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Do we really have free will or do we just imagine we do? Do we create our own destinies, or are we merely machines? Will the machines we are now making themselves have free will? These are the fundamental questions of The Quantum Brain. To answer them, psychiatrist, researcher, and critically acclaimed author ...
Do we really have free will or do we just imagine we do? Do we create our own destinies, or are we merely machines? Will the machines we are now making themselves have free will? These are the fundamental questions of The Quantum Brain. To answer them, psychiatrist, researcher, and critically acclaimed author Jeffrey Satinover first explores the latest discoveries in neuroscience, modern physics, and radically new kinds of computing, then shows how, together, they suggest the brain embodies and amplifies the mysterious laws of quantum physics. By its doing so, Satinover argues we are elevated above the mere learning machines modern science assumes us to be. Satinover also makes two provocative predictions: We will soon construct artificial devices as free and aware as we are; as well as begin a startling re-evaluation of just who and what we are, of our place in the universe, and perhaps even of God.
Pascal was startled. "How do you know that?" Telescopes did indeed invert images, but how would his mother know what the proper phase should look like, having never had her hands on a telescope before-indeed, how could she know that Venus had a phase at all? The phases of Venus were only discovered once telescopes were available.
"Because I can also see it under my naked eye."
Pascal had no particular response to this assertion. He certainly could not see Venus as anything more than a large point of light.
Was her vision better? Was she more observant? Three hundred years later we would find the answer. It was not her vision, it was the human retina. The retina is not really a "part" of the eye at all. It is rather a physical extrusion of the fourth brain itself into the eye. And like other brain matter, the retina therefore is organized not merely to gather light but to intelligently process visual data. It is a vastly powerful, sophisticated, and complex pattern discriminator and classifier. Insofar as one may refer to "machines that think," the retina is an enormously subtle thinking machine-a biological computer of great power and beauty, packed into an impressively small size. Imagine, if you will, a desktop computer about the size and thickness and delicacy of a rosepetal. And what limitations it has are now understood to lie far below the threshold of acuity needed to detect the phases of Venus with the unaided human eye-perhaps with the unaided eye of any terrestrial creature.
For the answer to Pascal's query we can largely credit the efforts of one flamboyant scientist, Frank Rosenblatt. Rosenblatt was a pioneer of neural computation-the construction of electronic devices that process information not according to "top-down" rules of logic but by mimicry of the "bottom-up" wanderings of nature. His prime device-the Perceptron-today sits in the Smithsonian, next to the von Neumann computer that made possible the atom bomb. Of the significance of the latter, very many are at least dimly aware: Every PC in the world was fathered by it; but of the former, very few. Indeed, there remains considerable irritation in some quarters that the Perceptron is there at all. But it is the father of the computer of the future, and it was modeled on the human retina.
Rosenblatt had developed a fierce and precocious fascination with the quest for machine-based intelligence, just then beginning, in the late 1940s. It seemed obvious, in those days, that "artificial intelligence" could best be developed by studying and copying natural intelligence. But the task proved more difficult than anticipated. Within twenty years, the burgeoning science of computation would abandon the biological template to develop the kind of machines that today sit on almost every desktop.
Like many young scientists to follow, Rosenblatt's passionate quest for biologically informed computation was fueled by a fantastically clever and influential article written by two pioneers at the Massachusetts Institute of Technology. In 1943 Warren McCulloch and Walter Pitts published a piece in the Bulletin of Mathematical Biophysics entitled "A Logical Calculus of Ideas Immanent in Nervous Tissue." With mathematical certainty, the article showed that a collection of nerve cells was not only capable of computing, but given how individual neurons behaved, and how they were connected to each other-with a lot of randomness-they would necessarily compute.
In particular, they showed that the "ideas" embodied in a collection of neurons were not explicit, as in high-level human languages, but implicit ("immanent"), carried by the collection of neurons as a whole in much the same way that in matchbox Hexapawn, no individual matchbox embodies the "idea" of the game, but, once trained, the entire assemblage of matchboxes does.' McCulloch and Pitts's paper is little known to the world at large; to the computational science community it is universally known and admired, and has been credited with spurring the entire computer revolution. But the first use to which it was put was in creating a neural network: a densely interconnected set of elementary processing elements that, as a whole, could spontaneously develop powerful intelligence.
Fifteen years after McCulloch and Pitts's seminal paper, Frank Rosenblatt created the Perceptron, a neural network based on the retina. By repeatedly processing information in network fashion-also called distributed or massively parallel processing-a group of even relatively simple neurons can acquire a fantastic capacity for discrimination (as the HER matchboxes acquired strategic ability). This is why the naked eye can in fact detect the crescent shape of Venus.
The elements in a neural network, and the neurons in the retina, operate roughly like this. An incoming signal (say, the local intensity of light) is stimulated by a detector neuron. It transforms the intensity into an electrical signal of a corresponding strength, which it then distributes to many other neurons. There are many such detector neurons, and they all distribute their individualized output to the many other neurons. Each adds up its inputs and similarly converts the net result into a corresponding output. In short, each of many neurons receives many different inputs from which each synthesizes a single output to distribute to many others-hence, "massively parallel."
If that's all there were to it, nothing would happen: Such a system couldn't learn. But the network of neurons has an additional mechanism that is the equivalent of HER jellybeans. The connections between neurons are themselves of varying strength (usually called "weight"): Depending on their weights, the connections either enhance the signal they are transmitting or diminish it. Since at the beginning these weights differ at random, neural nets initially scramble any incoming signal and put out noise. But in a living nervous system, the system itself modifies the weights in light of experience: Connections that frequently carry signals, especially strong ones, are themselves strengthened; connections that infrequently carry signals, or mostly weak ones, are themselves weakened, a mechanism first outlined in 1949 by neuropsychologist Donald O. Hebb in what has become a landmark book, The Organization of Behavior: A Neuropsychological Theory.'
It's almost like a statistical reasoning process: "Hmm. It seems to happen again and again that whenever interest rates are lowered, stock prices go up. From now on, whenever interest rates go down, I'm going to get excited about the stock market, even though I have no theory whatsoever as to why the two events should be connected. They just are-in my mind, at least."
Over time, the network diminishes connections that contribute mostly "noise" and bolsters connections that for the most part "work." Eventually the network "memorizes" the incoming pattern as a specific distribution of varying connection strengths, in the same way that HER "memorized" the strategy of Hexapawn as a distribution of varying matchbox contents. Furthermore, as long as the density of interconnections among neurons is sufficient, the connections themselves can be random: No "wiring diagram" is needed, just as HER needs no logical instructions in strategy.
In an artificial device, we modify the weights by hand, from the outside. Biologically plausible schemes such as "Hebbian learning" were the first step toward an understanding of how local, lower-level systems can influence the global behavior of a composite whole, without external supervision or human tinkerers.
This relatively simple process conforms to the actual structure of networked biological neurons. These typically have multiple short input channels called "dendrites" and a single long output channel called an "axon." The axon then branches out to reach at least one dendrite on many other neurons.
The structure itself suggests that the body of the neuron combines multiple inputs to produce some sort of overall total (or average), which it then puts out. Figure 2-1 shows how artificial processing elements are modeled directly on this biological structure...
|Pt. 1||The Quantum Brain|
|Introduction: The Quantum Crisis||3|
|Ch. 1||Mind Out of Matter - The Machinery of the Fourth Brain||9|
|Ch. 2||Opening the Mind's Eye||18|
|Ch. 3||Death and Birth||25|
|Ch. 4||Teaching a Young Dog Old Tricks||33|
|Ch. 5||The Wet Net||44|
|Ch. 6||Spinning the Glass Fantastic||54|
|Ch. 7||The Game of Life, or How the Leopard Gets Its Spots||74|
|Ch. 8||By Your Bootstraps||87|
|Ch. 9||Is Man a Machine?||99|
|Ch. 10||The Insoluble Solution||107|
|Ch. 11||EPR: Wanted: Dead and Alive||116|
|Ch. 12||E Unus Pluribum - Out of One Mystery, Many||127|
|Ch. 13||Stranger in a Strange Land (The Stranger the Better)||137|
|Ch. 14||In the Matter of Mind||155|
|Ch. 15||The Protean Computer||173|
|Ch. 16||To Fickle Chance, and Chaos Judge the Strife||190|
|Ch. 17||Quantum Ripples||213|
|App. A||The Simplest Hopfield Net and the "Canonical Cortical Microcircuit"||226|
|App. B||The Tuszynski Microtubule Model||230|
|App. C||Coherence and Decoherence||238|
Posted April 15, 2001
The best books are like a journey where the reader follows the course mapped by the author, a route where new vistas continually emerge along the way, and fresh paths leading in surprisingly new directions suddenly become visible. The Quantum Brain by Jeffrey Satinover is such a book. The landscape explored in this remarkably lucid and thought provoking book is the `terra incognito¿ of consciousness and the quantum realm. Along the way towards his ultimate destination -- the establishment of an empirically based argument that quantum indeterminacy is amplified by the brain so that human behavior on the classical level is not mechanically determined ¿ Satinover explores a wide range topics: including quantum physics, bio-chemistry, chaos theory, computer science, neurobiology, and more. Each of these areas is well handled thanks to Satinover¿s clear, accessible prose, though at times the reader may feel that Satinover is a hasty tour guide ¿ for no sooner has one surmounted challenging intellectual terrain than Satinover his charging ahead to the next stopover. Even those with a high degree of scientific literacy may need to catch their breath now and again, but the reader who is prepared to make the effort will find that these necessary exertions lead to an exciting new view of man and his place in the cosmos. One of the interesting waypoints in Satinover¿s intellectual trek is his discussion of quantum computers and the likely possibility, some time in the future, that we may build conscious computers that far exceed us in their intellectual capacity. Might a new generation of super computers help us to answer perennial scientific and philosophic questions? Or would they, perhaps, view mankind as an unnecessary impediment to their fuller existence, a species best dealt with in a ruthlessly Darwinian fashion? The answer to such questions, of course, lies somewhere in the future. But these and a host of other issues such as: God, morality, free will, and social concerns are raised, and Satinover manages to pose interesting questions on each of these. There are, of course, some gaps. Satinover does an excellent job explaining essential quantum principles, the nature of artificial and biological neural networks, computer science, and much more. But he never explains how or why a quantum computer ¿ a computer that harnesses the mysterious nature of matter when it `exists¿ in a superposed state -- might be able to perform operations that differentiate them significantly conventional computers such that sentience might emerge. Or more specifically, is a quantum computer something more than just a Universal Turing Machine? Also, cognitive scientists are just beginning to understand how much of our thought is rooted in the body. We `reach¿ for conclusions, `grasp¿ ideas, `point¿ out difficulties, and `hold¿ onto our opinions. Kant once said that ¿the hand was the visible expression of the mind¿ ¿ an idea that captures just how much of our seemingly `disembodied¿ thought depends on metaphors drawn from our immediate physical experience. If there is something to this view, what kind of physically rooted metaphors could a computer draw upon to describe its immediate experience? And if a computer¿s physical or `bodily¿ apparatus differs so greatly from ours could we ever hope to understand the metaphors they use to describe their experience? As Wittgenstein said, ¿If a lion could talk we could not understand him.¿ None of these off the cuff quibbles suggest that I believe conscious computers are an impossibility. Indeed, I think the issue will prove to be an empirical one. A strange thought, actually, when you consider that `subjectivity¿ isn¿t anything like other matters studied empirically ¿ like rocks, trees, stars, or protons. And yet subjectivity is a pre-condition for studying anything empirically to begin with. So where does subjectivity fit in the world? For the 20th century¿s most brilliantWas this review helpful? Yes NoThank you for your feedback. Report this reviewThank you, this review has been flagged.