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
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
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
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
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
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
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
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
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)
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
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