a Nerve Fibre,” A. L. Hodgkin and A. F. Huxley, ”Action Potentials Recorded from Inside a Nerve Fibre,” Nature Nature 144 (1939): 71012. 144 (1939): 71012.

22. 22. A. L. Hodgkin and A. F. Huxley, ”A Quant.i.tative Description of Membrane Current and Its Application to Conduction and Excitation in Nerve,” A. L. Hodgkin and A. F. Huxley, ”A Quant.i.tative Description of Membrane Current and Its Application to Conduction and Excitation in Nerve,” Journal of Physiology Journal of Physiology 117 (1952): 500544. 117 (1952): 500544.

23. 23. W. S. McCulloch and W. Pitts, ”A Logical Calculus of the Ideas Immanent in Nervous Activity,” W. S. McCulloch and W. Pitts, ”A Logical Calculus of the Ideas Immanent in Nervous Activity,” Bulletin of Mathematical Biophysics Bulletin of Mathematical Biophysics 5 (1943): 115-33. This seminal paper is a difficult one to understand. For a clear introduction and explanation, see ”A Computer Model of the Neuron,” the Mind Project, Illinois State University, s. See note 172 in chapter 5 for an algorithmic description of neural nets.

25. 25. E. Salinas and P. Thier, ”Gain Modulation: A Major Computational Principle of the Central Nervous System,” E. Salinas and P. Thier, ”Gain Modulation: A Major Computational Principle of the Central Nervous System,” Neuron 27 Neuron 27 (2000): 1521. (2000): 1521.

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