The Singularity Is Near: When Humans Transcend Biology (101 page)

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

64
. See note 62 above; 10
⁴²
cps is a factor of 10
^–8
less than 10
⁵⁰
cps, so one ten-thousandth of a nanosecond becomes 10 microseconds.

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

66
. At the rate of $10
¹²
and 10
²⁶
cps per thousand dollars ($10
³
), we get 10
³⁵
cps per
year in the mid-2040s. The ratio of this to the 10
²⁶
cps for all of the biological thinking in human civilization is 10
⁹
(one billion).

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 mass. 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,”
Analog
104 (April 1984): 41–53,
http://www.rfreitas.com/Astro/Xenopsychology.htm#SentienceQuotient
.

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.

69
. One thousand (10
³
) bits is less than the theoretical capacity of the atoms in the stone to store information (estimated at 10
²⁷
bits) by a factor of 10 24.

70
. 1 cps (10° cps) is less than the theoretical computing capacity of the atoms in the stone (estimated at 10
⁴²
cps) by a factor of 10 42.

71
. Edgar Buckingham, “Jet Propulsion for Airplanes,” NACA report no. 159, in
Ninth Annual Report of NACA-1923
(Washington, D.C.: NACA, 1924), pp. 75–90. See
http://naca.larc.nasa.gov/reports/1924/naca-report-159/
.

72
. Belle Dumé, “Microscopy Moves to the Picoscale,”
PhysicsWeb
, June 10, 2004,
http://physicsweb.org/article/news/8/6/6
, referring to Stefan Hembacher, Franz J. Giessibl, and Jochen Mannhart, “Force Microscopy with Light-Atom Probes,”
Science
305.5682 (July 16, 2004): 380–83. 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
. Henry Fountain, “New Detector May Test Heisenberg’s Uncertainty Principle,”
New York Times
, July 22, 2003.

74
. Mitch Jacoby, “Electron Moves in Attoseconds,”
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,”
Physical Review Letters
92.23 (June 11, 2004): 237–401.

75
. S. K. Lamoreaux and J. R. Torgerson, “Neutron Moderation in the Oklo Natural Reactor and the Time Variation of Alpha,”
Physical Review
D 69 (2004): 121701–6,
http://scitation.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=PRVDAQ
000069000012121701000001&idtype=cvips&gifs=yes; Eugenie S. Reich, “Speed of Light May Have Changed Recently,”
New Scientist
, June 30, 2004,
http://www.newscientist.com/news/news.jsp?id=ns99996092
.

76
. Charles Choi, “Computer Program to Send Data Back in Time,” UPI, October 1, 2002,
http://www.upi.com/view.cfm?StoryID=20021001-125805-3380r
; Todd Brun, “Computers with Closed Timelike Curves Can Solve Hard Problems,”
Foundation of Physics Letters
16 (2003): 245–53. Electronic edition, September 11, 2002,
http://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
. Lloyd Watts, “Visualizing Complexity in the Brain,” in D. Fogel and C. Robinson, eds.,
Computational Intelligence: The Experts Speak
(Piscataway, N.J.: IEEE Press/Wiley, 2003),
http://www.lloydwatts.com/wcci.pdf
.

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

3
. Neil A. Busis,“Neurosciences on the Internet,”
http://www.neuroguide.com
; “Neuroscientists Have Better Tools on the Brain,”
Bio IT Bulletin
,
http://www.bio-itworld.com/news/041503_report2345.html
; “Brain Projects to Reap Dividends for Neurotech Firms,”
Neurotech Reports
,
http://www.neurotechreports.com/pages/brainprojects.html
.

4
. Robert A. Freitas Jr.,
Nanomedicine
, vol. 1,
Basic Capabilities
, section 4.8.6, “Noninvasive Neuroelectric Monitoring” (Georgetown, Tex.: Landes Bioscience, 1999), pp. 115–16,
http://www.nanomedicine.com/NMI/4.8.6.htm
.

5
.
chapter 3
analyzed this issue; see the section “The Computational Capacity of the Human Brain.”

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

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 analyzed to derive a plurality of first signal channels at different frequencies and the second signal is analyzed 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
. 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,
http://www.microsoft.com/office/word/prodinfo/sysreq.mspx
.

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

10
. See note 57 in
chapter 2
for an analysis of the information content in the genome, which I estimate to be 30 to 100 million bytes, therefore less than 10
⁹
bits. See the section “Human Memory Capacity” in
chapter 3
(p. 126) for my analysis of the information in a human brain, estimated at 10
¹⁸
bits.

11
. Marie Gustafsson and Christian Balkenius, “Using Semantic Web Techniques for Validation of Cognitive Models against Neuroscientific Data,” AILS 04 Workshop, SAIS/SSLS Workshop (Swedish Artificial Intelligence Society; Swedish Society for Learning Systems), April 15–16, 2004, Lund, Sweden, www.lucs.lu.se/People/Christian.Balkenius/PDF/Gustafsson.Balkenius.2004.pdf.

12
. See discussion in
chapter 3
. In one useful reference, when modeling neuron by neuron, Tomaso Poggio and Christof Koch describe the neuron as similar to a chip with thousands of logical gates. See T. Poggio and C. Koch, “Synapses That Compute Motion,”
Scientific American
256 (1987): 46–52. Also C. Koch and T. Poggio, “Biophysics of Computational Systems: Neurons, Synapses, and Membranes,” in
Synaptic Function
, G. M. Edelman, W. E. Gall, and W. M. Cowan, eds. (New York: John Wiley and Sons, 1987), pp. 637–97.

13
. On Mead, see
http://www.technology.gov/Medal/2002/bios/Carver_A._Mead.pdf
. Carver Mead,
Analog VLSI and Neural Systems
(Reading, Mass.: Addison-Wesley, 1986).

14
. See note 172 in
chapter 5
for an algorithmic description of a self-organizing neural net and note 175 in
chapter 5
for a description of a self-organizing genetic algorithm.

15
. See Gary Dudley et al., “Autonomic Self-Healing Systems in a Cross-Product IT Environment,” proceedings of the IEEE International Conference on Autonomic Computing, New York City, May 17–19, 2004,
http://csdl.computer.org/comp/proceedings/icac/2004/2114/
00/21140312.pdf
; “About IBM Autonomic Computing,”
http://www-3.ibm.com/autonomic/about.shtml
; and Ric Telford, “The Autonomic Computing Architecture,” April 14, 2004,
http://www.dcs.st-andrews.ac.uk/undergrad/current/dates/disclec/2003–2/RicTelfordDistinguished2.pdf
.

16
. Christine A. Skarda and Walter J. Freeman, “Chaos and the New Science of the Brain,”
Concepts in Neuroscience
1.2 (1990): 275–85.

17
. C. Geoffrey Woods,“Crossing the Midline,”
Science
304.5676 (June 4, 2004): 1455–56; Stephen Matthews, “Early Programming of the Hypothalamo-Pituitary-Adrenal Axis,”
Trends in Endocrinology and Metabolism
13.9 (November 1, 2002): 373–80; Justin Crowley and Lawrence Katz, “Early Development of Ocular Dominance Columns,”
Science
290.5495 (November 17, 2000): 1321–24; Anna Penn et al., “Competition in the Retinogeniculate Patterning Driven by Spontaneous Activity,”
Science
279.5359 (March 27, 1998): 2108–12; M. V. Johnston et al., “Sculpting the Developing Brain,”
Advances in Pediatrics
48 (2001): 1–38; P. La Cerra and R. Bingham, “The Adaptive Nature of the Human Neurocognitive Architecture: An Alternative Model,”
Proceedings of the National Academy of Sciences
95 (September 15, 1998): 11290–94.

18
. Neural nets are simplified models of neurons that can self-organize and solve problems. See note 172 in
chapter 5
for an algorithmic description of neural nets. Genetic algorithms are models of evolution using sexual reproduction with controlled mutation rates. See note 175 in
chapter 5
for a detailed description of genetic algorithms. Markov models are products of a mathematical technique that are similar in some respects to neural nets.

19
. Aristotle,
The Works of Aristotle
, trans. W. D. Ross (Oxford: Clarendon Press, 1908–1952 (see, in particular,
Physics
); see also
http://www.encyclopedia.com/html/section/aristotl_philosophy.asp
.

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

21
. A. L. Hodgkin and A. F. Huxley, “Action Potentials Recorded from Inside a Nerve Fibre,”
Nature
144 (1939): 710–12.

22
. A. L. Hodgkin and A. F. Huxley, “A Quantitative Description of Membrane Current and Its Application to Conduction and Excitation in Nerve,”
Journal of Physiology
117 (1952): 500–544.

23
. W. S. McCulloch and W. Pitts, “A Logical Calculus of the Ideas Immanent in Nervous Activity,”
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,
http://www.mind.ilstu.edu/curriculum/perception/mpneuron1.html
.