Ray Kurzweil - How to Create a Mind - The Secret of Human Thought Revealed

1. Paul G. Allen and Mark Greaves, “Paul Allen: The Singularity Isn’t Near,” Technology Review , October 12, 2011, http://www.technologyreview.com/blog/guest/27206/.

2. ITRS, “International Technology Roadmap for Semiconductors,” http://www.itrs.net/Links/2011ITRS/Home2011.htm.

3. Ray Kurzweil, The Singularity Is Near (New York: Viking, 2005), chapter 2.

4. Endnote 2 in Allen and Greaves, “The Singularity Isn’t Near,” reads as follows: “We are beginning to get within range of the computer power we might need to support this kind of massive brain simulation. Petaflop-class computers (such as IBM’s BlueGene/P that was used in the Watson system) are now available commercially. Exaflop-class computers are currently on the drawing boards. These systems could probably deploy the raw computational capability needed to simulate the firing patterns for all of a brain’s neurons, though currently it happens many times more slowly than would happen in an actual brain.”

5. Kurzweil, The Singularity Is Near , chapter 9, section titled “The Criticism from Software” (pp. 435–42).

6. Ibid., chapter 9.

7. Although it is not possible to precisely determine 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 30 to 100 million bytes is conservatively high.

1. In terms of the uncompressed data, there are 3 billion DNA rungs in the human genetic code, each coding 2 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 massive 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 12 (2001): 512–14, http://www.jsbi.org/journal/GIW01/GIW01P130.pdf.

Thus we can compress 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 assumes only data compression and no algorithmic simplification.

Only a portion (although the majority) of this information characterizes the design of the brain.

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 assume those genes average 3,000 bases of useful data, those equal only approximately 78 million bases. A base of DNA requires only 2 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 3(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 221) 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 massive redundancies. So 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 constitutes the bulk of the genome, it appears to have a low level of information content and is replete with massive 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.

8. Dharmendra S. Modha et al., “Cognitive Computing,” Communications of the ACM 54, no. 8 (2011): 62–71, http://cacm.acm.org/magazines/2011/8/114944-cognitive-computing/fulltext.

9. Kurzweil, The Singularity Is Near , chapter 9, section titled “The Criticism from Ontology: Can a Computer Be Conscious?” (pp. 458–69).

10. Michael Denton, “Organism and Machine: The Flawed Analogy,” in Are We Spiritual Machines? Ray Kurzweil vs. the Critics of Strong AI (Seattle: Discovery Institute, 2002).

11. Hans Moravec, Mind Children (Cambridge, MA: Harvard University Press, 1988).

1. “In U.S., Optimism about Future for Youth Reaches All-Time Low,” Gallup Politics, May 2, 2011, http://www.gallup.com/poll/147350/optimism-future-youth-reaches-time-low.aspx.

2. James C. Riley, Rising Life Expectancy: A Global History (Cambridge: Cambridge University Press, 2001).

3. J. Bradford DeLong, “Estimating World GDP, One Million B.C.—Present,” May 24, 1998, http://econ161.berkeley.edu/TCEH/1998_Draft/World_GDP/Estimating_World_GDP.xhtml, and http://futurist.typepad.com/my_weblog/2007/07/economic-growth.xhtml. See also Peter H. Diamandis and Steven Kotler, Abundance: The Future Is Better Than You Think (New York: Free Press, 2012).

4. Martine Rothblatt, Transgender to Transhuman (privately printed, 2011). She explains how a similarly rapid trajectory of acceptance is most likely to occur for “transhumans,” for example, nonbiological but convincingly conscious minds as discussed in chapter 9.

5. The following excerpt from The Singularity Is Near , chapter 3 (pp. 133–35), by Ray Kurzweil (New York: Viking, 2005), discusses the limits of computation based on the laws of physics:

The ultimate limits of computers are profoundly high. Building on work by University of California at Berkeley Professor Hans Bremermann and nanotechnology theorist Robert Freitas, MIT Professor Seth Lloyd has estimated the maximum computational capacity, according to the known laws of physics, of a computer weighing one kilogram and occupying one liter of volume—about the size and weight of a small laptop computer—what he calls the “ultimate laptop.”

[Note: Seth Lloyd, “Ultimate Physical Limits to Computation,” Nature 406 (2000): 1047–54.

[Early work on the limits of computation were done by Hans J. Bremermann in 1962: Hans J. Bremermann, “Optimization Through Evolution and Recombination,” in M. C. Yovits, C. T. Jacobi, C. D. Goldstein, eds., Self-Organizing Systems (Washington, D.C.: Spartan Books, 1962), pp. 93–106.