Cosmologists argue about whether the world will end in fire (a big crunch to match the big bang) or ice (the death of the stars as they spread out into an eternal expansion), but this does not take into account the power of intelligence, as if its emergence were just an entertaining sideshow to the grand celestial mechanics that now rule the universe. How long will it take for us to spread our intelligence in its nonbiological form throughout the universe? If we can transcend the speed of light-admittedly a big if-for example, by using wormholes through space (which are consistent with our current understanding of physics), it could be achieved within a few centuries. Otherwise, it will likely take much longer. In either scenario, waking up the universe, and then intelligently deciding its fate by infusing it with our human intelligence in its nonbiological form, is our destiny.
NOTES
Introduction1. Here is one sentence from Here is one sentence from One Hundred Years of Solitude One Hundred Years of Solitude by Gabriel Garcia Marquez: by Gabriel Garcia Marquez:Aureliano Segundo was not aware of the singsong until the following day after breakfast when he felt himself being bothered by a buzzing that was by then more fluid and louder than the sound of the rain, and it was Fernanda, who was walking throughout the house complaining that they had raised her to be a queen only to have her end up as a servant in a madhouse, with a lazy, idolatrous, libertine husband who lay on his back waiting for bread to rain down from heaven while she was straining her kidneys trying to keep afloat a home held together with pins where there was so much to do, so much to bear up under and repair from the time God gave his morning sunlight until it was time to go to bed that when she got there her eyes were full of ground glass, and yet no one ever said to her, "Good morning, Fernanda, did you sleep well?," nor had they asked her, even out of courtesy, why she was so pale or why she awoke with purple rings under her eyes in spite of the fact that she expected it, of course, from a family that had always considered her a nuisance, an old rag, a booby painted on the wall, and who were always going around saying things against her behind her back, calling her churchmouse, calling her Pharisee, calling her crafty, and even Amaranta, may she rest in peace, had said aloud that she was one of those people who could not tell their rectums from their ashes, God have mercy, such words, and she had tolerated everything with resignation because of the Holy Father, but she had not been able to tolerate it any more when that evil Jose Arcadio Segundo said that the damnation of the family had come when it opened its doors to a stuck-up highlander, just imagine, a bossy highlander, Lord save us, a highlander daughter of evil spit of the same stripe as the highlanders the government sent to kill workers, you tell me, and he was referring to no one but her, the godchild of the Duke of Alba, a lady of such lineage that she made the liver of presidents' wives quiver, a noble dame of fine blood like her, who had the right to sign eleven peninsular names and who was the only mortal creature in that town full of bastards who did not feel all confused at the sight of sixteen pieces of silverware, so that her adulterous husband could die of laughter afterward and say that so many knives and forks and spoons were not meant for a human being but for a centipede, and the only one who could tell with her eyes closed when the white wine was served and on what side and in which glass and when the red wine and on what side and in which glass and not like that peasant of an Amaranta, may she rest in peace, who thought that white wine was served in the daytime and red wine at night, and the only one on the whole coast who could take pride in the fact that she took care of her bodily needs only in golden chamberpots, so that Colonel Aureliano Buendia, may he rest in peace, could have the effrontery to ask her with his Masonic ill humor where she had received that privilege and whether she did not shit shit but shat sweet basil, just imagine, with those very words, and so that Renata, her own daughter, who through an oversight had seen her stool in the bedroom, had answered that even if the pot was all gold and with a coat of arms, what was inside was pure shit, physical shit, and worse even than any other kind because it was stuck-up highland shit, just imagine, her own daughter, so that she never had any illusions about the rest of the family, but in any case she had the right to expect a little more consideration from her husband because, for better or for worse, he was her consecrated spouse, her helpmate, her legal despoiler, who took upon himself of his own free and sovereign will the grave responsibility of taking her away from her paternal home, where she never wanted for or suffered from anything, where she wove funeral wreaths as a pastime, since her godfather had sent a letter with his signature and the stamp of his ring on the sealing wax simply to say that the hands of his goddaughter were not meant for tasks of this world except to play the clavichord, and, nevertheless, her insane husband had taken her from her home with all manner of admonitions and warnings and had brought her to that frying pan of hell where a person could not breathe because of the heat, and before she had completed her Pentecostal fast he had gone off with his wandering trunks and his wastrel's accordion to loaf in adultery with a wretch of whom it was only enough to see her behind, well, that's been said, to see her wiggle her mare's behind in order to guess that she was a, that she was a, just the opposite of her, who was a lady in a palace or a pigsty, at the table or in bed, a lady of breeding, God-fearing, obeying His laws and submissive to His wishes, and with whom he could not perform, naturally, the acrobatics and trampish antics that he did with the other one, who, of course, was ready for anything, like the French matrons, and even worse, if one considers well, because they at least had the honesty to put a red light at their door, swinishness like that, just imagine, and that was all that was needed by the only and beloved daughter of Dona Renata Argote and Don Fernando del Carpio, and especially the latter, an upright man, a fine Christian, a Knight of the Order of the Holy Sepulcher, those who receive direct from God the privilege of remaining intact in their graves with their skin smooth like the cheeks of a bride and their eyes alive and clear like emeralds.2. See the graph "Growth in Genbank DNA Sequence Data" in See the graph "Growth in Genbank DNA Sequence Data" in chapter 10 chapter 10.3. Cheng Zhang and Jianpeng Ma, "Enhanced Sampling and Applications in Protein Folding in Explicit Solvent," Cheng Zhang and Jianpeng Ma, "Enhanced Sampling and Applications in Protein Folding in Explicit Solvent," Journal of Chemical Physics Journal of Chemical Physics 132, no. 24 (2010): 244101. See also http://folding.stanford.edu/English/About about the Folding@home project, which has harnessed over five million computers around the world to simulate protein folding. 132, no. 24 (2010): 244101. See also http://folding.stanford.edu/English/About about the Folding@home project, which has harnessed over five million computers around the world to simulate protein folding.4. For a more complete description of this argument, see the section "[The Impact...] on the Intelligent Destiny of the Cosmos: Why We Are Probably Alone in the Universe" in chapter 6 of For a more complete description of this argument, see the section "[The Impact...] on the Intelligent Destiny of the Cosmos: Why We Are Probably Alone in the Universe" in chapter 6 of The Singularity Is Near The Singularity Is Near by Ray Kurzweil (New York: Viking, 2005). by Ray Kurzweil (New York: Viking, 2005).5. James D. Watson, James D. Watson, Discovering the Brain Discovering the Brain (Washington, DC: National Academies Press, 1992). (Washington, DC: National Academies Press, 1992).6. Sebastian Seung, Sebastian Seung, Connectome: How the Brain's Wiring Makes Us Who We Are Connectome: How the Brain's Wiring Makes Us Who We Are (New York: Houghton Mifflin Harcourt, 2012). (New York: Houghton Mifflin Harcourt, 2012).7. "Mandelbrot Zoom," http://www.youtube.com/watch?v=gEw8xpb1aRA; "Fractal Zoom Mandelbrot Corner," http://www.youtube.com/watch?v=G_GBwuYuOOs. "Mandelbrot Zoom," http://www.youtube.com/watch?v=gEw8xpb1aRA; "Fractal Zoom Mandelbrot Corner," http://www.youtube.com/watch?v=G_GBwuYuOOs.
Chapter 1: Thought Experiments on the World1. Charles Darwin, Charles Darwin, The Origin of Species The Origin of Species (P. F. Collier & Son, 1909), 185/9596. (P. F. Collier & Son, 1909), 185/9596.2. Darwin, Darwin, On the Origin of Species On the Origin of Species, 751 (206.1.1-6), Peckham's Variorum edition, edited by Morse Peckham, The Origin of Species by Charles Darwin: A Variorum Text The Origin of Species by Charles Darwin: A Variorum Text (Philadelphia: University of Pennsylvania Press, 1959). (Philadelphia: University of Pennsylvania Press, 1959).3. R. Dahm, "Discovering DNA: Friedrich Miescher and the Early Years of Nucleic Acid Research," R. Dahm, "Discovering DNA: Friedrich Miescher and the Early Years of Nucleic Acid Research," Human Genetics Human Genetics 122, no. 6 (2008): 56581, doi:10.1007/s00439-007-0433-0; PMID 17901982. 122, no. 6 (2008): 56581, doi:10.1007/s00439-007-0433-0; PMID 17901982.4. Valery N. Soyfer, "The Consequences of Political Dictatorship for Russian Science," Valery N. Soyfer, "The Consequences of Political Dictatorship for Russian Science," Nature Reviews Genetics Nature Reviews Genetics 2, no. 9 (2001): 72329, doi:10.1038/35088598; PMID 11533721. 2, no. 9 (2001): 72329, doi:10.1038/35088598; PMID 11533721.5. J. D. Watson and F. H. C. Crick, "A Structure for Deoxyribose Nucleic Acid," J. D. Watson and F. H. C. Crick, "A Structure for Deoxyribose Nucleic Acid," Nature Nature 171 (1953): 73738, http://www.nature.com/nature/dna50/watsoncrick.pdf and "Double Helix: 50 Years of DNA," 171 (1953): 73738, http://www.nature.com/nature/dna50/watsoncrick.pdf and "Double Helix: 50 Years of DNA," Nature Nature archive, http://www.nature.com/nature/dna50/archive.xhtml. archive, http://www.nature.com/nature/dna50/archive.xhtml.6. Franklin died in 1958 and the Nobel Prize for the discovery of DNA was awarded in 1962. There is controversy as to whether or not she would have shared in that prize had she been alive in 1962. Franklin died in 1958 and the Nobel Prize for the discovery of DNA was awarded in 1962. There is controversy as to whether or not she would have shared in that prize had she been alive in 1962.7. Albert Einstein, "On the Electrodynamics of Moving Bodies" (1905). This paper established the special theory of relativity. See Robert Bruce Lindsay and Henry Margenau, Albert Einstein, "On the Electrodynamics of Moving Bodies" (1905). This paper established the special theory of relativity. See Robert Bruce Lindsay and Henry Margenau, Foundations of Physics Foundations of Physics (Woodbridge, CT: Ox Bow Press, 1981), 330. (Woodbridge, CT: Ox Bow Press, 1981), 330.8. "Crookes radiometer," Wikipedia, http://en.wikipedia.org/wiki/Crookes_radiometer. "Crookes radiometer," Wikipedia, http://en.wikipedia.org/wiki/Crookes_radiometer.9. Note that some of the momentum of the photons is transferred to the air molecules in the bulb (since it is not a perfect vacuum) and then transferred from the heated air molecules to the vane. Note that some of the momentum of the photons is transferred to the air molecules in the bulb (since it is not a perfect vacuum) and then transferred from the heated air molecules to the vane.10. Albert Einstein, "Does the Inertia of a Body Depend Upon Its Energy Content?" (1905). This paper established Einstein's famous formula Albert Einstein, "Does the Inertia of a Body Depend Upon Its Energy Content?" (1905). This paper established Einstein's famous formula E E = = mc mc2.11. "Albert Einstein's Letters to President Franklin Delano Roosevelt," http://hypertextbook.com/eworld/einstein.shtml. "Albert Einstein's Letters to President Franklin Delano Roosevelt," http://hypertextbook.com/eworld/einstein.shtml.
Chapter 3: A Model of the Neocortex: The Pattern Recognition Theory of Mind1. Some nonmammals, such as crows, parrots, and octopi, are reported to be capable of some level of reasoning; however, this is limited and has not been sufficient to create tools that have their own evolutionary course of development. These animals may have adapted other brain regions to perform a small number of levels of hierarchical thinking, but a neocortex is required for the relatively unrestricted hierarchical thinking that humans can perform. Some nonmammals, such as crows, parrots, and octopi, are reported to be capable of some level of reasoning; however, this is limited and has not been sufficient to create tools that have their own evolutionary course of development. These animals may have adapted other brain regions to perform a small number of levels of hierarchical thinking, but a neocortex is required for the relatively unrestricted hierarchical thinking that humans can perform.2. V. B. Mountcastle, "An Organizing Principle for Cerebral Function: The Unit Model and the Distributed System" (1978), in Gerald M. Edelman and Vernon B. Mountcastle, V. B. Mountcastle, "An Organizing Principle for Cerebral Function: The Unit Model and the Distributed System" (1978), in Gerald M. Edelman and Vernon B. Mountcastle, The Mindful Brain: Cortical Organization and the Group-Selective Theory of Higher Brain Function The Mindful Brain: Cortical Organization and the Group-Selective Theory of Higher Brain Function (Cambridge, MA: MIT Press, 1982). (Cambridge, MA: MIT Press, 1982).3. Herbert A. Simon, "The Organization of Complex Systems," in Howard H. Pattee, ed., Herbert A. Simon, "The Organization of Complex Systems," in Howard H. Pattee, ed., Hierarchy Theory: The Challenge of Complex Systems Hierarchy Theory: The Challenge of Complex Systems (New York: George Braziller, Inc., 1973), http://blog.santafe.edu/wp-content/uploads/2009/03/simon 1973.pdf. (New York: George Braziller, Inc., 1973), http://blog.santafe.edu/wp-content/uploads/2009/03/simon 1973.pdf.4. Marc D. Hauser, Noam Chomsky, and W. Tecumseh Fitch, "The Faculty of Language: What Is It, Who Has It, and How Did It Evolve?" Marc D. Hauser, Noam Chomsky, and W. Tecumseh Fitch, "The Faculty of Language: What Is It, Who Has It, and How Did It Evolve?" Science Science 298 (November 2002): 156979, http://www.sciencemag.org/content/298/5598/1569.short. 298 (November 2002): 156979, http://www.sciencemag.org/content/298/5598/1569.short.5. The following passage from the book The following passage from the book Transcend: Nine Steps to Living Well Forever Transcend: Nine Steps to Living Well Forever, by Ray Kurzweil and Terry Grossman (New York: Rodale, 2009), describes this lucid dreaming technique in more detail:I've developed a method of solving problems while I sleep. I've perfected it for myself over several decades and have learned the subtle means by which this is likely to work better.I start out by assigning myself a problem when I get into bed. This can be any kind of problem. It could be a math problem, an issue with one of my inventions, a business strategy question, or even an interpersonal problem.I'll think about the problem for a few minutes, but I try not to solve it. That would just cut off the creative problem solving to come. I do try to think about it. What do I know about this? What form could a solution take? And then I go to sleep. Doing this primes my subconscious mind to work on the problem.Terry: Sigmund Freud pointed out that when we dream, many of the censors in our brain are relaxed, so that we might dream about things that are socially, culturally, or even sexually taboo. We can dream about weird things that we wouldn't allow ourselves to think about during the day. That's at least one reason why dreams are strange. Sigmund Freud pointed out that when we dream, many of the censors in our brain are relaxed, so that we might dream about things that are socially, culturally, or even sexually taboo. We can dream about weird things that we wouldn't allow ourselves to think about during the day. That's at least one reason why dreams are strange.Ray: There are also professional blinders that prevent people from thinking creatively, many of which come from our professional training, mental blocks such as "you can't solve a signal processing problem that way" or "linguistics is not supposed to use those rules." These mental assumptions are also relaxed in our dream state, so I'll dream about new ways of solving problems without being burdened by these daytime constraints. There are also professional blinders that prevent people from thinking creatively, many of which come from our professional training, mental blocks such as "you can't solve a signal processing problem that way" or "linguistics is not supposed to use those rules." These mental assumptions are also relaxed in our dream state, so I'll dream about new ways of solving problems without being burdened by these daytime constraints.Terry: There's another part of our brain also not working when we dream, our rational faculties to evaluate whether an idea is reasonable. So that's another reason that weird or fantastic things happen in our dreams. When the elephant walks through the wall, we aren't shocked as to how the elephant could do this. We just say to our dream selves, "Okay, an elephant walked through the wall, no big deal." Indeed, if I wake up in the middle of the night, I often find that I've been dreaming in strange and oblique ways about the problem that I assigned myself. There's another part of our brain also not working when we dream, our rational faculties to evaluate whether an idea is reasonable. So that's another reason that weird or fantastic things happen in our dreams. When the elephant walks through the wall, we aren't shocked as to how the elephant could do this. We just say to our dream selves, "Okay, an elephant walked through the wall, no big deal." Indeed, if I wake up in the middle of the night, I often find that I've been dreaming in strange and oblique ways about the problem that I assigned myself.Ray: The next step occurs in the morning in the halfway state between dreaming and being awake, which is often called The next step occurs in the morning in the halfway state between dreaming and being awake, which is often called lucid dreaming lucid dreaming. In this state, I still have the feelings and imagery from my dreams, but now I do have my rational faculties. I realize, for example, that I am in a bed. And I could formulate the rational thought that I have a lot to do so I had better get out of bed. But that would be a mistake. Whenever I can, I will stay in bed and continue in this lucid dream state because that is key to this creative problem-solving method. By the way, this doesn't work if the alarm rings.Reader: Sounds like the best of both worlds. Sounds like the best of both worlds.Ray: Exactly. I still have access to the dream thoughts about the problem I assigned myself the night before. But now I'm sufficiently conscious and rational to evaluate the new creative ideas that came to me during the night. I can determine which ones make sense. After perhaps 20 minutes of this, I invariably will have keen new insights into the problem. Exactly. I still have access to the dream thoughts about the problem I assigned myself the night before. But now I'm sufficiently conscious and rational to evaluate the new creative ideas that came to me during the night. I can determine which ones make sense. After perhaps 20 minutes of this, I invariably will have keen new insights into the problem.I've come up with inventions this way (and spent the rest of the day writing a patent application), figured out how to organize material for a book such as this, and come up with useful ideas for a diverse set of problems. If I have a key decision to make, I will always go through this process, after which I am likely to have real confidence in my decision.The key to the process is to let your mind go, to be nonjudgmental, and not to worry about how well the method is working. It is the opposite of a mental discipline. Think about the problem, but then let ideas wash over you as you fall asleep. Then in the morning, let your mind go again as you review the strange ideas that your dreams generated. I have found this to be an invaluable method for harnessing the natural creativity of my dreams.Reader: Well, for the workaholics among us, we can now work in our dreams. Not sure my spouse is going to appreciate this. Well, for the workaholics among us, we can now work in our dreams. Not sure my spouse is going to appreciate this.Ray: Actually, you can think of it as getting your dreams to do your work for you. Actually, you can think of it as getting your dreams to do your work for you.
Chapter 4: The Biological Neocortex1. Steven Pinker, Steven Pinker, How the Mind Works How the Mind Works (New York: Norton, 1997), 15253. (New York: Norton, 1997), 15253.2. D. O. Hebb, D. O. Hebb, The Organization of Behavior The Organization of Behavior (New York: John Wiley & Sons, 1949). (New York: John Wiley & Sons, 1949).3. Henry Markram and Rodrigo Perrin, "Innate Neural Assemblies for Lego Memory," Henry Markram and Rodrigo Perrin, "Innate Neural Assemblies for Lego Memory," Frontiers in Neural Circuits Frontiers in Neural Circuits 5, no. 6 (2011). 5, no. 6 (2011).4. E-mail communication from Henry Markram, February 19, 2012. E-mail communication from Henry Markram, February 19, 2012.5. Van J. Wedeen et al., "The Geometric Structure of the Brain Fiber Pathways," Van J. Wedeen et al., "The Geometric Structure of the Brain Fiber Pathways," Science Science 335, no. 6076 (March 30, 2012). 335, no. 6076 (March 30, 2012).6. Tai Sing Lee, "Computations in the Early Visual Cortex," Tai Sing Lee, "Computations in the Early Visual Cortex," Journal of Physiology-Paris Journal of Physiology-Paris 97 (2003): 12139. 97 (2003): 12139.7. A list of papers can be found at http://cbcl.mit.edu/people/poggio/tpcv_short_pubs.pdf. A list of papers can be found at http://cbcl.mit.edu/people/poggio/tpcv_short_pubs.pdf.8. Daniel J. Felleman and David C. Van Essen, "Distributed Hierarchical Processing in the Primate Cerebral Cortex," Daniel J. Felleman and David C. Van Essen, "Distributed Hierarchical Processing in the Primate Cerebral Cortex," Cerebral Cortex Cerebral Cortex 1, no. 1 (January/February 1991): 147. A compelling analysis of the Bayesian mathematics of the top-down and bottom-up communication in the neocortex is provided by Tai Sing Lee in "Hierarchical Bayesian Inference in the Visual Cortex," 1, no. 1 (January/February 1991): 147. A compelling analysis of the Bayesian mathematics of the top-down and bottom-up communication in the neocortex is provided by Tai Sing Lee in "Hierarchical Bayesian Inference in the Visual Cortex," Journal of the Optical Society of America Journal of the Optical Society of America 20, no. 7 (July 2003): 143448. 20, no. 7 (July 2003): 143448.9. Uri Hasson et al., "A Hierarchy of Temporal Receptive Windows in Human Cortex," Uri Hasson et al., "A Hierarchy of Temporal Receptive Windows in Human Cortex," Journal of Neuroscience Journal of Neuroscience 28, no. 10 (March 5, 2008): 253950. 28, no. 10 (March 5, 2008): 253950.10. Marina Bedny et al., "Language Processing in the Occipital Cortex of Congenitally Blind Adults," Marina Bedny et al., "Language Processing in the Occipital Cortex of Congenitally Blind Adults," Proceedings of the National Academy of Sciences Proceedings of the National Academy of Sciences 108, no. 11 (March 15, 2011): 442934. 108, no. 11 (March 15, 2011): 442934.11. Daniel E. Feldman, "Synaptic Mechanisms for Plasticity in Neocortex," Daniel E. Feldman, "Synaptic Mechanisms for Plasticity in Neocortex," Annual Review of Neuroscience Annual Review of Neuroscience 32 (2009): 3355. 32 (2009): 3355.12. Aaron C. Koralek et al., "Corticostriatal Plasticity Is Necessary for Learning Intentional Neuroprosthetic Skills," Aaron C. Koralek et al., "Corticostriatal Plasticity Is Necessary for Learning Intentional Neuroprosthetic Skills," Nature Nature 483 (March 15, 2012): 33135. 483 (March 15, 2012): 33135.13. E-mail communication from Randal Koene, January 2012. E-mail communication from Randal Koene, January 2012.14. Min Fu, Xinzhu Yu, Ju Lu, and Yi Zuo, "Repetitive Motor Learning Induces Coordinated Formation of Clustered Dendritic Spines Min Fu, Xinzhu Yu, Ju Lu, and Yi Zuo, "Repetitive Motor Learning Induces Coordinated Formation of Clustered Dendritic Spines in Vivo in Vivo," Nature Nature 483 (March 1, 2012): 9295. 483 (March 1, 2012): 9295.15. Dario Bonanomi et al., "Ret Is a Multifunctional Coreceptor That Integrates Diffusible- and Contact-Axon Guidance Signals," Dario Bonanomi et al., "Ret Is a Multifunctional Coreceptor That Integrates Diffusible- and Contact-Axon Guidance Signals," Cell Cell 148, no. 3 (February 2012): 56882. 148, no. 3 (February 2012): 56882.16. See See endnote 7 in chapter 11 endnote 7 in chapter 11.
Chapter 5: The Old Brain1. Vernon B. Mountcastle, "The View from Within: Pathways to the Study of Perception," Vernon B. Mountcastle, "The View from Within: Pathways to the Study of Perception," Johns Hopkins Medical Journal Johns Hopkins Medical Journal 136 (1975): 10931. 136 (1975): 10931.2. B. Roska and F. Werblin, "Vertical Interactions Across Ten Parallel, Stacked Representations in the Mammalian Retina," B. Roska and F. Werblin, "Vertical Interactions Across Ten Parallel, Stacked Representations in the Mammalian Retina," Nature Nature 410, no. 6828 (March 29, 2001): 58387; "Eye Strips Images of All but Bare Essentials Before Sending Visual Information to Brain, UC Berkeley Research Shows," University of California at Berkeley news release, March 28, 2001, www.berkeley.edu/news/media/releases/2001/03/28_wers1.xhtml. 410, no. 6828 (March 29, 2001): 58387; "Eye Strips Images of All but Bare Essentials Before Sending Visual Information to Brain, UC Berkeley Research Shows," University of California at Berkeley news release, March 28, 2001, www.berkeley.edu/news/media/releases/2001/03/28_wers1.xhtml.3. Lloyd Watts, "Reverse-Engineering the Human Auditory Pathway," in J. Liu et al., eds., Lloyd Watts, "Reverse-Engineering the Human Auditory Pathway," in J. Liu et al., eds., WCCI 2012 WCCI 2012 (Berlin: Springer-Verlag, 2012), 4759. Lloyd Watts, "Real-Time, High-Resolution Simulation of the Auditory Pathway, with Application to Cell-Phone Noise Reduction," (Berlin: Springer-Verlag, 2012), 4759. Lloyd Watts, "Real-Time, High-Resolution Simulation of the Auditory Pathway, with Application to Cell-Phone Noise Reduction," ISCAS ISCAS (June 2, 2010): 382124. For other papers see http://www.lloydwatts.com/publications.xhtml. (June 2, 2010): 382124. For other papers see http://www.lloydwatts.com/publications.xhtml.4. See Sandra Blakeslee, "Humanity? Maybe It's All in the Wiring," See Sandra Blakeslee, "Humanity? Maybe It's All in the Wiring," New York New York Times Times, December 11, 2003, http://www.nytimes.com/2003/12/09/science/09BRAI.xhtml.5. T. E. J. Behrens et al., "Non-Invasive Mapping of Connections between Human Thalamus and Cortex Using Diffusion Imaging," T. E. J. Behrens et al., "Non-Invasive Mapping of Connections between Human Thalamus and Cortex Using Diffusion Imaging," Nature Neuroscience Nature Neuroscience 6, no. 7 (July 2003): 75057. 6, no. 7 (July 2003): 75057.6. Timothy J. Buschman et al., "Neural Substrates of Cognitive Capacity Limitations," Timothy J. Buschman et al., "Neural Substrates of Cognitive Capacity Limitations," Proceedings of the National Academy of Sciences Proceedings of the National Academy of Sciences 108, no. 27 (July 5, 2011): 1125255, http://www.pnas.org/content/108/27/11252.long. 108, no. 27 (July 5, 2011): 1125255, http://www.pnas.org/content/108/27/11252.long.7. Theodore W. Berger et al., "A Cortical Neural Prosthesis for Restoring and Enhancing Memory," Theodore W. Berger et al., "A Cortical Neural Prosthesis for Restoring and Enhancing Memory," Journal of Neural Engineering Journal of Neural Engineering 8, no. 4 (August 2011). 8, no. 4 (August 2011).8. Basis functions are nonlinear functions that can be combined linearly (by adding together multiple weighted-basis functions) to approximate any nonlinear function. A. Pouget and L. H. Snyder, "Computational Approaches to Sensorimotor Transformations," Basis functions are nonlinear functions that can be combined linearly (by adding together multiple weighted-basis functions) to approximate any nonlinear function. A. Pouget and L. H. Snyder, "Computational Approaches to Sensorimotor Transformations," Nature Neuroscience Nature Neuroscience 3, no. 11 Supplement (November 2000): 119298. 3, no. 11 Supplement (November 2000): 119298.9. J. R. Bloedel, "Functional Heterogeneity with Structural Homogeneity: How Does the Cerebellum Operate?" J. R. Bloedel, "Functional Heterogeneity with Structural Homogeneity: How Does the Cerebellum Operate?" Behavioral and Brain Sciences Behavioral and Brain Sciences 15, no. 4 (1992): 66678. 15, no. 4 (1992): 66678.10. S. Grossberg and R. W. Paine, "A Neural Model of Cortico-Cerebellar Interactions during Attentive Imitation and Predictive Learning of Sequential Handwriting Movements," S. Grossberg and R. W. Paine, "A Neural Model of Cortico-Cerebellar Interactions during Attentive Imitation and Predictive Learning of Sequential Handwriting Movements," Neural Networks Neural Networks 13, no. 89 (OctoberNovember 2000): 9991046. 13, no. 89 (OctoberNovember 2000): 9991046.11. Javier F. Medina and Michael D. Mauk, "Computer Simulation of Cerebellar Information Processing," Javier F. Medina and Michael D. Mauk, "Computer Simulation of Cerebellar Information Processing," Nature Neuroscience Nature Neuroscience 3 (November 2000): 120511. 3 (November 2000): 120511.12. James Olds, "Pleasure Centers in the Brain," James Olds, "Pleasure Centers in the Brain," Scientific American Scientific American (October 1956): 10516. Aryeh Routtenberg, "The Reward System of the Brain," (October 1956): 10516. Aryeh Routtenberg, "The Reward System of the Brain," Scientific American Scientific American 239 (November 1978): 15464. K. C. Berridge and M. L. Kringelbach, "Affective Neuroscience of Pleasure: Reward in Humans and Other Animals," 239 (November 1978): 15464. K. C. Berridge and M. L. Kringelbach, "Affective Neuroscience of Pleasure: Reward in Humans and Other Animals," Psychopharmacology Psychopharmacology 199 (2008): 45780. Morten L. Kringelbach, 199 (2008): 45780. Morten L. Kringelbach, The Pleasure Center: Trust Your Animal Instincts The Pleasure Center: Trust Your Animal Instincts (New York: Oxford University Press, 2009). Michael R. Liebowitz, (New York: Oxford University Press, 2009). Michael R. Liebowitz, The Chemistry of Love The Chemistry of Love (Boston: Little, Brown, 1983). W. L. Witters and P. Jones-Witters, (Boston: Little, Brown, 1983). W. L. Witters and P. Jones-Witters, Human Sexuality: A Biological Perspective Human Sexuality: A Biological Perspective (New York: Van Nostrand, 1980). (New York: Van Nostrand, 1980).
Chapter 6: Transcendent Abilities1. Michael Nielsen, Michael Nielsen, Reinventing Discovery: The New Era of Networked Science Reinventing Discovery: The New Era of Networked Science (Princeton, NJ: Princeton University Press, 2012), 13. T. Gowers and M. Nielsen, "Massively Collaborative Mathematics," (Princeton, NJ: Princeton University Press, 2012), 13. T. Gowers and M. Nielsen, "Massively Collaborative Mathematics," Nature Nature 461, no. 7266 (2009): 87981. "A Combinatorial Approach to Density Hales-Jewett," 461, no. 7266 (2009): 87981. "A Combinatorial Approach to Density Hales-Jewett," Gowers's Weblog Gowers's Weblog, http://gowers.wordpress.com/2009/02/01/a-combinatorial-approach-to-density-hales-jewett/. Michael Nielsen, "The Polymath Project: Scope of Participation," March 20, 2009, http://michaelnielsen.org/blog/?p=584. Julie Rehmeyer, "SIAM: Massively Collaborative Mathematics," Society for Industrial and Applied Mathematics, April 1, 2010, http://www.siam.org/news/news.php?id=1731.2. P. Dayan and Q. J. M. Huys, "Serotonin, Inhibition, and Negative Mood," P. Dayan and Q. J. M. Huys, "Serotonin, Inhibition, and Negative Mood," PLoS PLoS Computational Biology Computational Biology 4, no. 1 (2008), http://compbiol.plosjournals.org/perlserv/?request=get-document&doi=10.1371/journal.pcbi.0040004. 4, no. 1 (2008), http://compbiol.plosjournals.org/perlserv/?request=get-document&doi=10.1371/journal.pcbi.0040004.
Chapter 7: The Biologically Inspired Digital Neocortex1. Gary Cziko, Gary Cziko, Without Miracles: Universal Selection Theory and the Second Darwinian Revolution Without Miracles: Universal Selection Theory and the Second Darwinian Revolution (Cambridge, MA: MIT Press, 1955). (Cambridge, MA: MIT Press, 1955).2. David Dalrymple has been a mentee of mine since he was eight years old in 1999. You can read his background here: http://esp.mit.edu/learn/teachers/davidad/bio.xhtml, and http://www.brainsciences.org/Research-Team/mr-david-dalrymple.xhtml. David Dalrymple has been a mentee of mine since he was eight years old in 1999. You can read his background here: http://esp.mit.edu/learn/teachers/davidad/bio.xhtml, and http://www.brainsciences.org/Research-Team/mr-david-dalrymple.xhtml.3. Jonathan Fildes, "Artificial Brain '10 Years Away,'" BBC News, July 22, 2009, http://news.bbc.co.uk/2/hi/8164060.stm. See also the video "Henry Markram on Simulating the Brain: The Next Decisive Years," http://www.kurzweilai.net/henry-markram-simulating-the-brain-next-decisive-years. Jonathan Fildes, "Artificial Brain '10 Years Away,'" BBC News, July 22, 2009, http://news.bbc.co.uk/2/hi/8164060.stm. See also the video "Henry Markram on Simulating the Brain: The Next Decisive Years," http://www.kurzweilai.net/henry-markram-simulating-the-brain-next-decisive-years.4. M. Mitchell Waldrop, "Computer Modelling: Brain in a Box," M. Mitchell Waldrop, "Computer Modelling: Brain in a Box," Nature News Nature News, February 22, 2012, http://www.nature.com/news/computer-modelling-brain-in-a-box-1.10066.5. Jonah Lehrer, "Can a Thinking, Remembering, Decision-Making Biologically Accurate Brain Be Built from a Supercomputer?" Jonah Lehrer, "Can a Thinking, Remembering, Decision-Making Biologically Accurate Brain Be Built from a Supercomputer?" Seed Seed, http://seedmagazine.com/content/article/out_of_the_blue/.6. Fildes, "Artificial Brain '10 Years Away.'" Fildes, "Artificial Brain '10 Years Away.'"7. See http://www.humanconnectomeproject.org/. See http://www.humanconnectomeproject.org/.8. Anders Sandberg and Nick Bostrom, Anders Sandberg and Nick Bostrom, Whole Brain Emulation: A Roadmap Whole Brain Emulation: A Roadmap, Technical Report #20083 (2008), Future of Humanity Institute, Oxford University, www.fhi.ox.ac.uk/reports/20083.pdf.9. Here is the basic schema for a neural net algorithm. Many variations are possible, and the designer of the system needs to provide certain critical parameters and methods, detailed on the following pages. Here is the basic schema for a neural net algorithm. Many variations are possible, and the designer of the system needs to provide certain critical parameters and methods, detailed on the following pages.Creating a neural net solution to a problem involves the following steps:Define the input.
Define the topology of the neural net (i.e., the layers of neurons and the connections between the neurons).
Train the neural net on examples of the problem.
Run the trained neural net to solve new examples of the problem.
Take your neural net company public.
These steps (except for the last one) are detailed below:The Problem InputThe problem input to the neural net consists of a series of numbers. This input can be:In a visual pattern recognition system, a two-dimensional array of numbers representing the pixels of an image; or In an auditory (e.g., speech) recognition system, a two-dimensional array of numbers representing a sound, in which the first dimension represents parameters of the sound (e.g., frequency components) and the second dimension represents different points in time; or In an arbitrary pattern recognition system, an n n-dimensional array of numbers representing the input pattern.
Defining the TopologyTo set up the neural net, the architecture of each neuron consists of:Multiple inputs in which each input is "connected" to either the output of another neuron or one of the input numbers.
Generally, a single output, which is connected to either the input of another neuron (which is usually in a higher layer) or the final output.
Set Up the First Layer of NeuronsCreate N N0 neurons in the first layer. For each of these neurons, "connect" each of the multiple inputs of the neuron to "points" (i.e., numbers) in the problem input. These connections can be determined randomly or using an evolutionary algorithm (see below). neurons in the first layer. For each of these neurons, "connect" each of the multiple inputs of the neuron to "points" (i.e., numbers) in the problem input. These connections can be determined randomly or using an evolutionary algorithm (see below).
Assign an initial "synaptic strength" to each connection created. These weights can start out all the same, can be assigned randomly, or can be determined in another way (see below).
Set Up the Additional Layers of NeuronsSet up a total of M M layers of neurons. For each layer, set up the neurons in that layer. layers of neurons. For each layer, set up the neurons in that layer.For layeri:Create N Ni neurons in layer neurons in layeri. For each of these neurons, "connect" each of the multiple inputs of the neuron to the outputs of the neurons in layeri1 (see variations below). (see variations below).
Assign an initial "synaptic strength" to each connection created. These weights can start out all the same, can be assigned randomly, or can be determined in another way (see below).
The outputs of the neurons in layerM are the outputs of the neural net (see variations below). are the outputs of the neural net (see variations below).
The Recognition TrialsHow Each Neuron WorksOnce the neuron is set up, it does the following for each recognition trial: Each weighted input to the neuron is computed by multiplying the output of the other neuron (or initial input) that the input to this neuron is connected to by the synaptic strength of that connection.
All of these weighted inputs to the neuron are summed.
If this sum is greater than the firing threshold of this neuron, then this neuron is considered to fire and its output is 1. Otherwise, its output is 0 (see variations below).
Do the Following for Each Recognition TrialFor each layer, from layer0 to layer to layerM:For each neuron in the layer:Sum its weighted inputs (each weighted input = the output of the other neuron [or initial input] that the input to this neuron is connected to, multiplied by the synaptic strength of that connection).
If this sum of weighted inputs is greater than the firing threshold for this neuron, set the output of this neuron = 1, otherwise set it to 0.
To Train the Neural NetRun repeated recognition trials on sample problems.
After each trial, adjust the synaptic strengths of all the interneuronal connections to improve the performance of the neural net on this trial (see the discussion below on how to do this).
Continue this training until the accuracy rate of the neural net is no longer improving (i.e., reaches an asymptote).
Key Design DecisionsIn the simple schema above, the designer of this neural net algorithm needs to determine at the outset:What the input numbers represent.
The number of layers of neurons.
The number of neurons in each layer. (Each layer does not necessarily need to have the same number of neurons.) The number of inputs to each neuron in each layer. The number of inputs (i.e., interneuronal connections) can also vary from neuron to neuron and from layer to layer.
The actual "wiring" (i.e., the connections). For each neuron in each layer, this consists of a list of other neurons, the outputs of which constitute the inputs to this neuron. This represents a key design area. There are a number of possible ways to do this: (1) Wire the neural net randomly; or (2) Use an evolutionary algorithm (see below) to determine an optimal wiring; or (3) Use the system designer's best judgment in determining the wiring.
The initial synaptic strengths (i.e., weights) of each connection. There are a number of possible ways to do this: (1) Set the synaptic strengths to the same value; or (2) Set the synaptic strengths to different random values; or (3) Use an evolutionary algorithm to determine an optimal set of initial values; or (4) Use the system designer's best judgment in determining the initial values.
The firing threshold of each neuron.
Determine the output. The output can be: (1) the outputs of layerM of neurons; or of neurons; or (2) the output of a single output neuron, the inputs of which are the outputs of the neurons in layerM; or (3) a function of (e.g., a sum of) the outputs of the neurons in layerM; or (4) another function of neuron outputs in multiple layers.
Determine how the synaptic strengths of all the connections are adjusted during the training of this neural net. This is a key design decision and is the subject of a great deal of research and discussion. There are a number of possible ways to do this: (1) For each recognition trial, increment or decrement each synaptic strength by a (generally small) fixed amount so that the neural net's output more closely matches the correct answer. One way to do this is to try both incrementing and decrementing and see which has the more desirable effect. This can be time-consuming, so other methods exist for making local decisions on whether to increment or decrement each synaptic strength.
(2) Other statistical methods exist for modifying the synaptic strengths after each recognition trial so that the performance of the neural net on that trial more closely matches the correct answer.
Note that neural net training will work even if the answers to the training trials are not all correct. This allows using real-world training data that may have an inherent error rate. One key to the success of a neural netbased recognition system is the amount of data used for training. Usually a very substantial amount is needed to obtain satisfactory results. As with human students, the amount of time that a neural net spends learning its lessons is a key factor in its performance.
VariationsMany variations of the above are feasible. For example:There are different ways of determining the topology. In particular, the interneuronal wiring can be set either randomly or using an evolutionary algorithm.
There are different ways of setting the initial synaptic strengths.
The inputs to the neurons in layeri do not necessarily need to come from the outputs of the neurons in layer do not necessarily need to come from the outputs of the neurons in layeri1. Alternatively, the inputs to the neurons in each layer can come from any lower layer or any layer.
There are different ways to determine the final output.
The method described above results in an "all or nothing" (1 or 0) firing called a nonlinearity. There are other nonlinear functions that can be used. Commonly a function is used that goes from 0 to 1 in a rapid but more gradual fashion. Also, the outputs can be numbers other than 0 and 1.
The different methods for adjusting the synaptic strengths during training represent key design decisions.
The above schema describes a "synchronous" neural net, in which each recognition trial proceeds by computing the outputs of each layer, starting with layer0 through layer through layerM. In a true parallel system, in which each neuron is operating independently of the others, the neurons can operate "asynchronously" (i.e., independently). In an asynchronous approach, each neuron is constantly scanning its inputs and fires whenever the sum of its weighted inputs exceeds its threshold (or whatever its output function specifies).
10. Robert Mannell, "Acoustic Representations of Speech," 2008, http://clas.mq.edu.au/acoustics/frequency/acoustic_speech.xhtml. 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. 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 AlgorithmCreate N N solution "creatures." Each one has: 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 N solution creatures: 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 L solution creatures with the highest ratings to survive into the next generation. solution creatures with the highest ratings to survive into the next generation.
Eliminate the (N L L) nonsurviving solution creatures.
Create (N L L) new solution creatures from the L L surviving solution creatures by: surviving solution creatures by: (1) Making copies of the L L surviving creatures. Introduce small random variations into each copy; or 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 L surviving creatures; or 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 DecisionsIn 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 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. 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.
VariationsMany 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 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). 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," A. M. Turing, "Computing Machinery and Intelligence," Mind 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. 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. "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. Dragon Go! Nuance Communications, Inc., http://www.nuance.com/products/dragon-go-in-action/index.htm.17. "Overcoming Artificial Stupidity," "Overcoming Artificial Stupidity," WolframAlpha Blog WolframAlpha Blog, April 17, 2012, http://blog.wolframalpha.com/author/stephenwolfram/.
Chapter 8: The Mind as Computer1. Salomon Bochner, Salomon Bochner, A Biographical Memoir of John von Neumann A Biographical Memoir of John von Neumann (Washington, DC: National Academy of Sciences, 1958). (Washington, DC: National Academy of Sciences, 1958).2. A. M. Turing, "On Computable Numbers, with an Application to the Entscheidungsproblem," A. M. Turing, "On Computable Numbers, with an Application to the Entscheidungsproblem," Proceedings of the London Mathematical Society Proceedings of the London Mathematical Society Series 2, vol. 42 (193637): 23065, 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," Series 2, vol. 42 (193637): 23065, 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 Proceedings of the London Mathematical Society 43 (1938): 54446. 43 (1938): 54446.3. 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," 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 Bell System Technical Journal, July and October 1948.4. Jeremy Bernstein, Jeremy Bernstein, The Analytical Engine: Computers-Past, Present, and Future 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," "Japan's K Computer Tops 10 Petaflop/s to Stay Atop TOP500 List," Top 500 Top 500, November 11, 2011, http://top500.org/lists/2011/11/press-release.6. Carver Mead, Carver Mead, Analog VLSI and Neural Systems Analog VLSI and Neural Systems (Reading, MA: Addison-Wesley, 1986). (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. "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." "Japan's K Computer Tops 10 Petaflop/s to Stay Atop TOP500 List."
Chapter 9: Thought Experiments on the Mind1. John R. Searle, "I Married a Computer," in Jay W. Richards, ed., John R. Searle, "I Married a Computer," in Jay W. Richards, ed., Are We Spiritual Machines? Ray Kurzweil vs. the Critics of Strong AI Are We Spiritual Machines? Ray Kurzweil vs. the Critics of Strong AI (Seattle: Discovery Institute, 2002). (Seattle: Discovery Institute, 2002).2. Stuart Hameroff, Stuart Hameroff, Ultimate Computing: Biomolecular Consciousness and Nanotechnology Ultimate Computing: Biomolecular Consciousness and Nanotechnology (Amsterdam: Elsevier Science, 1987). (Amsterdam: Elsevier Science, 1987).3. P. S. Sebel et al., "The Incidence of Awareness during Anesthesia: A Multicenter United States Study," P. S. Sebel et al., "The Incidence of Awareness during Anesthesia: A Multicenter United States Study," Anesthesia and Analgesia Anesthesia and Analgesia 99 (2004): 83339. 99 (2004): 83339.4. Stuart Sutherland, Stuart Sutherland, The International Dictionary of Psychology The International Dictionary of Psychology (New York: Macmillan, 1990). (New York: Macmillan, 1990).5. David Cockburn, "Human Beings and Giant Squids," David Cockburn, "Human Beings and Giant Squids," Philosophy Philosophy 69, no. 268 (April 1994): 13550. 69, no. 268 (April 1994): 13550.6. Ivan Petrovich Pavlov, from a lecture given in 1913, published in 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 Lectures on Conditioned Reflexes: Twenty-Five Years of Objective Study of the Higher Nervous Activity [Behavior] of Animals (London: Martin Lawrence, 1928), 222. (London: Martin Lawrence, 1928), 222.7. Roger W. Sperry, from James Arthur Lecture on the Evolution of the Human Brain, 1964, p. 2. Roger W. Sperry, from James Arthur Lecture on the Evolution of the Human Brain, 1964, p. 2.8. Henry Maudsley, "The Double Brain," Henry Maudsley, "The Double Brain," Mind Mind 14, no. 54 (1889): 16187. 14, no. 54 (1889): 16187.9. Susan Curtiss and Stella de Bode, "Language after Hemispherectomy," Susan Curtiss and Stella de Bode, "Language after Hemispherectomy," Brain and Cogn Brain and Cognition 43, nos. 13 (JuneAugust 2000): 13538.10. 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): 16371. M. B. Pulsifer et al., "The Cognitive Outcome of Hemispherectomy in 71 Children," 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): 16371. M. B. Pulsifer et al., "The Cognitive Outcome of Hemispherectomy in 71 Children," Epilepsia Epilepsia 45, no. 3 (March 2004): 24354. 45, no. 3 (March 2004): 24354.11. S. McClelland III and R. E. Maxwell, "Hemispherectomy for Intractable Epilepsy in Adults: The First Reported Series," S. McClelland III and R. E. Maxwell, "Hemispherectomy for Intractable Epilepsy in Adults: The First Reported Series," Annals of Neurology Annals of Neurology 61, no. 4 (April 2007): 37276. 61, no. 4 (April 2007): 37276.12. Lars Muckli, Marcus J. Naumerd, and Wolf Singer, "Bilateral Visual Field Maps in a Patient with Only One Hemisphere," Lars Muckli, Marcus J. Naumerd, and Wolf Singer, "Bilateral Visual Field Maps in a Patient with Only One Hemisphere," Proceedings of the National Academy of Sciences Proceedings of the National Academy of Sciences 106, no. 31 (August 4, 2009), http://dx.doi.org/10.1073/pnas.0809688106. 106, no. 31 (August 4, 2009), http://dx.doi.org/10.1073/pnas.0809688106.13. Marvin Minsky, Marvin Minsky, The Society of Mind The Society of Mind (New York: Simon and Schuster, 1988). (New York: Simon and Schuster, 1988).14. F. Fay Evans-Martin, F. Fay Evans-Martin, The Nervous System The Nervous System (New York: Chelsea House, 2005), http://www.scribd.com/doc/5012597/The-Nervous-System. (New York: Chelsea House, 2005), http://www.scribd.com/doc/5012597/The-Nervous-System.15. Benjamin Libet, Benjamin Libet, Mind Time: The Temporal Factor in Consciousness Mind Time: The Temporal Factor in Consciousness (Cambridge, MA: Harvard University Press, 2005). (Cambridge, MA: Harvard University Press, 2005).16. Daniel C. Dennett, Daniel C. Dennett, Freedom Evolves Freedom Evolves (New York: Viking, 2003). (New York: Viking, 2003).17. Michael S. Gazzaniga, Michael S. Gazzaniga, Who's in Charge? Free Will and the Science of the Brain Who's in Charge? Free Will and the Science of the Brain (New York: Ecco/HarperCollins, 2011). (New York: Ecco/HarperCollins, 2011).18. David Hume, David Hume, An Enquiry Concerning Human Understanding An Enquiry Concerning Human Understanding (1765), 2nd ed., edited by Eric Steinberg (Indianapolis: Hackett, 1993). (1765), 2nd ed., edited by Eric Steinberg (Indianapolis: Hackett, 1993).19. Arthur Schopenhauer, Arthur Schopenhauer, The Wisdom of Life The Wisdom of Life.20. Arthur Schopenhauer, Arthur Schopenhauer, On the Freedom of the Will On the Freedom of the Will (1839). (1839).21. From Raymond Smullyan, From Raymond Smullyan, 5000 B.C. and Other Philosophical Fantasies 5000 B.C. and Other Philosophical Fantasies (New York: St. Martin's Press, 1983). (New York: St. Martin's Press, 1983).22. For an insightful and entertaining examination of similar issues of identity and consciousness, see Martine Rothblatt, "The Terasem Mind Uploading Experiment," For an insightful and entertaining examination of similar issues of identity and consciousness, see Martine Rothblatt, "The Terasem Mind Uploading Experiment," International Journal of Machine Consciousness International Journal of Machine Consciousness 4, no. 1 (2012): 14158. In this paper, Rothblatt examines the issue of identity with regard to software that emulates a person based on "a database of video interviews and associated information about a predecessor person." In this proposed future experiment, the software is successfully emulating the person it is based on. 4, no. 1 (2012): 14158. In this paper, Rothblatt examines the issue of identity with regard to software that emulates a person based on "a database of video interviews and associated information about a predecessor person." In this proposed future experiment, the software is successfully emulating the person it is based on.23. "How Do You Persist When Your Molecules Don't?" "How Do You Persist When Your Molecules Don't?" Science and Consciousness Review Science and Consciousness Review 1, no. 1 (June 2004), http://www.sci-con.org/articles/20040601.xhtml. 1, no. 1 (June 2004), http://www.sci-con.org/articles/20040601.xhtml.