The Singularity Is Near: When Humans Transcend Biology (111 page)

• 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
.

184
. 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
.