Part 30 (1/2)

29. 29. Data from Intel Corporation. See also Gordon Moore, ”No Exponential Is Forever ... but We Can Delay 'Forever,' ” presented at the International Solid State Circuits Conference (lSSCC), February 10, 2003, researchl silicon/Gordon_Moore_ISSCC_ 021003.pdf. Data from Intel Corporation. See also Gordon Moore, ”No Exponential Is Forever ... but We Can Delay 'Forever,' ” presented at the International Solid State Circuits Conference (lSSCC), February 10, 2003, researchl silicon/Gordon_Moore_ISSCC_ 021003.pdf.

30. 30. Steve Cullen, ”Semiconductor Industry Outlook,” InStat/MDR, report no. IN0401550SI, April 2004, /abstract.asp?id=68&SKU=IN0401550SI. Steve Cullen, ”Semiconductor Industry Outlook,” InStat/MDR, report no. IN0401550SI, April 2004, /abstract.asp?id=68&SKU=IN0401550SI.

31. 31. World Semiconductor Trade Statistics, wsts..at. World Semiconductor Trade Statistics, wsts..at.

32. 32. Bureau of Economic a.n.a.lysis, U.S. Department of Commerce, Bureau of Economic a.n.a.lysis, U.S. Department of Commerce,

33. 33. See notes 2224 and 2630. See notes 2224 and 2630.

34. 34. International Technology Roadmap for Semiconductors, 2002 update, International Sematech. International Technology Roadmap for Semiconductors, 2002 update, International Sematech.

35. 35. ”25 Years of Computer History,” pros.com/timeline.html; Linley Gwennap, ”Birth of a Chip,” ”25 Years of Computer History,” pros.com/timeline.html; Linley Gwennap, ”Birth of a Chip,” BYTE BYTE (December 1996), /art/9612/sec6/art2.htm; ”The CDC 6000 Series Computer,” /standardpascal/cdc6400.html; ”A Chronology of Computer History,” lhcs/museum/chron.htm; Mark Brader, ”A Chronology of Digital Computing Machines (to 1952),” phist/61ordnance/index.html; Ken Polsson, ”Chronology of Personal Computers,” /~kpolsson/comphist; ”The History of Computing at Los Alamos,” bang.lanl.gov/video/sunedu/computer/comphist.html (requires pa.s.sword); the Machine Room, puter data, /article2/0,1759,23390,00.asp; Stan Augarten, (December 1996), /art/9612/sec6/art2.htm; ”The CDC 6000 Series Computer,” /standardpascal/cdc6400.html; ”A Chronology of Computer History,” lhcs/museum/chron.htm; Mark Brader, ”A Chronology of Digital Computing Machines (to 1952),” phist/61ordnance/index.html; Ken Polsson, ”Chronology of Personal Computers,” /~kpolsson/comphist; ”The History of Computing at Los Alamos,” bang.lanl.gov/video/sunedu/computer/comphist.html (requires pa.s.sword); the Machine Room, puter data, /article2/0,1759,23390,00.asp; Stan Augarten, Bit by Bit: An Ill.u.s.trated History of Computers Bit by Bit: An Ill.u.s.trated History of Computers (New York: Ticknor and Fields, 1984); International a.s.sociation of Electrical and Electronics Engineers (IEEE), (New York: Ticknor and Fields, 1984); International a.s.sociation of Electrical and Electronics Engineers (IEEE), Annals of the History of the Computer Annals of the History of the Computer 9.2 (1987): 15053 and 16.3 (1994): 20; Hans Moravec, 9.2 (1987): 15053 and 16.3 (1994): 20; Hans Moravec, Mind Children: The Future of Robot and Human Intelligence Mind Children: The Future of Robot and Human Intelligence (Cambridge, Ma.s.s.: Harvard University Press, 1988); Rene Moreau, (Cambridge, Ma.s.s.: Harvard University Press, 1988); Rene Moreau, The Computer Comes of Age The Computer Comes of Age (Cambridge, Ma.s.s.: MIT Press, 1984). (Cambridge, Ma.s.s.: MIT Press, 1984).

36. 36. The plots in this chapter labeled ”Logarithmic Plot” are technically semilogarithmic plots in that one axis (time) is on a linear scale, and the other axis is on a logarithmic scale. However, I am calling these plots ”logarithmic plots” for simplicity. The plots in this chapter labeled ”Logarithmic Plot” are technically semilogarithmic plots in that one axis (time) is on a linear scale, and the other axis is on a logarithmic scale. However, I am calling these plots ”logarithmic plots” for simplicity.

37. 37. See the appendix, ”The Law of Accelerating Returns Revisited,” which provides a mathematical derivation of why there are two levels of exponential growth (that is, exponential growth over time in which the rate of the exponential growth-the exponent-is itself growing exponentially over time) in computational power as measured by MIPS per unit cost. See the appendix, ”The Law of Accelerating Returns Revisited,” which provides a mathematical derivation of why there are two levels of exponential growth (that is, exponential growth over time in which the rate of the exponential growth-the exponent-is itself growing exponentially over time) in computational power as measured by MIPS per unit cost.

38. 38. Hans Moravec, ”When Will Computer Hardware Match the Human Brain?” Hans Moravec, ”When Will Computer Hardware Match the Human Brain?” Journal of Evolution and Technology Journal of Evolution and Technology 1 (1998), puter,” ”IBM Details Blue Gene Supercomputer,” CNET News CNET News, May 8, 2003, news.com.com/2100-1008_3-1000421.html.

42. 42. See Alfred North Whitehead, See Alfred North Whitehead, An Introduction to Mathematics An Introduction to Mathematics (London: Williams and Norgate, 1911), which he wrote at the same time he and Bertrand Russell were working on their seminal three-volume (London: Williams and Norgate, 1911), which he wrote at the same time he and Bertrand Russell were working on their seminal three-volume Principia Mathematica Principia Mathematica.

43. 43. While originally projected to take fifteen years, ”the Human Genome Project was finished two and a half years ahead of time and, at $2.7 billion in FY 1991 dollars, significantly under original spending projections”: /news/20031117/07.

45. 45. Data from National Center for Biotechnology Information, ”GenBank Statistics,” revised May 4, 2004, /news/medtech/0,1286,58481.00.html?tw=wn_story _related.

In contrast, the efforts to sequence HIV began in the 1980s. HIV 1 and HIV 2 were completely sequenced in 2003 and 2002 respectively. National Center for Biotechnology Information, /library/weekly/aa062398.htm; ”Initial Date of Operation of Computing Systems in the USA (19501958),” compiled from 1968 OECD data, members.iinet.net.au/~dgreen/timeline.html; Douglas Jones, ”Frequently Asked Questions about the DEC PDP-8 computer,” (New York: John Wiley and Sons, 1977); University of Cambridge Computer Laboratory, EDSAC99, /library/weekly/aa062398.htm; ”Initial Date of Operation of Computing Systems in the USA (19501958),” compiled from 1968 OECD data, members.iinet.net.au/~dgreen/timeline.html; Douglas Jones, ”Frequently Asked Questions about the DEC PDP-8 computer,” Programmed Data Processor-1 Handbook Programmed Data Processor-1 Handbook, Digital Equipment Corporation (19601963), /greeng3/pdp1/pdp1.html#INTRODUCTION; John Walker, ”Typical UNIVAC 1108 Prices: 1968,” /univac.htm; Wikipedia, ”Data General Nova,” /topic/data-general-nova; Darren Brewer, ”Chronology of Personal Computers 19721974,” uk.geocities.com/magoos_universe/comp1972.htm; ; /pa.r.s.e.cgi?news/pricewatch/raw/pw-010702; /pa.r.s.e.cgi ?news/pricewatch/raw/pw-020624;(11/17/04); sharkyextreme.com/guidesIWMPG/article.php/10706_2227191_2; Byte Byte advertis.e.m.e.nts, September 1975March 1998; advertis.e.m.e.nts, September 1975March 1998; PC Computing PC Computing advertis.e.m.e.nts, March 1977April 2000. advertis.e.m.e.nts, March 1977April 2000.

48. 48. Seagate, ”Products,” ./cda/products/discsales/index; Seagate, ”Products,” ./cda/products/discsales/index; Byte Byte advertis.e.m.e.nts, 19771998; advertis.e.m.e.nts, 19771998; PC Computing PC Computing advertis.e.m.e.nts, March 1999; Editors of Time-Life Books, advertis.e.m.e.nts, March 1999; Editors of Time-Life Books, Understanding Computers: Memory and Storage Understanding Computers: Memory and Storage, rev. ed. (New York: Warner Books, 1990); ”Historical Notes about the Cost of Hard Drive Storage s.p.a.ce,” /history/ibm-305-ramac.html; John C. McCallum, ”Disk Drive Prices (19552004),” /diskprice.htm.

49. 49. James DeRose, James DeRose, The Wireless Data Handbook The Wireless Data Handbook (St. Johnsbury, Vt.: Quantrum, 1996); First Mile Wireless, l; J.B. Miles, ”Wireless LANs,” (St. Johnsbury, Vt.: Quantrum, 1996); First Mile Wireless, l; J.B. Miles, ”Wireless LANs,” Government Computer News Government Computer News 18.28 (April 30, 1999), /vo118_no28/guide/514-1.html; 18.28 (April 30, 1999), /vo118_no28/guide/514-1.html; Wireless Week Wireless Week (April 14, 1997), /toc/4%2F14%2F1997; Office of Technology a.s.sessment, ”Wireless Technologies and the National Information Infrastructure,” September 1995, infoventures.com/emf/federal/ota/ota95-tc.html; Signal Lake, ”Broadband Wireless Network Economics Update,” January 14, 2003, /publications/broadbandupdate.pdf; BridgeWave Communications communication, /050604.htm. (April 14, 1997), /toc/4%2F14%2F1997; Office of Technology a.s.sessment, ”Wireless Technologies and the National Information Infrastructure,” September 1995, infoventures.com/emf/federal/ota/ota95-tc.html; Signal Lake, ”Broadband Wireless Network Economics Update,” January 14, 2003, /publications/broadbandupdate.pdf; BridgeWave Communications communication, /050604.htm.

50. 50. Internet Software Consortium (/webstation/net-history.shtml; Robert Zakon, ”Hobbes' Internet Timeline v8.0,” /webstation/net-history.shtml; Robert Zakon, ”Hobbes' Internet Timeline v8.0,” /Daily/daily.asp?vn=v9n229&fecha=December%2005,%202002; V. Cerf, ”Cerf's Up,” 2004, global.mci.com/de/resources/cerfs_up/.

54. 54. H. C. Nathanson et al., ”The Resonant Gate Transistor,” H. C. Nathanson et al., ”The Resonant Gate Transistor,” IEEE Transactions on Electron Devices IEEE Transactions on Electron Devices 14.3 (March 1967): 11733; Larry J. Hornbeck, ”128 x 128 Deformable Mirror Device,” 14.3 (March 1967): 11733; Larry J. Hornbeck, ”128 x 128 Deformable Mirror Device,” IEEE Transactions on Electron Devices IEEE Transactions on Electron Devices 30.5 (April 1983): 53943; J. Storrs Hall, ”Nanocomputers and Reversible Logic,” 30.5 (April 1983): 53943; J. Storrs Hall, ”Nanocomputers and Reversible Logic,” Nanotechnology Nanotechnology 5 (July 1994): 15767; V.V.Aristov et al., ”A New Approach to Fabrication of Nanostructures,” 5 (July 1994): 15767; V.V.Aristov et al., ”A New Approach to Fabrication of Nanostructures,” Nanotechnology Nanotechnology 6 (April 1995): 3539; C. Montemagno et al., ”Constructing Biological Motor Powered Nanomechanical Devices,” 6 (April 1995): 3539; C. Montemagno et al., ”Constructing Biological Motor Powered Nanomechanical Devices,” Nanotechnology Nanotechnology 10 (1999): 22531,News Service NewScientist.com News Service, March 18, 2004, /article.ns?id=dn4794.

55. 55. ETC Group, ”From Genomes to Atoms: The Big Down,” p. 39, pressed data. Here are two approaches to estimating the compressed information content of the genome, both of which demonstrate that a range of thirty to one hundred million bytes is conservatively high. Although it is not possible to determine precisely the information content in the genome, because of the repeated base pairs it is clearly much less than the total uncompressed data. Here are two approaches to estimating the compressed information content of the genome, both of which demonstrate that a range of thirty to one hundred million bytes is conservatively high.

1. 1. In terms of the uncompressed data, there are three billion DNA rungs in the human genetic code, each coding two bits (since there are four possibilities for each DNA base pair). Thus, the human genome is about 800 million bytes uncompressed. The noncoding DNA used to be called ”junk DNA,” but it is now clear that it plays an important role in gene expression. However, it is very inefficiently coded. For one thing, there are ma.s.sive redundancies (for example, the sequence called ”ALU” is repeated hundreds of thousands of times), which compression algorithms can take advantage of. In terms of the uncompressed data, there are three billion DNA rungs in the human genetic code, each coding two bits (since there are four possibilities for each DNA base pair). Thus, the human genome is about 800 million bytes uncompressed. The noncoding DNA used to be called ”junk DNA,” but it is now clear that it plays an important role in gene expression. However, it is very inefficiently coded. For one thing, there are ma.s.sive redundancies (for example, the sequence called ”ALU” is repeated hundreds of thousands of times), which compression algorithms can take advantage of.

With the recent explosion of genetic data banks, there is a great deal of interest in compressing genetic data. Recent work on applying standard data compression algorithms to genetic data indicates that reducing the data by 90 percent (for bit-perfect compression) is feasible: Hisahiko Sato et al., ”DNA Data Compression in the Post Genome Era,” Genome Informatics Genome Informatics 12 (2001): 51214, press the genome to about 80 million bytes without loss of information (meaning we can perfectly reconstruct the full 800-million-byte uncompressed genome).Now consider that more than 98 percent of the genome does not code for proteins. Even after standard data compression (which eliminates redundancies and uses a dictionary lookup for common sequences), the algorithmic content of the noncoding regions appears to be rather low, meaning that it is likely that we could code an algorithm that would perform the same function with fewer bits. However, since we are still early in the process of reverse engineering the genome, we cannot make a reliable estimate of this further decrease based on a functionally equivalent algorithm. I am using, therefore, a range of 30 to 100 million bytes of compressed information in the genome. The top part of this range a.s.sumes only data compression and no algorithmic simplification.Only a portion (although the majority) of this information characterizes the design of the brain.

2. 2. Another line of reasoning is as follows. Though the human genome contains around 3 billion bases, only a small percentage, as mentioned above, codes for proteins. By current estimates, there are 26,000 genes that code for proteins. If we a.s.sume those genes average 3,000 bases of useful data, those equal only approximately 78 million bases. A base of DNA requires only two bits, which translate to about 20 million bytes (78 million bases divided by four). In the protein-coding sequence of a gene, each ”word” (codon) of three DNA bases translates into one amino acid. There are, therefore, 4 Another line of reasoning is as follows. Though the human genome contains around 3 billion bases, only a small percentage, as mentioned above, codes for proteins. By current estimates, there are 26,000 genes that code for proteins. If we a.s.sume those genes average 3,000 bases of useful data, those equal only approximately 78 million bases. A base of DNA requires only two bits, which translate to about 20 million bytes (78 million bases divided by four). In the protein-coding sequence of a gene, each ”word” (codon) of three DNA bases translates into one amino acid. There are, therefore, 43 (64) possible codon codes, each consisting of three DNA bases. There are, however, only 20 amino acids used plus a stop codon (null amino acid) out of the 64. The rest of the 4 (64) possible codon codes, each consisting of three DNA bases. There are, however, only 20 amino acids used plus a stop codon (null amino acid) out of the 64. The rest of the 43 codes are used as synonyms of the 21 useful ones. Whereas 6 bits are required to code for 64 possible combinations, only about 4.4 (log codes are used as synonyms of the 21 useful ones. Whereas 6 bits are required to code for 64 possible combinations, only about 4.4 (log221) bits are required to code for 21 possibilities, a savings of 1.6 out of 6 bits (about 27 percent), bringing us down to about 15 million bytes. In addition, some standard compression based on repeating sequences is feasible here, although much less compression is possible on this protein-coding portion of the DNA than in the so-called junk DNA, which has ma.s.sive redundancies. this will bring the figure probably below 12 million bytes. However, now we have to add information for the noncoding portion of the DNA that controls gene expression. Although this portion of the DNA comprises the bulk of the genome, it appears to have a low level of information content and is replete with ma.s.sive redundancies. Estimating that it matches the approximately 12 million bytes of protein-coding DNA, we again come to approximately 24 million bytes. From this perspective, an estimate of 30 to 100 million bytes is conservatively high.

58. 58. Continuous values can be represented by floating-point numbers to any desired degree of accuracy. A floating-point number consists of two sequences of bits. One ”exponent” sequence represents a power of 2. The ”base” sequence represents a fraction of 1. By increasing the number of bits in the base, any desired degree of accuracy can be achieved. Continuous values can be represented by floating-point numbers to any desired degree of accuracy. A floating-point number consists of two sequences of bits. One ”exponent” sequence represents a power of 2. The ”base” sequence represents a fraction of 1. By increasing the number of bits in the base, any desired degree of accuracy can be achieved.

59. 59. Stephen Wolfram, Stephen Wolfram, A New Kind of Science A New Kind of Science (Champaign, Ill.: Wolfram Media, 2002). (Champaign, Ill.: Wolfram Media, 2002).

60. 60. Early work on a digital theory of physics was also presented by Frederick W. Kantor, Early work on a digital theory of physics was also presented by Frederick W. Kantor, Information Mechanics Information Mechanics (New York: John Wiley and Sons, 1977). Links to several of Kantor's papers can be found at w3.execnet.com/kantor/pm00.htm (l997); w3.execnet.com/kantor/1b2p.htm (l989); and w3.execnet.com/kantor/ipoim.htm (l982). Also see at /listbox/k/msg05621.html. (New York: John Wiley and Sons, 1977). Links to several of Kantor's papers can be found at w3.execnet.com/kantor/pm00.htm (l997); w3.execnet.com/kantor/1b2p.htm (l989); and w3.execnet.com/kantor/ipoim.htm (l982). Also see at /listbox/k/msg05621.html.

61. 61. Konrad Zuse, ”Rechnender Raum,” Konrad Zuse, ”Rechnender Raum,” Elektronische Datenverarbeitung Elektronische Datenverarbeitung, 1967, vol. 8, pp. 33644. Konrad Zuse's book on a cellular automaton-based universe was published two years later: Rechnender Raum, Schriften zur Datenverarbeitung Rechnender Raum, Schriften zur Datenverarbeitung (Braunschweig, Germany: Friedrich Vieweg & Sohn, 1969). English translation: (Braunschweig, Germany: Friedrich Vieweg & Sohn, 1969). English translation: Calculating s.p.a.ce Calculating s.p.a.ce, MIT Technical Translation AZT-70-164-GEMIT, February 1970.MIT Project MAC, Cambridge, MA 02139. PDF.

62. 62. Edward Fredkin quoted in Robert Wright, ”Did the Universe Just Happen?” Edward Fredkin quoted in Robert Wright, ”Did the Universe Just Happen?” Atlantic Monthly Atlantic Monthly, April 1988, 2944, digitalphysics.org/Publications/Wri88a/html.

63. 63. Ibid. Ibid.

64. 64. Many of Fredkin's results come from studying his own model of computation, which explicitly reflects a number of fundamental principles of physics. See the cla.s.sic article Edward Fredkin and Tommaso Toffoli, ”Conservative Logic,” Many of Fredkin's results come from studying his own model of computation, which explicitly reflects a number of fundamental principles of physics. See the cla.s.sic article Edward Fredkin and Tommaso Toffoli, ”Conservative Logic,” International Journal of Theoretical Physics International Journal of Theoretical Physics 21.34 (l982): 21953, putation a.n.a.lytically similar to those of Fredkin's may be found in Norman Margolus, ”Physics and Computation,” Ph.D. thesis, MIT/LCS/TR-415, MIT Laboratory for Computer Science, 1988. 21.34 (l982): 21953, putation a.n.a.lytically similar to those of Fredkin's may be found in Norman Margolus, ”Physics and Computation,” Ph.D. thesis, MIT/LCS/TR-415, MIT Laboratory for Computer Science, 1988.