TOPIC 7 : MODERN TRENDS

7.1 The Artificial Intelligence Field

	Artificial intelligence (AI) is a field of study that explores how computers can be used for tasks that require the human characteristics of intelligence, imagination, and intuition. Computer scientists sometimes prefer a looser definition, calling AI the study of how to make computers do things that - at the present time - people can do better. The phrase “at the present time” is significant because artificial intelligence is an evolving science: As soon as a problem is solved, it is moved off the artificial intelligence agenda. A good example is the game of chess, once considered a mighty AI challenge. But now that most computer chess programs can beat most human competitors, chess is no longer an object of study by scientists and thus no longer on the artificial intelligence agenda.

	Today the term artificial intelligence encompasses several subsets of interests:

•	Problem solving. This area of AI includes a spectrum of activities, from playing games to planning military strategy.
•	Natural languages. This facet involves the study of the person/ computer interface in unconstrained English language.
•	Fuzzy logic. This is a type of artificial intelligence that allows machine to “think” more like humans, providing a method of precisely measuring vague concepts. 
•	Expert systems. These AI systems present the computer as an expert on some particular topic. 
•	Robotics. This field involves endowing computer-controlled machines with electronic capabilities for vision, speech and touch.

Early Mishaps

Simulating intelligence is far more complex then just stuffing facts into the computer. Facts are useless without the ability to interpret and learn from them.
	An artificial intelligence failure on the grand scale was the attempt to translate human languages via the computer. Although scientists were able to pour vocabulary and rules of grammar into the computer, the literal word-for-word translations often produced ludicrous output. But still the scientists continued to explore artificial intelligence, focusing on how computers learn.

How Computers Learn

The study of artificial intelligence is predicated on the computer’s ability to learn - and to improve performance based on past errors. The two key elements of this process are called the knowledge base and the inference engine. A knowledge base is the set of facts and rules about those facts. An inference engine accesses, selects, and interprets a set of rules. The inference engine applies the rules to the facts to make up new facts - thus the computer has learned something new. Consider this example:

FACT: Barbara is George’s wife.
RULE: If X is Y’s wife, then Y is X’s husband.

	The computer - the inference engine - can apply the rule to the fact and come up with the new fact: George is Barbara’s husband. (Although the result of this simplistic example may seem of little value, it is indeed true that the computer now knows two facts instead of just one.)

	Rules, of course, can be much more complex and facts more plentiful, yielding more sophisticated results. In fact, artificial intelligence software is capable of searching through long chains of related facts to reach a conclusion - a new fact.

The Artificial Intelligence Debate

	To imitate the functioning of the human mind, a machine with artificial intelligence would have to be able to examine a variety of facts, address multiple subjects, and devise a solution to a problem by comparing new facts to its existing storehouse of data from many fields. So far, artificial intelligence systems cannot match a person’s ability to solve problems through original thought instead of using familiar patterns as guides.

	Experiments have shown that people rarely solve problems using step-by-step logic, the very thing the computer do best. Most modern computers still plod through problems one step at a time. The human brain beats computers at “Aha!” problem solving, because it has millions of neurons working simultaneously. Now some scientists are talking approach with computers.

Brainpower: Neural Networks

A microprocessor chip is sometimes referred to as the “brain” of a computer. But, in truth, computers have not yet come to matching the human brain, which has trillions of connections between billions of neurons. What is more, the most sophisticated conventional computer does not “learn” the same way the human brain learns. But let us consider an unconventional computer, one whose chips are actually design to mimic the human brain. These computers are called neural networks, or , simply, neural nets.

At best, today’s neural networks consists of only a few thousand connections - still far from the billions found in the human brain.

The Famous Turing Test

So, can a computer think or not? In the Turing test, a human subject is seated before two terminals that are connected to hidden devices. One terminal is connected to a terminal run by another person, and the second terminal is connected to a computer. The subject is asked to guess, by carrying out conversations through the terminals, which is the person and which is the computer. If the human judge cannot tell the difference, the computer is said to have passed and is considered as a thinking machine. 
	But perhaps we are asking the wrong question: Will a computer ever really think ? One possible answer: Who cares? If a machine can perform a task really well, does it matter if it really thinks ? Still another answer is : Yes, machine will really think, but not as humans do. They lack the sensitivity, appreciation, and passion that are intrinsic to human thought.

7.2 The Natural-Language Factor

	The language people use a daily basis to write and speak is called a natural language. Natural languages are associated with artificial intelligence because humans can make the best use of artificial intelligence if they can communicate with the computer in natural language. Furthermore, understanding natural language is a skill thought to require intelligence. 
	Some natural-language words are easy to understand because they represent the definable item: horse, chair, mountain. Other words, however, are much too abstract to lend themselves to straightforward definitions: justice, virtue, beauty.

	Natural language abounds with ambiguities. A key function of the AI study of natural languages is to develop a computer system that can resolve ambiguities.
	Feeding computers the vocabulary and grammatical rules it needs to know is a step in the right direction. However, true understanding requires more: Words must be taken in context. Humans begin acquiring a context for words from the day they are  born. 

7.3 Fuzzy logic

	What is “fuzzy” about fuzzy logic ? As humans, we deal with many vague notions in arriving at decisions. In contrast, machine intelligence is very precise. Machine intelligence is like classical logic - is based on either/or thinking.
	Fuzzy logic expands AI thinking so it more closely resembles the way the human deals with the gray areas of rationalization. Fuzzy logic was invented more than 25 years ago and has been used successfully in electronically controlled devices ranging from vacuum cleaners and automobile transmission to subway systems and rocket docking systems. It is the logic underlying a video camera’s ability to steady an image even when the camera is jostled during filming. In a washing machine, fuzzy logic assesses the size and untidiness of the load, then decides how long to run. It also helps guide self-guidance systems in robots.

7.4 Expert Systems

	An expert system is a software package used with an extensive set of organized data that presents the computer as an expert on a particular topic. For example, a computer could be an expert on where to drill oil wells, or what stock purchase looks promising. The expert system works by figuring out what the question means, then matching it against the facts and rules that it “knows”. These facts and rules, which reside on disk, originally come from the human expert.

	For years, expert systems were no more than bold experiments, the exclusive property of the medical and scientific communities. In the early 1980s, expert system began to make their way into commercial environment.

Expert System in Business

•	Using an expert system to extend customer credit limits. For example, a customer wants to charge a shirt whose cost pushes the total debt beyond the current credit limit. In the past, a salesperson phones a human expert who reviewed credit records and made the decision. The decision process has been transferred to an expert system, which is faster, less expensive, and accomplishes the same job.
•	Using an expert system to assemble electrical connectors for airplanes. In the past, workers had to hunt through 20,000 pages of cross-referenced specifications to find the right parts, tools, and techniques for the job - approximately 42 minutes per search. The expert system lets them do the same thing in about 5 minutes.

The cost of an expert system can usually be justified in situations where there are few experts but great demand for knowledge. It is also worthwhile to have a system that is not subject to human failings, such as fatigue. 

Building an Expert System

	Some organizations choose to built their own expert systems to performs well-focused tasks that can be easily crystallized into rules. A simple example is a set of rules for a banker to use when making decisions about whether to extend credit. But very few organizations are capable of building an expert system from the scratch. This sensible alternative is to buy an expert shell, a software package that consists of the basic structure used to find answers to questions. It is up to the buyer to fill in the actual knowledge on the chosen subject.

	The most challenging task of building an expert system often is deciding who the appropriate expert is and how to pin down his knowledge. The person ferreting out the information is called a knowledge engineer and he must have the keen eye and the skills of a diplomat. 

Once  the rules are uncovered, they are formed into a set of IF-THEN rules. 

IF the customer has exceeded a credit limit by more than 20% 
     AND  has paid the monthly bill for six months
THEN extend further credit

	After the system is translated into a computerized version, it is reviewed, changed, tested, and changed some more. This repetitive process can take months or even years. Finally, it is put into the same situations as the human expert would face, where it should give equal or better service much more quickly.

The Outlook for Expert Systems

	Some industry analysts feel that expert systems are beginning to mimic the analytic processes of humans and that, as a result, these programs border on true artificial intelligence. A computer having artificial intelligence should understand the facts it knows, come up with new facts, and be able to engage in a wide-ranging conversation about them in a natural language. By these standard, expert systems today are still dim-witted. Furthermore, each system has intelligence only in one specific area.

	Expert systems will infiltrate companies department by department, much as personal computers did before them. Some expert systems are now available on personal computers. The main limitation of using an expert system on a personal computer is that the expert system requires a substantial amount of memory. In addition, a large amount of rules, facts, and program code must be stored, requiring the use of the hard-disk. Even so, it seems likely that more expert systems for personal computer will appear in the near future.

7.5   Robotics

Robots in the Factory

	Most robots are in factories, spray-painting  and welding - and taking away jobs. A loose definition of robot is a type of automation that replaces human presence. But a robot is more completely defined as a computer-controlled device that can physically manipulate its surroundings. There wide variety of sizes and shapes of robots, each designed with a particular use in mind. Often these uses are functions that would be tedious or even dangerous for a human to perform. The most common industrial robots today a re mechanical devices with five of six axes of motion, so they can rotate into proper position to perform their tasks. A more intelligent robot can adapt to changing circumstance. For example, with the help of a TV-camera eye, a robot can see components it is meant to assemble. It is able to pick them up, rearrange them in the right order, or place them in the right position before assembling them.

Robot Vision

	Recently, vision robots have been taught to see in living color - that is, to recognize multicolored objects solely from their colors. Before this, robots recognize objects by their shapes and from vision machines that “see” only a dominant color. 

Field Robots 

	Field robots do some dangerous and dirty jobs. These days, robots “in the field” inspect and repair nuclear power plants, dispose of bombs, inspect oil rigs for undersea exploration, clean up chemical accidents, and much more. Space researchers look forward to the day when  “astrobots” can be stationed in orbit, ready to repair faulty satellites. 
	Field robots have been largely overshadowed by factory robots, mainly because they lack the independent glamour of their manufacturing counterparts; field robots must be remotely controlled by human operators. Now, they have the poor relative status of filed robots is changing because enough computer power can be packed into a field robot to enable it to make most decisions independently. Unlike factory robots, which are bolted to the ground and blindly do the same tasks over and over again, field robots must contend with a highly unstructured environment.

7.6   Controlling Your Destiny

	The immediate prospects for expert systems and robots in growth and  more growth. We can anticipate both increased sophistication and more diverse applications. The progress in the more esoteric applications of artificial intelligence will continue to be relatively slow. No one need worry that any computer can capture the wide-ranging sophistication of  a human just yet. People are in charge of computers, not the other way around.
	In the Information Age, understanding computers and learning to use them will help us manage out lives more profitably. In the past, managing our daily existence has had more to do with how we handle relationships with people than anything else. In the future, our relationships with technology will become just as important.