The Singularity Is Near: When Humans Transcend Biology
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(i) In the context of a game (for example, chess), the last move allows us to win (such as checkmate).
(ii) In the context of solving a mathematical theorem, the last step proves the theorem.
(iii) In the context of an artistic program (for example, a computer poet or composer), the last step matches the goals for the next word or note.
If the problem has been satisfactorily solved, the program returns with a value of “SUCCESS” and the sequence of steps that caused the success.
If the problem has not been solved, determine if a solution is now hopeless. Examples are:
(i) In the context of a game (such as chess), this move causes us to lose (checkmate for the other side).
(ii) In the context of solving a mathematical theorem, this step violates the theorem.
(iii) In the context of an artistic creation, this step violates the goals for the next word or note.
If the solution at this point has been deemed hopeless, the program returns with a value of “FAILURE.”
If the problem has been neither solved nor deemed hopeless at this point of recursive expansion, determine whether or not the expansion should be abandoned anyway. This is a key aspect of the design and takes into consideration the limited amount of computer time we have to spend. Examples are:
(i) In the context of a game (such as chess), this move puts our side sufficiently “ahead” or “behind.” Making this determination may not be straightforward and is the primary design decision. However, simple approaches (such as adding up piece values) can still provide good results. If the program determines that our side is sufficiently ahead, then Pick Best Next Step returns in a similar manner to a determination that our side has won (that is, with a value of “SUCCESS”). If the program determines that our side is sufficiently behind, then Pick Best Next Step returns in a similar manner to a determination that our side has lost (that is, with a value of “FAILURE”).
(ii) In the context of solving a mathematical theorem, this step involves determining if the sequence of steps in the proof is unlikely to yield a proof. If so, then this path should be abandoned, and Pick Best Next Step returns in a similar manner to a determination that this step violates the theorem (that is, with a value of “FAILURE”). There is no “soft” equivalent of success. We can’t return with a value of “SUCCESS” until we have actually solved the problem. That’s the nature of math.
(iii) In the context of an artistic program (such as a computer poet or composer), this step involves determining if the sequence of steps (such as the words in a poem, notes in a song) is unlikely to satisfy the goals for the next step. If so, then this path should be abandoned, and Pick Best Next Step returns in a similar manner to a determination that this step violates the goals for the next step (that is, with a value of “FAILURE”).
If Pick Best Next Step has not returned (because the program has neither determined success nor failure nor made a determination that this path should be abandoned at this point), then we have not escaped from continued recursive expansion. In this case, we now generate a list of all possible next steps at this point. This is where the precise statement of the problem comes in:
(i) In the context of a game (such as chess), this involves generating all possible moves for “our” side for the current state of the board. This involves a straightforward codification of the rules of the game.
(ii) In the context of finding a proof for a mathematical theorem, this involves listing the possible axioms or previously proved theorems that can be applied at this point in the solution.
(iii) In the context of a cybernetic art program, this involves listing the possible words/notes/line segments that could be used at this point.
For each such possible next step:
(i) Create the hypothetical situation that would exist if this step were implemented. In a game, this means the hypothetical state of the board. In a mathematical proof, this means adding this step (for example, axiom) to the proof. In an art program, this means adding this word/note/line segment.
(ii) Now call Pick Best Next Step to examine this hypothetical situation. This is, of course, where the recursion comes in because the program is now calling itself.
(iii) If the above call to Pick Best Next Step returns with a value of “SUCCESS,” then return from the call to Pick Best Next Step (that we are now in) also with a value of “SUCCESS.” Otherwise consider the next possible step.
If all the possible next steps have been considered without finding a step that resulted in a return from the call to Pick Best Next Step with a value of “SUCCESS,” then return from this call to Pick Best Next Step (that we are now in) with a value of “FAILURE.”
End of PICK BEST NEXT STEP
If the original call to Pick Best Next Move returns with a value of “SUCCESS,” it will also return the correct sequence of steps:
(i) In the context of a game, the first step in this sequence is the next move you should make.
(ii) In the context of a mathematical proof, the full sequence of steps is the proof.
(iii) In the context of a cybernetic art program, the sequence of steps is your work of art.
If the original call to Pick Best Next Step returns with a value of “FAILURE,” then you need to go back to the drawing board.
Key Design Decisions
In the simple schema above, the designer of the recursive algorithm needs to determine the following at the outset:
The key to a recursive algorithm is the determination in Pick Best Next Step of when to abandon the recursive expansion. This is easy when the program has achieved clear success (such as checkmate in chess or the requisite solution in a math or combinatorial problem) or clear failure. It is more difficult when a clear win or loss has not yet been achieved. Abandoning a line of inquiry before a well-defined outcome is necessary because otherwise the program might run for billions of years (or at least until the warranty on your computer runs out).
The other primary requirement for the recursive algorithm is a straight-forward codification of the problem. In a game like chess, that’s easy. But in other situations, a clear definition of the problem is not always so easy to come by.
178. See Kurzweil CyberArt, http://www.KurzweilCyberArt.com, for further description of Ray Kurzweil’s Cybernetic Poet and to download a free copy of the program. See U.S. Patent No. 6,647,395, “Poet Personalities,” inventors: Ray Kurzweil and John Keklak. Abstract: “A method of generating a poet personality including reading poems, each of the poems containing text, generating analysis models, each of the analysis models representing one of the poems and storing the analysis models in a personality data structure. The personality data structure further includes weights, each of the weights associated with each of the analysis models. The weights include integer values.”
179. Ben Goertzel: The Structure of Intelligence (New York: Springer-Verlag, 1993); The Evolving Mind (Gordon and Breach, 1993); Chaotic Logic (Plenum, 1994); From Complexity to Creativity (Plenum, 1997). For a link to Ben Goertzel’s books and essays, see http://www.goertzel.org/work.html.
180. KurzweilAI.net (http://www.KurzweilAI.net) provides hundreds of articles by one hundred “big thinkers” and other features on “accelerating intelligence.” The site offers a free daily or weekly newsletter on the latest developments in the areas covered by this book. To subscribe, enter your e-mail address (which is maintained in strict confidence and is not shared with anyone) on the home page.
181. John Gosney, Business Communications Company, “Artificial Intelligence: Burgeoning Applications in Industry,” June 2003, http://www.bccresearch.com/comm/G275.html.
182. Kathleen Melymuka,“Good Morning, Dave ...,” Computerworld, November 11, 2002, http://www.computerworld.com/industrytopics/defense/story/
0,10801,75728,00.html.
183. JTRS Technology Awareness Bulletin, August 2004, http://jtrs.army.mil/sections/technicalinformation/fset_technical.html?tech_aware_2004-8.
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84. Otis Port, Michael Arndt, and John Carey, “Smart Tools,” Spring 2003, http://www.businessweek.com/bw50/content/mar2003/a3826072.htm.
185. Wade Roush, “Immobots Take Control: From Photocopiers to Space Probes, Machines Injected with Robotic Self-Awareness Are Reliable Problem Solvers,” Technology Review (December 2002–January 2003), http://www.occm.de/roush1202.pdf.
186. Jason Lohn quoted in NASA news release “NASA ‘Evolutionary’ Software Automatically Designs Antenna,” http://www.nasa.gov/lb/centers/ames/news/releases/2004/
04_55AR.html.
187. Robert Roy Britt, “Automatic Astronomy: New Robotic Telescopes See and Think,” June 4, 2003, http://www.space.com/businesstechnology/technology/
automated_astronomy_030604.html.
188. H. Keith Melton, “Spies in the Digital Age,” http://www.cnn.com/SPECIALS/cold.war/experience/spies/
melton.essay.
189. “United Therapeutics (UT) is a biotechnology company focused on developing chronic therapies for life-threatening conditions in three therapeutic areas: cardiovascular, oncology and infectious diseases” (http://www.unither.com). Kurzweil Technologies is working with UT to develop pattern recognition–based analysis from either “Holter” monitoring (twenty-four-hour recordings) or “Event” monitoring (thirty days or more).
190. Kristen Philipkoski, “A Map That Maps Gene Functions,” Wired News, May 28, 2002, www.wired.com/news/medtech/0,1286,52723,00.html.
191. Jennifer Ouellette, “Bioinformatics Moves into the Mainstream,” The Industrial Physicist (October–November 2003), http://www.sciencemasters.com/bioinformatics.pdf.
192. Port, Arndt, and Carey, “Smart Tools.”
193. “Protein Patterns in Blood May Predict Prostate Cancer Diagnosis,” National Cancer Institute, October 15, 2002, http://www.nci.nih.gov/newscenter/Prostate Proteomics, reporting on Emanuel F. Petricoin et al., “Serum Proteomic Patterns for Detection of Prostate Cancer,” Journal of the National Cancer Institute 94 (2002): 1576–78.
194. Charlene Laino, “New Blood Test Spots Cancer,” December 13, 2002, http://my.webmd.com/content/Article/56/65831.htm; Emanuel F. Petricoin III et al., “Use of Proteomic Patterns in Serum to Identify Ovarian Cancer,” Lancet 359.9306 (February 16, 2002): 572–77.
195. For information of TriPath’s FocalPoint, see “Make a Diagnosis,” Wired, October 2003, http://www.wired.com/wired/archive/10.03/everywhere.html?pg=5. Mark Hagland, “Doctors’ Orders,” January 2003, http://www.healthcare-informatics.com/issues/2003/01_03/cpoe.htm.
196. Ross D. King et al., “Functional Genomic Hypothesis Generation and Experimentation by a Robot Scientist,” Nature 427 (January 15, 2004): 247–52.
197. Port, Arndt, and Carey, “Smart Tools.”
198. “Future Route Releases AI-Based Fraud Detection Product,” August 18, 2004, http://www.finextra.com/fullstory.asp?id=12365.
199. John Hackett, “Computers Are Learning the Business,” Collections World, April 24, 2001, http://www.creditcollectionsworld.com/news/042401_2.htm.
200. “Innovative Use of Artificial Intelligence, Monitoring NASDAQ for Potential Insider Trading and Fraud,” AAAI press release, July 30, 2003, http://www.aaai.org/Pressroom/Releases/release-03-0730.html.
201. “Adaptive Learning, Fly the Brainy Skies,” Wired News, March 2002, http://www.wired.com/wired/archive/10.03/everywhere.html?pg=2.
202. “Introduction to Artificial Intelligence,” EL 629, Maxwell Air Force Base, Air University Library course, http://www.au.af.mil/au/aul/school/acsc/ai02.htm. Sam Williams, “Computer, Heal Thyself,” Salon.com, July 12, 2004, http://www.salon.com/tech/feature/2004/07/12/
self_healing_computing/index_np.html.
203. See http://www.Seegrid.com. Disclosure: The author is an investor in Seegrid and a member of its board of directors.
204. No Hands Across America Web site, http://cart.frc.ri.cmu.edu/users/hpm/
project.archive/reference.file/nhaa.html, and “Carnegie Mellon Researchers Will Prove Autonomous Driving Technologies During a 3,000 Mile, Hands-off-the-Wheel Trip from Pittsburgh to San Diego,” Carnegie Mellon press release, http://www-2.cs.cmu.edu/afs/cs/user/tjochem/www/nhaa/
official_press_release.html; Robert J. Derocher, “Almost Human,” September 2001, http://www.insight-mag.com/insight/01/09/col-2-pt-1-ClickCulture.htm.
205. “Search and Rescue Robots,” Associated Press, September 3, 2004, http://www.smh.com.au/articles/2004/09/02/1093939058792.html?oneclick=true.
206. “From Factoids to Facts,” Economist, August 26, 2004, http://www.economist.com/science/displayStory.cfm?story_id=3127462.
207. Joe McCool, “Voice Recognition, It Pays to Talk,” May 2003, http://www.bcs.org/BCS/Products/Publications/JournalsAndMagazines/
ComputerBulletin/OnlineArchive/may03/voicerecognition.htm.
208. John Gartner, “Finally a Car That Talks Back,” Wired News, September 2, 2004, http://www.wired.com/news/autotech/0,2554,64809,00.html?tw=wn_14techhead.
209. “Computer Language Translation System Romances the Rosetta Stone,” Information Sciences Institute, USC School of Engineering (July 24, 2003), http://www.usc.edu/isinews/stories/102.html.
210. Torsten Reil quoted in Steven Johnson, “Darwin in a Box,” Discover 24.8 (August 2003), http://www.discover.com/issues/aug-03/departments/feattech/.
211. “Let Software Catch the Game for You,” July 3, 2004, http://www.newscientist.com/news/news.jsp?id=ns99996097.
212. Michelle Delio, “Breeding Race Cars to Win,” Wired News, June 18, 2004, http://www.wired.com/news/autotech/0,2554,63900,00.html.
213. Marvin Minsky, The Society of Mind (New York: Simon & Schuster, 1988).
214. Hans Moravec, “When Will Computer Hardware Match the Human Brain?” Journal of Evolution and Technology 1 (1998).
215. Ray Kurzweil, The Age of Spiritual Machines (New York: Viking, 1999), p. 156.
216. See chapter 2, notes 22 and 23, on the International Technology Roadmap for Semiconductors.
217. “The First Turing Test,” http://www.loebner.net/Prizef/loebner-prize.html.
218. Douglas R. Hofstadter, “A Coffeehouse Conversation on the Turing Test,” May 1981, included in Ray Kurzweil, The Age of Intelligent Machines (Cambridge, Mass.: MIT Press, 1990), pp. 80–102, http://www.KurzweilAI.net/meme/frame.html?main=/articles/art0318.html.
219. Ray Kurzweil, “Why I Think I Will Win,” and Mitch Kapor, “Why I Think I Will Win,” rules: http://www.KurzweilAI.net/meme/frame.html?main=/articles/art0373.html; Kapor: http://www.KurzweilAI.net/meme/frame.html?main=/articles/art0412.html; Kurzweil: http://www.KurzweilAI.net/meme/frame.html?main=/articles/art0374.html; Kurzweil “final word”: http://www.KurzweilAI.net/meme/frame.html?main=/articles/art0413.html.
220. Edward A. Feigenbaum, “Some Challenges and Grand Challenges for Computational Intelligence,” Journal of the Association for Computing Machinery 50 (January 2003): 32–40.
221. According to the serial endosymbiosis theory of eukaryotic evolution, the ancestors of mitochondria (the structures in cells that produce energy and have their own genetic code comprising thirteen genes in humans) were originally independent bacteria (that is, not part of another cell) similar to the Daptobacter bacteria of today. “Serial Endosymbiosis Theory,” http://encyclopedia.thefreedictionary.com/Serial%20endosymbiosis%20theory.
Chapter Six: The Impact . . .
1. Donovan, “Season of the Witch,” Sunshine Superman (1966).
2. Reasons for the reduction in farm workforce include the mechanization that lessened the need for animal and human labor, the economic opportunities that were created in urban areas during World War II, and the development of intensive farming techniques that required less land for comparable yields. U.S. Department of Agriculture, National Agricultural Statistics Service, Trends in U.S. Agriculture, http://www.usda.gov/nass/pubs/trends/farmpopulation.htm. Computer-assisted production, just-in-time production (which results in lower inventory), and offshoring manufacturing to reduce costs
are some of the methods that have contributed to the loss of factory jobs. See U.S. Department of Labor, Future-work: Trends and Challenges of Work in the 21st Century, http://www.dol.gov/asp/programs/history/herman/reports/
futurework/report.htm.
3. For example, see Natasha Vita-More, “The New [Human] Genre Primo [First] Posthuman,” paper delivered at Ciber@RT Conference, Bilbao, Spain, April 2004, http://www.natasha.cc/paper.htm.
4. Rashid Bashir summarizes in 2004:
Much progress has also been made in therapeutic micro- and nanotechnology. . . . Some specific examples include (i) silicon-based implantable devices that can be electrically actuated to open an orifice from which preloaded drugs can be released, (ii) silicon devices functionalized with electrically actuated polymers which can act as a valve or muscle to release preloaded drugs, (iii) silicon-based micro-capsules with nano-porous membranes for the release of insulin, (iv) all polymer (or hydrogel) particles which can be pre-loaded with drugs and then forced to expand upon exposure to specific environmental conditions such as change in pH and release the loaded drug, (v) metal nano-particles coated with recognition proteins, where the particles can be heated with external optical energy and can locally heat and damage unwanted cells and tissue, etc.
R. Bashir, “BioMEMS: State-of-the-Art in Detection, Opportunities and Prospects,” Advanced Drug Delivery Reviews 56.11 (September 22, 2004): 1565–86. Reprint available at https://engineering.purdue.edu/LIBNA/pdf/publications/BioMEMS%20review%20ADDR%20final.pdf. See also Richard Grayson et al., “A BioMEMS Review: MEMS Technology for Physiologically Integrated Devices,” IEEE Proceedings 92 (2004): 6–21.
5. For activities of the International Society for BioMEMS and Biomedical Nano-technology, see http://www.bme.ohio-state.edu/isb.BioMEMS conferences are also listed on the SPIE Web site, http://www.spie.org/Conferences.