FREE ESSAY ON MIND AND MACHINE

MIND AND MACHINE

Mind and Machine: The Essay 
Technology has traditionally evolved as the result of human needs. Invention, when prized
and rewarded, will invariably rise-up to meet the free market demands of society. It is
in this realm that Artificial Intelligence research and the resultant expert systems have
been forged. 
Much of the material that relates to the field of Artificial Intelligence deals with
human psychology and the nature of consciousness. Exhaustive debate on consciousness and
the possibilities of consciousnessness in machines has adequately, in my opinion,
revealed that it is most unlikely that we will ever converse or interract with a machine
of artificial consciousness. 
In John Searle's collection of lectures, Minds, Brains and Science, arguments centering
around the mind-body problem alone is 
sufficient to convince a reasonable person that there is no way science will ever unravel
the mysteries of consciousness. 
Key to Searle's analysis of consciousness in the context of Artificial Intelligence
machines are refutations of strong and weak AI theses. Strong AI Theorists (SATs) believe
that in the future, mankind will forge machines that will think as well as, if not better
than humans. To them, pesent technology constrains this achievement. The Weak AI
Theorists (WATs), almost converse to the SATs, believe that if a machine performs
functions that resemble a human's, then there must be a correlation between it and
consciousness. To them, there is no technological impediment to thinking machines,
because our most advanced machines already think. 
It is important to review Searle's refutations of these respective theorists' proposition
to establish a foundation (for the purpose of this essay) for discussing the applications
of Artificial Intelligence, both now and in the future. 
Strong AI Thesis 
Strong AI Thesis, according to Searle, can be described in four basic propositions.
Proposition one categorizes human thought as the result of computational processes. Given
enough computational power, memory, inputs, etc., machines will be able to think, if you
believe this proposition. Proposition two, in essence, relegates the human mind to the
software bin. Proponents of this proposition believe that humans just happen to have
biological computers that run wetware as opposed to software. Proposition three, the
Turing proposition, holds that if a conscious being can be convinced that, through
context-input manipulation, a machine is intelligent, then it is. Proposition four is
where the ends will meet the means. It purports that when we are able to finally
understand the brain, we will be able to duplicate its functions. Thus, if we replicate
the computational power of the mind, we will then understand it. 
Through argument and experimentation, Searle is able to refute or severely diminish these
propositions. Searle argues that machines may well be able to understand syntax, but not
the semantics, or meaning communicated thereby. 
Esentially, he makes his point by citing the famous Chinese Room Thought Experiment. It
is here he demonstrates that a computer (a non-chinese speaker, a book of rules and the
chinese symbols) can fool a native speaker, but have no idea what he is saying. By
proving that entities don't have to understand what they are processing to appear as
understanding refutes proposition one. 
Proposition two is refuted by the simple fact that there are no artificial minds or
mind-like devices. Proposition two is thus a matter of science fiction rather than a
plausible theory 
A good chess program, like my (as yet undefeated) Chessmaster 4000 Trubo refutes
proposition three by passing a Turing test. It appears to be intelligent, but I know it
beats me through number crunching and symbol manipulation. 
The Chessmaster 4000 example is also an adequate refutation of Professor Simon's fourth
proposition: you can understand a process if you can reproduce it. Because the Software
Toolworks company created a program for my computer that simulates the behavior of a
grandmaster in the game, doesn't mean that the computer is indeed intelligent. 
Weak AI Thesis 
There are five basic propositions that fall in the Weak AI Thesis (WAT) camp. The first
of these states that the brain, due to its complexity of operation, must function
something like a computer, the most sophisticated of human invention. The second WAT
proposition states that if a machine's output, if it were compared to that of a human
counterpart appeared to be the result of intelligence, then the machine must be so.
Proposition three concerns itself with the similarity between how humans solve problems
and how computers do so. By solving problems based on information gathered from their
respective surroundings and memory and by obeying rules of logic, it is proven that
machines can indeed think. The fourth WAT proposition deals with the fact that brains are
known to have computational abilities and that a program therein can be inferred.
Therefore, the mind is just a big program (wetware). The fifth and final WAT proposition
states that, since the mind appears to be wetware, dualism is valid. 
Proposition one of the Weak AI Thesis is refuted by gazing into the past. People have
historically associated the state of the art technology of the time to have elements of
intelligence and consciousness. An example of this is shown in the telegraph system of
the latter part of the last century. People at the time saw correlations between the
brain and the telegraph network itself. 
Proposition two is readily refuted by the fact that semantical meaning is not addressed
by this argument. The fact that a clock can compute and display time doesn't mean that it
has any concept of coounting or the meaning of time. 
Defining the nature of rule-following is the where the weakness lies with the fourth
proposition. Proposition four fails to again account for the semantical nature of symbol
manipulation. Referring to the Chinese Room Thought Experiment best refutes this
argument. 
By examining the nature by which humans make conscious decisions, it becomes clear that
the fifth proposition is an item of 
fancy. Humans follow a virtually infinite set of rules that rarely follow highly ordered
patterns. A computer may be programmed to react to syntactical information with seeminly
semantical output, but again, is it really cognizant? 
We, through Searle's arguments, have amply established that the future of AI lies not in
the semantic cognition of data by machines, but in expert systems designed to perform
ordered tasks. 
Technologically, there is hope for some of the proponents of Strong AI Thesis. This hope
lies in the advent of neural networks and the application of fuzzy logic engines. 
Fuzzy logic was created as a subset of boolean logic that was designed to handle data
that is neither completely true, nor completely false. Intoduced by Dr. Lotfi Zadeh in
1964, fuzzy logic enabled the modelling of uncertainties of natural language. 
Dr. Zadeh regards fuzzy theory not as a single theory, but as fuzzification, or the
generalization of specific theories from discrete forms to continuous (fuzzy) forms. 
The meat and potatos of fuzzy logic is in the extrapolation of data from seta of
variables. A fairly apt example of this is the variable lamp. Conventional boolean
logical processes deal well with the binary nature of lights. They are either on, or off.
But introduce the variable lamp, which can range in intensity from logically on to
logically off, and this is where applications demanding the application of fuzzy logic
come in. Using fuzzy algorithms on sets of data, such as differing intensities of
illumination over time, we can infer a comfortable lighting level based upon an analysis
of the data. 
Taking fuzzy logic one step further, we can incorporate them into fuzzy expert systems.
This systems takes collections of data in fuzzy rule format. According to Dr. Lotfi, the
rules in a fuzzy logic expert system will usually follow the following simple rule: 
if x is low and y is high, then z is medium. 
Under this rule, x is the low value of a set of data (the light is off) and y is the high
value of the same set of data (the light is fully on). z is the output of the inference
based upon the degree of fuzzy logic application desired. It is logical to determine that
based upon the inputs, more than one output (z) may be ascertained. The rules in a fuzzy
logic expert system is described as the rulebase. 
The fuzzy logic inference process follows three firm steps and sometimes an optional
fourth. They are: 
1. Fuzzification is the process by which the membership functions determined for the
input variables are applied to their true values so that truthfulness of rules may be
established. 
2. Under inference, truth values for each rule's premise are calculated and then applied
to the output portion of each rule. 
3. Composition is where all of the fuzzy subsets of a particular problem are combined
into a single fuzzy variable for a particular outcome. 
4. Defuzzification is the optional process by which fuzzy data is converted to a crisp
variable. In the lighting example, a level of illumination can be determined (such as
potentiometer or lux values). 
A new form of information theory is the Possibility Theory. This theory is similar to,
but independent of fuzzy theory. By evaluating sets of data (either fuzzy or discrete),
rules regarding relative distribution can be determined and possibilities can be
assigned. It is logical to assert that the more data that's availible, the better
possibilities can be determined. 
The application of fuzzy logic on neural networks (properly known as artificial neural
networks) will revolutionalize many industries in the future. Though we have determined
that conscious machines may never come to fruition, expert systems will certainly gain
intelligence as the wheels of technological innovation turn. 
A neural network is loosely based upon the design of the brain itself. Though the brain
is an impossibly intricate and complex, it has 
a reasonably understood feature in its networking of neurons. The neuron is the
foundation of the brain itself; each one manifests up to 50,000 connections to other
neurons. Multiply that by 100 billion, and one begins to grasp the magnitude of the
brain's computational ability. 
A neural network is a network of a multitude of simple processors, each of which with a
small amount of memory. These processors are connected by uniderectional data busses and
process only information addressed to them. A centralized processor acts as a traffic cop
for data, which is parcelled-out to the neural network and retrieved in its digested
form. Logically, the more processors connected in the neural net, the more powerful the
system. 
Like the human brain, neural networks are designed to acquire data through experience, or
learning. By providing examples to a neural network expert system, generalizations are
made much as they are for your children learning about items (such as chairs, dogs,
etc.). 
Modern neural network system properties include a greatly enhanced computational ability
due to the parallelism of their circuitry. They have also proven themselves in fields
such as mapping, where minor errors are tolerable, there is alot of example-data, and
where rules are generally hard to nail-down. 
Educating neural networks begins by programming a backpropigation of error, which is the
foundational operating systems that defines the inputs and outputs of the system. The
best example I can cite is the Windows operating system from Microsoft. Of-course,
personal computers don't learn by example, but Windows-based software will not run
outside (or in the absence) of Windows. 
One negative feature of educating neural networks by backpropigation of error is a
phenomena known as, overfitting. Overfitting errors occur when conflicting information is
memorized, so the neural network exhibits a degraded state of function as a result. At
the worst, the expert system may lock-up, but it is more common to see an impeded state
of operation. By running programs in the operating shell that review data against a data
base, these problems have been minimalized. 
In the real world, we are seeing an increasing prevalence of neural networks. To fully
realize the potential benefits of neural networks our lives, research must be intense and
global in nature. In the course of my research on this essay, I was privy to several
institutions and organizations dedicated to the collaborative development of neural
network expert systems. 
To be a success, research and development of neural networking must address societal
problems of high interest and intrigue. Motivating the talents of the computing industry
will be the only way we will fully realize the benefits and potential power of neural
networks. 
There would be no support, naturally, if there was no short-term progress. Research and
development of neural networks must be intensive enough to show results before interest
wanes. 
New technology must be developed through basic research to enhance the capabilities of
neural net expert systems. It is generally 
acknowledged that the future of neural networks depends on overcoming many technological
challenges, such as data cross-talk (caused by radio frequency generation of rapid data
transfer) and limited data bandwidth. 
Real-world applications of these intelligent neural network expert systems include,
according to the Artificial Intelligence Center, Knowbots/Infobots and intelligent Help
desks. These are primarily easily accessible entities that will host a wealth of data and
advice for prospective users. Autonomous vehicles are another future application of
intelligent neural networks. There may come a time in the future where planes will fly
themselves and taxis will deliver passengers without human intervention. Translation is a
wonderful possibility of these expert systems. Imagine the ability to have a device
translate your English spoken words into Mandarin Chinese! This goes beyond simple
languages and syntactical manipulation. Cultural gulfs in language would also be the
focus of such devices. 
Through the course of Mind and Machine, we have established that artificial
intelligence's function will not be to replicate the conscious state of man, but to act
as an auxiliary to him. Proponents of Strong AI Thesis and Weak AI Thesis may hold out,
but the inevitable will manifest itself in the end. 
It may be easy to ridicule those proponents, but I submit that in their research into
making conscious machines, they are doing the field a favor in the innovations and
discoveries they make. 
In conclusion, technology will prevail in the field of expert systems only if the
philosophy behind them is clear and strong. We should not strive to make machines that
may supplant our causal powers, but rather ones that complement them. To me, these expert
systems will not replace man - they shouldn't. We will see a future where we shall
increasingly find ourselves working beside intelligent systems.