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    The Evolution of Intelligence beyond the Computer Age

    Copyright (c) Paul Pangaro 1987. All Rights Reserved.

    [The following text was written as an introduction to one major theme of a proposed book. A "proposal" followed, but the relation to this sample was not perfect. References and illustrations (with one exception) are omitted.

    This text has been OCR converted and is probably not be perfect; apologies.]

    The joy of creating ideals, new and eternal, in and of a world, old and temporal, robots have it not. For this my mother bore me. ---- Warren McCulloch

    1 The Fantasy

    Figure 1, "Our machine, the Eureka", was first published in "Calculator Saturnalia", a short volume made up of two, intertwined conceits. A novella, about the adventures of a cybernetic research team and their robotic/intelligent partner (named Olga), is punctuated by instructions for calculator games.

    To leave it that the calculator games are mere amusements would be a disservice. They consist of political satire and irony; they are clever, instructive, and usually misprinted. This last point makes it hard to follow the directions.

    The novella is the setting for our little Eureka, a pocket calculator (truly). As you expect from the drawing, it too is an extended conceit. The original caption is given just below, and has metaphorical value for a major theme of MACHINE NARCISSUS. (It is worthwhile looking for each indicated point in the drawing as you read each section a, b, c, .-- as this enhances the effect.)


    Figure 1: "Our machine, the Eureka, supplied also with Corinthian and Ionic supporting pillars. If dismantled by removing all thumbscrews (a) fits easily into pockets. Compass (b) as required for navigation. Keyboard (c) for numbers (0 to 9) and operations (+ - * /). Useful display of results (d). Electric motor to aid manual crank-action (e) with starting capacitor. Advanced design reflected in auxiliary solid-state display (f) and integral random number generator (g) may be scrutinized through inspection port (h). Weather vane atop the equipment provides further and independent source of random numbers."

    The Eureka, however, is not entirely a joke. For Peek wants you to realize that present-day digital computers, from the smallest calculator-in-a-watch to the largest computer on earth, are all as quaint and clunky and bloody stupid as our little Eureka. And a good deal less charming. Pask is saying that there is no sense in trying to make such a crude calculating engine "smart" because any attempt will fail. You may as well try to dismantle it and carry in your pockets.

    2 The Mirror

    Considered to be the highest faculty of human beings, Intelligence is at the center of our history and achievements. Efforts to amplify human intelligence find their greatest expression in the medium called the computer. Some researchers seek to make machines intelligent like humans; others say they seek intelligence in any form.

    Mythology brings us Narcissus who fell in love with his own reflection, not realizing he was seeing an image of himself. So too in seeking to reproduce, intelligence in modern "artificial intelligence" (AI), we think we see the universals of intelligence as independent of human life. Instead we only see our own cultural view of what it means to think. Culture and its view of intelligence are complements: each delimits the other. Developments in AI are both reflections of culture and influences upon it.

    On the face of it, to consider our Eureka as a candidate technology for smart machines is quite absurd --- and yet that is precisely what researchers in the AI community have done since the 1950s. Our culture at large has brought about this startling condition, and examining the culture helps us to understand the limitations of the current technology. That is where our story will begin.

    3 The Goal

    I do not mean to imply that the phrase "machine intelligence" cannot be given meaning; but where you begin determines where you end up. Some researchers now say that the directions of mainstream AI cannot produce intelligent machines to any significant degree when compared to human capacities. But they do believe there is a meaningful interpretation to "machine intelligence" and are working to make it.

    Until recently, perhaps the last 10 years, this position would not have been easily defended in scientific and academic circles. It has been dismissed as pure bias or mere "intuition" or smacking of humanistic rather than scientific values. Discussion on the subject deteriorates into stand-off and reasoned arguments become static statements of personal views.

    However, work that is squarely in the discipline of science itself now questions the universality of present-day computation, previously thought to be powerful enough to make brains. Major fields of scientific enquiry are descending on AI with powerful, new perspectives on the problems of intelligence. Early results point far beyond the already-predicted revolutions in computers and biology. Even our conceptions of language and society will be changed, and perhaps more profoundly than by any science since the Renaissance.

    Our story in MACHINE NARCISSUS is the story of the imminent development of intelligent machines as an evolution of technology and of culture. Computer science has produced a technology, but society and its concept of intelligence are the boundaries within which technology develops. Some technology might aid the quest, but which? And based on what goals and whose cultural biases? Success in creating machine intelligence requires a major change in science and society, and in science that change has begun.

    4 The Paradigm

    In a culture which has successfully reduced the world to its atomic parts and their causal interactions, it is inconceivable that the same reductionist approach would fail when applied to intelligence. But the evidence against reductionism in machine intelligence is growing, and the pressure to change is coming not from one field but from no less than four: physics, mathematics, biology and cybernetics. Each are providing new evidence, experimental as well as theoretical, as to why and how a revolution in AI is required. And AI, within itself, is evolving and responding with new models and new technologies.

    AI developed from the context of the present scientific milieu. Although there are other perspectives not in the mainstream (as our story will tell), AI is mainstream science. The context of any enquiry has implicit influence on the nature of the search. Modern parlance might call this context "the paradigm", but modern parlance would also miss the essence of that term --- that the implicit framework is not known --- for a paradigm is the underlying structure that influences but is not known explicitly. The irony is that, once known, the paradigm exists no longer. But while it exists, it forces researchers' hands and only with revolution can real change occur.

    Ironically too there have been countless side-effects (read: advantages) in the search for AI in the field of AI. Given the very hard problem of making machines "smart" in that way, computer scientists have worked hard to create problem solving tools. Powerful techniques and computer languages, software development workstations and even expert systems are entirely within this category. These technical advances cloud whether the goal is being approached. As raw power increases it is easy to say (1) advances have been made and (2) further advances require only better technology.

    But smart machines, built on the paradigm of the Eureka? First 1 expose the paradigm in our culture's notions of intelligence, and its effect on efforts to embed intelligence into computers. Once exposed it is disposed, and we can more clearly discern a new context of interpretation for machine intelligence in modern research.

    5 The Computable

    Alan Turing created the "mathematics" behind digital computers --- and the quotation marks emphasize that the term has a specialized context of interpretation. After all, one person's mathematics is another's alchemy. Ironically Turing didn't intend his earliest work quite the way it was taken on by computerists. But under the circumstances they couldn't help it.

    Turing made a mathematical proof. I don't say "discovered" a proof because that would imply it was a universal waiting to be found. And if anyone at all could have found it, it would not have been such a good gift.

    The value of the gift was to reduce all "computation" to one type, or class, of computation. No matter what you were trying to do (balance a checkbook or compute the orbit of the sun) the calculation could be described in an abstract form. In Turing's sense we have two types of fodder for calculation: that which is generally recognized as the "data" of conventional problems (the amounts in dollars and cents, positions of the heavenly bodies); and a representation of the desired calculation (the goal, in the form of a procedure). This is good news in the short term, but bad news in the long term.

    What would be the advantage of this abstract description, other than an aesthetic consistency? Here is the genius of Turing: once the descriptions were made consistent, each description could itself stand alone without any reference to an individual knower, or a particular calculating engine. There is no reference to physical embodiment, yet the procedure is completely defined. The concept of an abstract machine is invoked to simulate the calculation of any specific machine. A specific machine for the specific problem need never be built. Only the data of the problem, and the data that describes the appropriate machine to "solve" it, is needed. Here is the culmination of years of reductionism in science: the complete, stand-alone, disembodied calculation.

    Turing took the opportunity to move from thought experiment to technology: he constructed his abstract machine in physical form. Turing's computer was fed the data for the calculation (about our checking account, for example) as well as the disembodied description of a machine that, if it existed in physical, restricted, hard-wired form, would compute what we wished (i.e., the balance of our checkbook). Today we have a name for this disembodied description: "software." Although that physical hard-wired computer which only balances checkbooks does not exist, its results do. A machine is created by its computation being performed, even though the machine itself is not created, except virtually. Doesn't it have a Frankenstein monster appeal, even at this primitive stage of its capability? (Would the Eureka be scary, rolling down the hall at you and in the dark of night?)

    6 The Implications

    The first implication is that you can make money by going public with a company selling expert systems based on the Eureka. No, sorry, we need to develop the argument ~a bit further for that one.

    The following is a partial list of where we end up as researchers in the present milieu, carrying on from the present scientific traditions, adding Turing Computability to a fair amount of chutzpah:

    1. Problem solving is breaking large problem into smaller ones.
    2. Solving small problems means extracting the variables and plugging them into procedures.
    3. Procedures are what we use to think.
    4. Knowledge is what is in the procedures.
    5. Computer languages can represent and execute procedures and hence can have knowledge.
    6. Putting procedures into computers is called programming.
    7. Computers can be smart if they have the right programming.

    Got it? It surely took a great deal of cultural and scientific development to invent and engender that scenario, told of in Part 1 of MACHINE NARCISSUS. Part 2 of our story will show how each of the above points is interpreted in computer science, including the present fashion in expert systems. Alternatives, the newest starting points, will be the focus of Part 3.

    7 The Limitations

    Would that we did get the above points without any restrictions. One problem is that Turing machines, in their abstract and theoretical form, perform any computation of the Turing Computable type. This has the advantage that we now only need one machine and anything (do you detect a climax coming?) could be computed- It takes no PhD to realize that the crux of the matter lies in the definition of Turing Computable, a topic which must be taken up again in some detail.

    But what has this to do with human intelligence? Some would feel that thinking and the execution of procedures as described are quite different things. Aside from superficial likenesses, there seems to be no reason, other than naive desire, to push the analogy.

    But what if there were some hint that the brain worked along the same lines as Turing machines? Or, at least, that brains and computers could be compared in some way? You must forget, during this leap, that you are assuming that the brain is the only item of interest in a discussion of intelligence. The impact of this error (paradigm?)is a major topic of our Story, and is dealt with briefly a few sections below. Before that, I present the stepping stones for the leap that brains and computers have similarity. Much of this book, and the remainder of some century of research (perhaps this One) will be about knowing their differences.

    8 The Nervous

    Warren McCulloch and Walter Pitts put neurophysiology and mathematics together to look into the computational structures of the nervous system. With the powerful Turing metaphor so close by, they sought to take advantage of it. Knowing about neurons and theories respectively, McCulloch and Pitts made a theory about networks of simplified neurons, showing that these networks could compute anything that Turing machines could. Their purpose was to show a strict relationship between neural nets and Turing machines. Putting it in the crude and possibly misleading terms of "the layperson,"

    However, the community of computer scientists, soon to be researchers in the new field of artificial intelligence, took over from there. With the personal confidence that Turing Computability was all encompassing, they became Sorcerer's Apprentice. Turning the reasoning backwards in an absolute fit of scientific reduction, their dream is simplified to:

    A computer could be a brain.

    After all, the brain is a "meat machine", isn't it? It has been a slippery slope for smart machines ever since.

    9 The Culture

    This feels like a kind of wish fulfillment, having all this theory and the advancing technology too. The euphoria is understandable, though the stock market price of expert systems firms may be in doubt. Fundamentally, every desire within this community of research is to bring mentation down to the level of computation.

    Is it a reasonable wish? According to Turing mathematics, it is. Now no one would easily imagine that what we have here is a theory of mind --- this is mathematics, only. And although logical and others consider mathematics the center of the universe there is not much agreement on that point from physicists, cyberneticians, or biologists.

    A theory does not itself make an artificial mind.

    Researchers in many fields have had the instinct (or more) that Turing Computability is a trap. However workers in AI have been, until recently, unstoppable. Some concession to parallel computers, where many processors are working at once, has been made all along, but usually in terms of efficiency, not inherent limitation. But at last a strong response has come from physicists, extending the notion of computation to forms which are not contained in the Turing model. (The world has caught up with Turing himself who, after his early work on computability, explored how living organisms execute procedures beyond the limits of his earlier formulation.)

    Now the concept of Quantum Computability subsumes that of Turing Computability and the physics of this is the subject of [Chapter x]. Because physics is a respected field and plays the same "objectivity" game as mathematics, and because these fields have currency with the computer scientists, this work is having widespread impact. At this same time, AI itself has been toying with new computer architectures.

    So it is all moving ahead smoothly, or so it seems. The solution, however, is not merely an expanded definition of Computation with a capital "C". It also requires a shift of emphasis, away from what goes on "in the brain", to what happens "between the organism and its environment". And suddenly we are in the world of the New Biology.

    10 The Organism

    Everyone who studies biology knows that it is about living things, and part of that study is the relationship of the "organism" to its "environment". Our descriptions of this relationship include the concept of the organism's model of the environment: an inner-constructed view of the world which is the means by which the organism operates, plans, and survives.

    Plausible? Yes indeed, it is an excellent model. For that, in turn, is all that it is: our model of what we conceive of as happening in the organism. We do not, and can never, know what is inside the organism and by what basis its operations are conceived and carried out. Instead, and almost without our knowing it (one of those paradigms creeping in?), we project the notion that the organism builds a model of the environment.

    Who are the "we" of the above paragraph, who posit these models? We act as individuals, surely, but always in the context of the culture. To say that organisms build models of the world is a cultural statement rather than "the way it is." Why do we have that particular view of organisms? Because clearly we humans do it that way, and so met other living things...oops, falling into the trap again. Just because we 88 humans have the power to describe a world in language, we forget the subjective nature of those descriptions.

    Why must it be the case that our description of our experience of building internal models of the world is actually what we do in our nervous systems?

    Descriptions are created in the context of purpose: to predict, to build a technology, to fulfill a cultural desire.

    So, why complain? Only if the descriptions generated mislead or inhibit certain developments (considered, of course, to be desirable).

    11 The Gap

    We might consider the eye an "input" device: we say that when we see a beautiful flower, it is because the eye receives the image of the flower as input. We might also consider the hand as an action or "output" device (even in a restricted context, say of movement). But just because we attribute the status of input or output does not make it so; the words used can cause us to think that the nervous system takes input from the world and provides output to it.

    A new body of work, in biology primarily, warns against this as mis-stated. Consider the connection between a neuron and a synapse in the nervous system: Can you imagine spreading apart that connection and calling the neuron an input device, and the synapse an output device? It seems absurd, but from the point of view of the nervous system, any attribution of input or output is from an observer, not from the nervous system. To say of ourselves that "our own eye receives input" is an observer statement. To the nervous system, what happens in the retina is a perturbation; but it is no more "input" than what a synapse has to say to a neuron.

    What alternative view can replace the conventional view and provide new capabilities? That provided by New Biology (and affirmed by a variety of other fields, perhaps even literary deconstruction) is that the nervous system is a closed system. Nothing external to it (as the observer sees "external") can determine what happens in the nervous system; it can only trigger changes determined within its structure. (The implication is that the nervous system is "deterministic" and in important ways, it is; it is not however usefully predictable. The full implications must be reported in [Chapter y].) If the behavior of the nervous system is not determined by the environment, then the nervous system cannot construct a representation of the environment "internally." The observer may say that an interaction between the nervous system and the environment causes a structural change in the nervous system, and that this change constitutes a representation; but for the operation the nervous system, this structural change is not a representation of the environment.

    To quote one maestro of this reasoning, "To talk of representation in the operation of the nervous system has no sense."

    Why bother with this reasoning? Sounds like pure pedantry on first reading (or maybe on second, when the reasoning appears more firm but no more helpful). To build an artificial intelligence that *s like a nervous system, the implications are major. The amount of energy spent in AI in the areas of knowledge representation and expert systems is huge. Perhaps the direction to follow is not that of AI, which is merely building on the current cultural paradigm. Would it be better to first approach from theory, then create the technology? Turing could create the technology because he had a theory. That the theory has allowed the technology to be taken too far is ironic.

    The implications of the reasoning in New Biology might not be savory to some. Reversing the example of placing a gap between the synapse and neuron, consider that the environment is the medium that closes the gap between eye and hand. In that sense the environment is a component of the "mind"; just another synaptic gap. The so-called mind-body "problem" takes on another perspective.

    Closed nervous systems imply a forced solipsistic existence for us all; but this is not really so. Instead of remaining monads, we share with each other the status of observers. We agree that we see the flower, and even agree that it is beautiful. We enter each other's synaptic gaps and thus, through language, intertwine our nervous systems in a dance of agreement.

    12 The Convolution

    To imagine a change coming about in a field of science is not difficult. The constant evolution in all fields of endeavor, and the acceleration of that change in this century, is reported daily. in the media (albeit by hyperbole). Within science the stories of revolution have made news, careers and reputations. What is particularly difficult about our story is that it represents revolution not just in computer science or artificial intelligence, but in physics, biology and mathematics as well.

    To have a major change within a defined field is part of the excitement of intellectual endeavors in science; individuals become scientists in hopes of bringing about such revolutions. For major fields of science to also converge upon each other, not for the sake of taking over another's domain but because there is no choice in the matter, is very, very rare.

    The details of how each f~ield fire my pattern is the story of the book itself. The focus of each field, and the revolution that each represents, is surprising. I just presented the some surprises ~in biology, and the next sections do the same for physics and mathematics.

    13 The Uncertain

    At its extremes, the field of physics appears more concerned with description than anything else. Implementation and technology seem irrelevant when discussing the composition of distant galaxies or the existence of atoms or molecules or quanta or quarks, whatever happens to ~be fashionable in the current century. To expect that physics has anything to say about the nature of computation is to expect that the molecules of the Eureka ~better not come apart or we've got troubles in our pillars. Not very helpful, really.

    The physics of things would also help insure that the computation itself were robust. We hope that it will be consistent and give the same result for the same data every time, for example. We do want determinism somewhere. Physically, the Eureka achieves this because of its mechanical, and hence physical, prowess; if it holds together (why do you doubt this?) it calculates consistently. Electronic computers appear to do it by physics too; after all, the electrons are connected to the ions, the ions' connected to the... A familiar tune. But knowing the physics at this level is not a useful description of the calculation. That would require consideration of what the electrons, et al, represent in the calculation. This requires a symbolic representation, and quickly we are out of the physics and into the computation science of the problem. (Here we have one reason why the physicists want some way to move back into the spotlight.)

    Physics in the 20th century has been dominated by quantum mechanics, and we still hear stories of how you might actually appear on the other side of a wall, just due to the physics of things, though no one has ever seen this. To say "physics" in the modern age must invoke some sense of the uncertain, indeterminate, probable, and even the unlikely. But, how might such indeterminacy be at the heart of an argument about intelligent machines?

    Quantum Computability requires a look at 20th century physics' current metaphor: quantum mechanics. Oddly enough, quantum computers might compute some things faster than conventional, deterministic Turing machines, and, of course, they compute some things which Turing machines cannot. Quantum computers are capable of simulating any arbitrary, finite physical system --- including the universe itself. Hence, in the conclusion of one main proponent of this work) all this does is merely to "make computer science a branch of physics

    14 The Illogical

    Mathematics too is part of the story and not unexpectedly. The realm of logic and representation has often been its triumph, though the struggle between mathematicians and physicists is sometimes forceful,

    The logic provided by modern mathematics is not the trivial sort of Mr Spock of Star Trek; it admits to the subjective and multiple-world sort that humans sense every day. Computers though derive from a tradition of representation and calculation that is impoverished by comparison. Their technology is the very newest, but not their techniques. In this sense computation science is as new and as primitive as can be.

    One contribution of George Boole's was to apply logic to do arithmetic, two subjects that are not the same. When binary numbers arose as an easy technique in which to interpret Boolean logic, computer technology jumped ahead because here was a single framework in which to represent and compute forms that were logical ("if a and b, then always c") and arithmetic ("z = x + y"). Single framework means simpler technology, and computers could therefore be built from simpler elements.

    So the only hard logic built into computers has been the sort .from this zero/one, yes/no, true/false, black/white, all/none tradition. As the physical embodiment of the logic glided across vacuum tubes and transistors and various electronic forms, there was no questioning of the binary mapping of zero/one to true/false. Facts became primarily true or false. When a fudge was needed, any uncertainty became a probability, and without much satisfaction. Yet another probability is then required: a measure of confidence that the result was indeed correct!

    Alternative logics have existed in philosophy and mathematics for many, many decades. Only recently are these other choices coming into fashion with those closer to technology. These logics have various for but all strive to represent values in shades of grey.

    One direction to move is to the more primitive: "In the beginning was the distinction." The distinction is said to be more primitive than yes or no, 0 or 1. Breaking down any complex structure can be performed by carving more and more distinctions until no more can be drawn with the available data. Indeed any division along a continuum.(another means of providing multi-values), or the convolving of many values along many dimensions, can be modeled as distinctions, and vice versa.

    These alternatives have been invoked to handle belief systems.

    Contradiction and conflict, ambiguity, context, commands and questions all survive in representations that are not true/false based. Ancient and modern paradoxes are not solved but are resolved in formulations that provide multiple values for any given variable. Modal logics have been found by some researchers (prominently the Japanese) to hold greater promise for representing complex problems.

    Recently an algebra has been proposed which embodies an "ethical" structure. It has been used to explain differences in the morality of whole cultures. If validated (and already the evidence is strong) such an "algebra of conscience" would be a candidate for implementing the core of Asimov's Three Rules of Robotics.

    Mathematics can also express the structure of existence. The topology of Systems would be an odd place to look for an explanation of laughter and tears, but it has been done. The dimension of event that is required to be wise can also be derived, and it corresponds closely to a lifetime.

    15 The Nexus

    There is a danger in the convergence of so many strong fields on a single domain: divergence of descriptions, competition for dominance, in general the view that one view should take general precedence because of its scope, aesthetics, or (heaven help us in a subjective domain) predictive power. But, like in an imaginary science fiction, there is also available a single (I hesitate to use the world "unifying") framework of description in which to couch the disparate approaches of these many fields. Cybernetics, the most misunderstood field of the era, is this framework.

    Lest it immediately appear that a single icon is being invoked to prevent the dominance of conventional science, some perspective is necessary. Cybernetics is an epistemology, rather than a scientific discipline defined by a domain of application. Not concerned initially with embodiment, it has no entrenched interests, as physics, biology, mathematics, or computer science invariably do. Though not from mainstream science, cybernetics is periodically adopted by those fields to provide answers to otherwise unanswerable questions.

    Cybernetics is concerned with how we know what can be known; therefore it is about uncertainty of the ultimate kind. It is not control theory, or neural modeling, or robotics. It may be about all of these things, but only if they are modeled in particular ways. it is near to mathematics, because it models the trajectories of systems. It 18 near biology because it models a system's ontology. It is like physics because it seeks to encompass the macro and micro, in a unity of physical and mental worlds. And it encompasses AI and computer science because It can combine their objective needs with the subjective acts of inventing distinction and having purpose,

    The above comments must simplify things somewhat. We similarly simplify conventional biology to be concerned with living things, AI with intelligent machines, physics with the large and small of the universe, and mathematics with representation in symbol systems. All of these are human endeavors, which is their unifying core. Each Involve the observation of the interaction of systems. Cybernetics, as an epistemology, is concerned with the observation of interacting systems; its primitive is the interaction.

    One major product of this enquiry is a theory of conversations that 18 not restricted to verbal exchange between humans. It has the scope to model exchange among the tiniest particles of physics, and the largest concepts of mind. It can model consciousness, time and the specious present. In a view of existence as solely interaction (as that is all that we know)* the use of the word "conversation" is not just a brilliant metaphor, it is the unification of subjective and objective, scientific and humanistic.

    This is the contribution that cybernetics makes to the fields outlined above: it brings an image of synthesis, not to replace but to enhance and connect. In the process, many a paradigm is challenged and revised. And this is how we will complete our whole story, with one additional contribution from cybernetics to the nature of machine intelligence, summarized as follows:

    If interaction is primary, then distinct individuals must exist in order to interact. The observer of the interaction posits their existence --- and at the same time, posits his or her own existence, by acting as observer of one's own conversation with the interaction under observation. All knowing is relative to the ability to interact, and hence it is relative to the existence of the distinction of the individual. If knowledge cannot exist without a knower, making machine intelligence means making an individual --- not in the sense of robots, but in the ability to draw distinctions, of oneself and others. To succeed at this is to create, in a single stroke, self and other, the here and now.

    16 The Mirror

    Recognizing oneself ("one as a self"), then, becomes central to intelligence. The mental places we inhabit provide distinctions we can make (our culture), and create a context for further distinctions that we invent (our evolution). To view ourselves on the outside, we model the cosmos. To view ourselves on the inside, we model intelligence. Everywhere between these extremes, our language and society attempt to unify experience, to smooth any edges and to create a seamless flow of description from inside to outside, macro to micro, within to among. After centuries of specialization and particularization, the movements of science intertwine and converge: physics becomes computation, mathematics becomes reason, and conversation becomes epistemology. And if science allows biology to become cybernetic (remember synaptic gaps and the dance of agreement), intelligence can be embodied in new forms.

    Our story, then, begins and ends with a reflection of ourselves. Our insides project into the world, and the world reflects how we conceive. We project into ourselves, and our mind reflects back the universe. The nested reflections of science and culture, our world and ourselves, are the cycle of human endeavor that our story is about. At the center is what we name as intelligence. Our naming makes it ours. At some time, machine intelligence will also do naming, drawing the distinctions of the world in its own language, just as the old story says Man and Woman were commanded to do.

    - end -

    © Copyright Paul Pangaro 1994 - 2000. All Rights Reserved.