Mind And Machine Essay, Research Paper

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. Essentially, 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.