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

10. Robert Mannell, “Acoustic Representations of Speech,” 2008, http://clas.mq.edu.au/acoustics/frequency/acoustic_speech.xhtml.

11. Here is the basic schema for a genetic (evolutionary) algorithm. Many variations are possible, and the designer of the system needs to provide certain critical parameters and methods, detailed below.

The Evolutionary Algorithm

Create N solution “creatures.” Each one has:

A genetic code: a sequence of numbers that characterize a possible solution to the problem. The numbers can represent critical parameters, steps to a solution, rules, etc.

For each generation of evolution, do the following:

Do the following for each of the N solution creatures:

Apply this solution creature’s solution (as represented by its genetic code) to the problem, or simulated environment. Rate the solution.

Pick the L solution creatures with the highest ratings to survive into the next generation.

Eliminate the ( N – L ) nonsurviving solution creatures.

Create ( N – L ) new solution creatures from the L surviving solution creatures by:

(1) Making copies of the L surviving creatures. Introduce small random variations into each copy; or

(2) Create additional solution creatures by combining parts of the genetic code (using “sexual” reproduction, or otherwise combining portions of the chromosomes) from the L surviving creatures; or

(3) Do a combination of (1) and (2).

Determine whether or not to continue evolving:

Improvement = (highest rating in this generation) – (highest rating in the previous generation).

If Improvement < Improvement Threshold then we’re done.

The solution creature with the highest rating from the last generation of evolution has the best solution. Apply the solution defined by its genetic code to the problem.

Key Design Decisions

In the simple schema above, the designer needs to determine at the outset:

Key parameters:

N

L

Improvement threshold.

What the numbers in the genetic code represent and how the solution is computed from the genetic code.

A method for determining the N solution creatures in the first generation. In general, these need only be “reasonable” attempts at a solution. If these first-generation solutions are too far afield, the evolutionary algorithm may have difficulty converging on a good solution. It is often worthwhile to create the initial solution creatures in such a way that they are reasonably diverse. This will help prevent the evolutionary process from just finding a “locally” optimal solution.

How the solutions are rated.

How the surviving solution creatures reproduce.

Variations

Many variations of the above are feasible. For example:

There does not need to be a fixed number of surviving solution creatures ( L ) from each generation. The survival rule(s) can allow for a variable number of survivors.

There does not need to be a fixed number of new solution creatures created in each generation ( N – L ). The procreation rules can be independent of the size of the population. Procreation can be related to survival, thereby allowing the fittest solution creatures to procreate the most.

The decision as to whether or not to continue evolving can be varied. It can consider more than just the highest-rated solution creature from the most recent generation(s). It can also consider a trend that goes beyond just the last two generations.

12. Dileep George, “How the Brain Might Work: A Hierarchical and Temporal Model for Learning and Recognition” (PhD dissertation, Stanford University, June 2008).

13. A. M. Turing, “Computing Machinery and Intelligence,” Mind , October 1950.

14. Hugh Loebner has a “Loebner Prize” competition that is run each year. The Loebner silver medal will go to a computer that passes Turing’s original text-only test. The gold medal will go to a computer that can pass a version of the test that includes audio and video input and output. In my view, the inclusion of audio and video does not actually make the test more challenging.

15. “Cognitive Assistant That Learns and Organizes,” Artificial Intelligence Center, SRI International, http://www.ai.sri.com/project/CALO.

16. Dragon Go! Nuance Communications, Inc., http://www.nuance.com/products/dragon-go-in-action/index.htm.

17. “Overcoming Artificial Stupidity,” WolframAlpha Blog , April 17, 2012, http://blog.wolframalpha.com/author/stephenwolfram/.

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2. A. M. Turing, “On Computable Numbers, with an Application to the Entscheidungsproblem,” Proceedings of the London Mathematical Society Series 2, vol. 42 (1936–37): 230–65, http://www.comlab.ox.ac.uk/activities/ieg/e-library/sources/tp2-ie.pdf. A. M. Turing, “On Computable Numbers, with an Application to the Entscheidungsproblem: A Correction,” Proceedings of the London Mathematical Society 43 (1938): 544–46.

3. John von Neumann, “First Draft of a Report on the EDVAC,” Moore School of Electrical Engineering, University of Pennsylvania, June 30, 1945. John von Neumann, “A Mathematical Theory of Communication,” Bell System Technical Journal , July and October 1948.

4. Jeremy Bernstein, The Analytical Engine: Computers—Past, Present, and Future , rev. ed. (New York: William Morrow & Co., 1981).

5. “Japan’s K Computer Tops 10 Petaflop/s to Stay Atop TOP500 List,” Top 500 , November 11, 2011, http://top500.org/lists/2011/11/press-release.

6. Carver Mead, Analog VLSI and Neural Systems (Reading, MA: Addison-Wesley, 1986).

7. “IBM Unveils Cognitive Computing Chips,” IBM news release, August 18, 2011, http://www-03.ibm.com/press/us/en/pressrelease/35251.wss.

8. “Japan’s K Computer Tops 10 Petaflop/s to Stay Atop TOP500 List.”

1. John R. Searle, “I Married a Computer,” in Jay W. Richards, ed., Are We Spiritual Machines? Ray Kurzweil vs. the Critics of Strong AI (Seattle: Discovery Institute, 2002).

2. Stuart Hameroff, Ultimate Computing: Biomolecular Consciousness and Nanotechnology (Amsterdam: Elsevier Science, 1987).

3. P. S. Sebel et al., “The Incidence of Awareness during Anesthesia: A Multicenter United States Study,” Anesthesia and Analgesia 99 (2004): 833–39.

4. Stuart Sutherland, The International Dictionary of Psychology (New York: Macmillan, 1990).

5. David Cockburn, “Human Beings and Giant Squids,” Philosophy 69, no. 268 (April 1994): 135–50.

6. Ivan Petrovich Pavlov, from a lecture given in 1913, published in Lectures on Conditioned Reflexes: Twenty-Five Years of Objective Study of the Higher Nervous Activity [Behavior] of Animals (London: Martin Lawrence, 1928), 222.

7. Roger W. Sperry, from James Arthur Lecture on the Evolution of the Human Brain, 1964, p. 2.

8. Henry Maudsley, “The Double Brain,” Mind 14, no. 54 (1889): 161–87.

9. Susan Curtiss and Stella de Bode, “Language after Hemispherectomy,” Brain and Cogn ition 43, nos. 1–3 (June–August 2000): 135–38.

10. E. P. Vining et al., “Why Would You Remove Half a Brain? The Outcome of 58 Children after Hemispherectomy—the Johns Hopkins Experience: 1968 to 1996,” Pediatrics 100 (August 1997): 163–71. M. B. Pulsifer et al., “The Cognitive Outcome of Hemispherectomy in 71 Children,” Epilepsia 45, no. 3 (March 2004): 243–54.