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A 4 Pages Term Paper on Possibility of Artificial mind

As artificial intelligence (Al) has matured, it has provided a unified home for approaches to intelligence that manipulates symbolic representations of knowledge. Research into other, non-symbolic approaches to computational intelligence is much less accessible, scattered through a dozen disciplines and hundreds of different conferences, journals, and books. Franklin seeks to pull these scattered threads together to understand how one might implement intelligence on a machine. His approach is unabashedly inferential and conversational. Though a mathematician and a researcher, he assumes the role of an amateur toward most of the technologies he discusses, and explicitly presents the book as a tour of various research projects that he has found of interest.

AI cannot just be about getting systems to do apparently intelligent things - for then all of computer science could be AI. The functional approach, started by Turing, of building systems to do things by any means possible, is worthwhile enough, but it is engineering - plain programming, hacking. We need first to understand that how do living systems do it? And whether an AI can make some contribution to cognitive science - an AI that works the way that living things might plausibly work.

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Dembski writes that it is possible to prejudge the relation between intelligence and computation. Therefore, it can be assumed that computation comprehends all of intelligence. Alternatively, this can also be suggested that intelligence can never be subsumed under computation. The link between intelligence and computation involves contribution of mathematical logic to recursion theory, physics prescribes limits on computational speed, philosophy lays out the mind-body problem, theology raises the question of immaterial souls and spirits, etc. However, because these perspectives provide informative debate over the respective boundaries of computation and intelligence, it cannot be ignored that such a debate is primarily philosophical and thus independent of cognitive science qua science.

  In the Foundations of Cognitive Science Simon and Kaplan also offer the following account of artificial intelligence (AI):
Artificial intelligence is concerned with programming computers to perform in ways that, if observed in human beings, would be regarded as intelligent.

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Dembski regards this proposal to be scientifically unobjectionable, as well as, suggests the main practical business of cognitive science-programming computers to perform tasks thought to require intelligence in humans to be accomplished by AI. However, for the cognitive scientist who has prejudged the relation between intelligence and computation, the very phrase artificial intelligence becomes tendentious, implying that artificial intelligence has subsumed the whole of human intelligence. To understand how AI is possible, first need to study why computers to be the sole exclusive tool of AI? And why this AI to be an equivalent to human intelligence?
Dembski claims that there is a causal connection between brain. He emphasizes that philosophical materialism that permeates today's intellectual climate, with it comes a commitment to explain human intelligence strictly in terms of the human physical system. Given the indisputable connection between brain-states and behavior, the materialist has a facile answer to the mind-body problem: mind = brain.

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Human Brain

To know how artificial mind works and how it can follow the pattern of human brain, it is mandatory to study how human brain works and what it really is made of. The human cerebral cortex contains something like 1010 to 1014 nerve cells. With that astronomical number of basic units, the cerebral cortex is sometimes referred to as the "great analyzer." Each nerve cell makes contact with some 5,000 or so other nerve cells; that is, each nerve cell has up to 5,000 junctions with neighboring nerve cells, some as many as 50,000 junctions. At those synaptic junctions or synapses, information is passed between the nerve cells. What is significant about that process is that the information may be modified during its transfer. The brain's cellular organization shows an almost unbelievable profusion of connections between nerve cells. Without such intricate connectivity, learning processes would be impossible.
The brain is considered an adequate explanation for mind and intelligence because of its vast complexity and intricate organization. By being complicated enough, by comprising billions of interrelated components, the brain is supposed to render thought possible.
The connection between brain-states and intelligence is a matter of ignorance. This is not to say there is no causal relation between brain and behavior. There is if one looks at isolated, discrete behaviors. But as soon as one moves to the level of goals, intentions, and what philosophers more generally call propositional attitudes, cognitive scientists abandon hope of understanding this higher level through the lower neurological level. Thus cognition supervenes on neural activity, which in turn supervenes on the underlying physics; alternatively, intelligence emerges out of neural activity, which in turn emerges out of the underlying physical configuration; and consciousness is an epiphenomenon of neural activity.

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Artificial Mind

Unlike brains for which identical copies cannot be mass-produced, computers and their programs can be copied at will. Inasmuch as science thrives on replicability and control, AI offers tremendous practical advantages over neurological research.

Now the problem to understand is the way computers can be very well model the brain. How are cognitive scientists working to fit that model? How can they justify the claim that computation provides a sufficient cause for intelligence? Rather than simulate brains, cognitive scientists write computer programs which simulate behaviors typically regarded as requiring intelligence. Thus they bypass the neural level and move directly to the highest cognitive levels: perception, language, problem solving, concept formation, and intentions. Instead of modeling the brain, cognitive scientists model the intelligent behaviors exhibited through those brains. Thus many man-years of programming have been spent developing language translators (unsuccessful), chess playing programs (successful), expert systems (successful to varying degrees), etc. On balance it is fair to say that from the technological side AI has been and will continue to be successful. Nevertheless, as a comprehensive approach to human intelligence, its results have been less impressive. This is not for any lack of ingenuity on the part of computer programmers-some are very clever indeed. But intelligence involves much more than clever programs which are adept at isolated tasks. What goes by the name of AI has only delivered programs with very narrow competence.

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Confident that this will change, cognitive scientists adopt the following rationale. If through concrete computer programs (algorithms) they can simulate all-important aspects of human intelligence within a complete information-processing package, then they will have proved their case that human intelligence is a species of artificial intelligence. According to Zenon Pylyshyn, professor of psychology and computer science, and director of the Center for Cognitive Science at the University of Western Ontario:

I want to maintain that computation is a literal model [nota bene] of mental activity, not a simulation of behavior, as was sometimes claimed in the early years of cognitive modeling. Unlike the case of simulating, say, a chemical process or a traffic flow, I do not claim merely that the model generates a sequence of predictions of behavior, but rather that it does so in essentially the same way or by virtue of the same functional mechanisms (not, of course, the same biological mechanisms) and in virtue of having something that corresponds to the same thoughts or cognitive states as those which govern the behavior of the organism being modeled. Being the same thought entails having the same semantic content (that is, identical thoughts have identical semantic contents). The brain is the kind of system that processes such [programs] and that the [programs] do in fact have a semantic content.

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Even if human intelligence is physically realizable through  an electronic computer, a hypothesis that Dembski rejects, it is by no means obvious that human intelligence is capable of realizing it. Thus even if some super-intelligence could build a computer that, to use Pylyshyn's phrase, is a "literal model of mental activity," it is not at all clear whether cognitive scientists have the brains, if you will, to get the job done.

Beloff, in his response to the "strong claim" of the artificial intelligence, suggests that it is false belief that AI could get equivalent to human intelligence. Strong claim of AI movement here is taken to mean that there is no essential distinction to be drawn between a living mind and some possible mind-like machine, with the corollary that there is no upper limit to the intellectual achievements of which a machine might be capable. He shows that this claim rests on a particular theory of mind that has come to be known as Functionalism. He discusses this theory but found it to be based on a fallacy. The valid reasons in its disapproval is ignoring the fact that the mind-brain problem is, primarily, a problem about the ontological status of minds and not, in the first instance, about their functional properties. Finally, he reaches to he conclusion that “the essential distinction between minds and machines remains and cannot be eliminated hence the strong claim must be dismissed, as must its corollary.” Beloff is very doubtful about the extent to which automation can take us in practice, the matter that can only be settled empirically. The proposition suggested by him is that “AI can make to our understanding of the mind is the negative one of demonstrating what mind is not. In other words its value lies in the clearer appreciation it affords of the difference between the mechanical aspects of mental activity as opposed to the intrinsic properties and powers of the mind as such.”

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Works Cited

Beloff, John. Minds or Machines. Truth Journal. Feb 28, 2002

< http://www.leaderu.com/truth/2truth04.html>

Dembski, William A.Converting Matter into Mind. Feb 28, 2002 <http://www.leaderu.com/offices/dembski/docs/bd-converting.html>

Franklin, Stan. Artificial minds. MIT Press. Cambndge, MA. 1995. 449 pp., ISBN 026206 1783.
 
 


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