For software to provide an economic service, the software program need not contain any artificial intelligence whatsoever. For example, the software for an ATM machine enables the customer to select the desired service from a series of menus by pushing buttons. In many information services, computer programs based on menus could replace professionals, for example, travel agents, realtors, and those who provide simple legal services such as routine wills or divorces. Consider travel agents who collect commissions for using the airlines' software reservation systems to obtain tickets for their customers. The airlines are moving to simplify the use of their software for final customers, thus avoiding having to pay agents commissions. These reservation systems are available through information utilities. Over time the amount of service a computer could provide will increase. One impediment will be the monopoly power of the practitioners who will try to have the new software services declared illegal. The student should know that a legal monopoly element is prevalent in many services because the practitioner must be licensed by the government and the professional organization for the practitioners usually controls the licensing standards. For example, lawyers are not exactly happy about the prospect of legal software controlled by the user.
Expert or Knowledge-Based Systems: Obviously, the capability of software to provide economic services is enhanced by the incorporation of artificial intelligence. Let us first consider the advance of expert systems, which AI professionals prefer to call knowledge-based systems. These expert services in many cases are specialized information services which provide opinions or answers. The basis of an expert system is the knowledge base which is constructed by a knowledge engineer consulting with the expert. A rule of thumb is that if a problem takes less than twenty minutes for the expert to solve it is not worth the effort and if it takes over two hours it is too complicated. The knowledge engineer attempts to reduce the expert's problem solving approach to a list of conditional (if) statements and rules. The expert program provides a search procedure to search through the knowledge base in order to solve a problem. A particular problem is solved by entering the facts of the case. The knowledge incorporated in most expert programs is empirical not theoretical knowledge. Thus, this approach works best on problems which are clearly focused. Expert programs have had some market successes:
a. XCON of Digital: This expert program configures VAX computers for customers and makes fewer mistakes than humans.
b. Dipmeter advisor of Schlumberer: This expert program interprets readings from oil wells and performs as well as a junior geologist 90%of the time.
c. Prospector of the US Geological Service: This expert program found a major deposit worth $100M.
d. MYCIN of Stanford: This expert program can diagnose disorders of the blood better than a GP but not as well as an expert.
Before the mid80s AI was a research activity in universities. With the advent of the first commercial successes of expert systems a new industry was created. The new industry oversold the possibility of expert systems, and sold firms software packages with the mistaken idea that the firms could easily create the knowledge base for their applications themselves. The result was a fiasco which discredited the new industry. Critics claim that the AI types are overreaching themselves because the computer limitations imply an expert program is unlikely to be more than just competent and can not deal with new situations. This is the reason the AI types prefer to call expert systems knowledge based systems. Such systems in practice act as intelligent assistants not experts. They change the composition of work groups by replacing assistants and offer the possibility of new services. For example, knowledge based accounting systems reduces the need for junior accountants and enables accounting groups to answer what-if type questions for their clients.
From an economic prospective, if competency via an expert program is cheaper than competency via training humans, then the expert program industry will continue to grow. Once you have created an expert program, the cost of creating an additional copy is very low. The biggest success in expert systems is in the area of equipment maintenance programs. The use of expert systems continues to grow.
Other types of quasi-intelligent software: New types of quasi-intelligent software are neural networks and case-based reasoning. Neural networks are being used to spot credit card crooks, pick stocks, sort apples, and even drive trucks. A neural network is better at spotting credit card crooks than an expert program because the former can use many more variable than the latter. Expert systems used to spot credit card crooks tend to give off so many false alarms that they are of little use.
Case-based reasoning systems are natural language systems which employ a ``case base'' of previously solved problems. To solve a new problem the program searches for similar, previously solved cases and then adapts those solutions to the case at hand. Each new case is added to the case base. An important application is customer query systems. Quasi-intelligent software is being created which combines expert systems, neural networks and case-based reasoning with other types of software such as genetic algorithms( a powerful search tool for the best alternative), virtual reality, and multimedia.
The newest type of quasi-intelligent software is the concept of an intelligent agent. Intelligent agents are designed to perform tasks for their owner. For example, network agents could scan data bases and electronic mail, schedule meetings, and help with travel arrangements. Clerical agents in offices could answer phones, tap into computers for customer data and send faxes.