How to Create a Mind: The Secret of Human Thought Revealed

“In mathematics you don’t understand things. You just get used to them. —John von Neumann”

“Finally, our new brain needs a purpose. A purpose is expressed as a series of goals. In the case of our biological brains, our goals are established by the pleasure and fear centers that we have inherited from the old brain. These primitive drives were initially set by biological evolution to foster the survival of species, but the neocortex has enabled us to sublimate them. Watson’s goal was to respond to Jeopardy! queries. Another simply stated goal could be to pass the Turing test. To do so, a digital brain would need a human narrative of its own fictional story so that it can pretend to be a biological human. It would also have to dumb itself down considerably, for any system that displayed the knowledge of, say, Watson would be quickly unmasked as nonbiological.”

“A primary reason that people believe that life is getting worse is because our information about the problems of the world has steadily improved. If there is a battle today somewhere on the planet, we experience it almost as if we were there. DuringWorld War II, tens of thousands of people might perish in a battle, and if the public could see it at all it was in a grainy newsreel in a movie theater weeks later. During World War I a small elite could read about the progress of the conflict in the newspaper(without pictures). During the nineteenth century there was almost no access to news in a timely fashion for anyone.”

“The story of evolution unfolds with increasing levels of abstraction.”

“We are a pattern that changes slowly but has stability and continuity, even though the stuff constituting the pattern changes quickly.”

“Human beings have only a weak ability to process logic, but a very deep core capability of recognizing patterns. To do logical thinking, we need to use the neocortex, which is basically a large pattern recognizer. It is not an ideal mechanism for performing logical transformations, but it is the only facility we have for the job. Compare, for example, how a human plays chess to how a typical computer chess program works. Deep Blue, the computer that defeated Garry Kasparov, the human world chess champion, in 1997 was capable of analyzing the logical implications of 200 million board positions (representing different move-countermove sequences) every second. (That can now be done, by the way, on a few personal computers.) Kasparov was asked how many positions he could analyze each second, and he said it was less than one. How is it, then, that he was able to hold up to Deep Blue at all? The answer is the very strong ability humans have to recognize patterns. However, we need to train this facility, which is why not everyone can play master chess.”

“What we found was that rather than being haphazardly arranged or independent pathways, we find that all of the pathways of the brain taken together fit together in a single exceedingly simple structure. They basically look like a cube. They basically run in three perpendicular directions, and in each one of those three directions the pathways are highly parallel to each other and arranged in arrays. So, instead of independent spaghettis, we see that the connectivity of the brain is, in a sense, a single coherent structure.”

“Electronic circuits are millions of times faster than our biological circuits. At first we will have to devote all of this speed increase to compensating for the relative lack of parallelism in our computers, but ultimately the digital neocortex will be much faster than the biological variety and will only continue to increase in speed.”

“In order for a digital neocortex to learn a new skill, it will still require many iterations of education, just as a biological neocortex does, but once a single digital neocortex somewhere and at some time learns something, it can share that knowledge with every other digital neocortex without delay. We can each have our own private neocortex extenders in the cloud, just as we have our own private stores of personal data today.”

“Our everyday “commonsense” knowledge as a human being is even greater; “street smarts” actually require substantially more of our neocortex than “book smarts.” Including this brings our estimate to well over 100 million patterns, taking into account the redundancy factor of about 100.”

“The evolution of animal behavior does constitute a learning process, but it is learning by the species, not by the individual, and the fruits of this learning process are encoded in DNA.”

“The pattern recognition theory of mind that I articulate in this book is based on a different fundamental unit: not the neuron itself, but rather an assembly of neurons, which I estimate to number around a hundred. The wiring and synaptic strengths within each unit are relatively stable and determined genetically—that is the organization within each pattern recognition module is determined by genetic design. Learning takes place in the creation of connections between these units, not within them, and probably in the synaptic strengths of the interunit connections.”

“It is important to note that the design of an entire brain region is simpler than the design of a single neuron. As discussed earlier, models often get simpler at a higher level—consider an analogy with a computer. We do need to understand the detailedphysics ofsemiconductors to model a transistor, and the equations underlying a single real transistor are complex. A digital circuit that multiples two numbers requires hundreds of them. Yet we can model this multiplication circuit very simply with one ortwo formulas. An entire computer with billions of transistors can be modeled through its instruction set and register description, which can be described on a handful of written pages of text and formulas. The software programs for an operating system,language compilers, and assemblers are reasonably complex, but modeling a particular program—for example, a speech recognition programbased on hierarchical hidden Markov modeling—may likewise be described in only a few pages ofequations. Nowhere in such a description would be found the details ofsemiconductor physics or even of computer architecture. A similar observation holds true for the brain. A particular neocortical pattern recognizer that detects a particular invariantvisualfeature (such as a face) or that performs a bandpass filtering (restricting input to a specific frequency range) on sound or that evaluates the temporal proximity of two events can be described with far fewer specific details than the actual physics andchemicalrelations controlling the neurotransmitters, ion channels, and other synaptic and dendritic variables involved in the neural processes. Although all of this complexity needs to be carefully considered before advancing to the next higher conceptual level,much of it can be simplified as the operating principles of the brain are revealed.”

“The brain is a three-pound mass you can hold in your hand that can conceive of a universe a hundred billion light years across. —Marian Diamond”

“The problem is never how to get new, innovative thoughts into your mind, but how to get old ones out.”

“philosopher Kurt Gödel reached a similar conclusion in his 1931 “incompleteness theorem.” We are thus left with the perplexing situation of being able to define a problem, to prove that a unique answer exists, and yet know that the answer can never be found.”

“Identity lies not in our genes, but in the connections between our brain cells.”

“Ever since we picked up a stick to reach a higher branch, we have used our tools to extend our reach, both physically and mentally.”

“If we were building a consciousness detector, Searle would want it to ascertain that it was squirting biological neurotransmitters. American philosopher Daniel Dennett (born in 1942) would be more flexible on substrate, but might want to determine whether or not the system contained a model of itself and of its own performance. That view comes closer to my own, but at its core is still a philosophical assumption.”

“If these biochemical phenomena sound similar to those of the fight-or-flight syndrome, they are, except that here we are running toward something or someone; indeed, a cynic might say toward rather than away from danger. The changes are also fully consistent with those of the early phases of addictive behavior. The Roxy Music song “Love Is the Drug” is quite accurate in describing this state (albeit the subject of the song is looking to score his next fix of love).”

“Shaped a little like a loaf of French country bread, our brain is a crowded chemistry lab, bustling with nonstop neural conversations. Imagine the brain, that shiny mound of being, that mouse-gray parliament of cells, that dream factory, that petit tyrant inside a ball of bone, that huddle of neurons calling all the plays, that little everywhere, that fickle pleasuredome, that wrinkled wardrobe of selves stuffed into the skull like too many clothes into a gym bag. —Diane Ackerman”

“Recall the metaphor I used in chapter 4 relating the random movements of molecules in a gas to the random movements of evolutionary change. Molecules in a gas move randomly with no apparent sense of direction. Despite this, virtually every molecule in a gas in a beaker, given sufficient time, will leave the beaker. I noted that this provides a perspective on an important question concerning the evolution of intelligence. Like molecules in a gas, evolutionary changes also move every which way with no apparent direction. Yet we nonetheless see a movement toward greater complexity and greater intelligence, indeed to evolution’s supreme achievement of evolving a neocortex capable of hierarchical thinking. So we are able to gain an insightinto how an apparently purposeless and directionless process can achieve an apparently purposeful result in one field (biological evolution) by looking at another field (thermodynamics).”

“Suppose that there be a machine, the structure of which produces thinking, feeling, and perceiving; imagine this machine enlarged but preserving the same proportions, so you could enter it as if it were a mill. This being supposed, you might visit inside; but what would you observe there? Nothing but parts which push and move each other, and never anything that could explain perception. —Gottfried Wilhelm Leibniz”

“If understanding language and other phenomena through statistical analysis does not count as true understanding, then humans have no understanding either.”

“Where some people see a divine hand, others see a multiverse spawning an evolution of universes with the boring (non-information-bearing) ones dying out. But regardless of how our universe got to be the way it is, we can start our story with a world based on information.”

“One view is that philosophy is a kind of halfway house for questions that have not yet yielded to the scientific method.”

“To appreciate its apparent complication, it is useful to zoom in on its image (which you can access via the links in this endnote).”

“Although I’m not prepared to move up my prediction of a computer passing the Turing test by 2029, the progress that has been achieved in systems like Watson should give anyone substantial confidence that the advent of Turing-level AI is close at hand. If one were to create a version of Watson that was optimized for the Turing test, it would probably come pretty close.”

“If the teacher is correct only 60 percent of the time, the student neural net will still learn its lessons with an accuracy approaching 100 percent.”

“Predicting the future is actually the primary reason that we have a brain.”