.. emanding 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. Sociology.