Expert system in “artificial intelligence” is becoming popular in the technological sector, and for good reasons. Typically, the name artificial intelligence connotes the artificial intelligence of a machine. In the study of computers, machine intelligence is another name for artificial intelligence (AI). Human intelligence refers to the knowledge a person possesses, the same as Artificial Intelligence refers to the ability a machine can exhibit.
What is an Expert System in AI?
This area of AI has been studied extensively by researchers at Stanford University’s Computer Science Department. It is a computer program that can resolve the most challenging issues in any field. Based on information learned from an expert, it is regarded as having the highest level of human intelligence and expertise.
Another definition of an expert system is a computer-based decision-making system that can resolve complex decision-making issues by combining heuristics and facts. An expert system’s primary goal is to reproduce the knowledge and skills of human experts in a specific field by learning from them. The system will then employ these abilities to solve challenging issues in that particular field without the involvement of human expertise.
What are the characteristics of Expert systems?
- Great performance
- Understandable
- Reliable
- Highly receptive
How to implement an Expert System?
A growing number of managers want to know if and how they can use expert systems if they are the perfect fit. Those looking to use the new technology can pick up tips from early adopters of sizable, commercial expert systems. Their experience demonstrates that expert systems are valuable for frequently gathering and sharing skills and knowledge to gain a competitive advantage. However, prospective users could be delighted to learn that, despite the interest in using the new technology for less mundane chores like credit verification or capital budgeting analysis, some of the most significant opportunities for expert systems lay in these smaller, more routine jobs.
From computers to accounting, expert systems (ESs) have been successfully applied to various industries’ design, diagnosis, and monitoring.
According to studies, experts can rarely retrace the analytical steps needed to reach a specific choice. Although they frequently cannot convey the entire process, they may be able to emphasize significant variables that affect a decision. They need to be more evasive on purpose. Experts can intuitively and deeply understand a great deal of information, yet they cannot express it.
The ability to share and protect knowledge once encoded in code is another advantage of ES. An ES can assist as a training tool if a human expert gets hired elsewhere or is too busy to instruct younger colleagues.
People can consult with an ES in private if they are hesitant to ask questions because they feel that they should already know the answers. And by observing how another expert approaches the task, the experienced person can gain insight. In a sense, many individuals can learn from a select group of professionals.
Lastly, creating an ES is a step toward artificial intelligence. In addition to the few available turnkey systems, most systems are custom-built.
It is alluring to imagine a thinking machine. ES developers are frequently drawn to glitzy programs. However, the more routine chores appear among the most fertile fields for new applications, according to the observation of dozens of systems being developed and used. Numerous vital but ordinary or dull occupations exist in business and government. Contracting, negotiating, and auditing are commonplace business activities that offer exciting prospects.
A “standard” ES application is occasionally mentioned; however, this is frequently an oxymoron. A tailored strategy is typically required when a business needs the expertise to address a specific issue. As a result, implementing an ES often involves more of a process of spreading a viewpoint or set of beliefs than offering a fix for a problem.
After identifying the ES opportunities, you must determine whether using the new technology to complete those tasks is practical.
Consider the size of the issue, for example. A suitable candidate can be resolved within a few minutes or hours. Days to fix problems are too ambitious. Consider whether there are terms that can adequately convey the issue. Can you solve it over the phone? An ES may not be the best choice for the assignment if the expert must see or touch the data.
Determine whether you can pinpoint the skills that set experts apart from novices as a third feasibility test. Making the distinction is essential because you need to understand who specialists should be involved in developing the system and what standard to use when evaluating its performance.
How to meet the implementation challenge?
The creation and application of an ES resemble new technology development in many aspects. All new technologies carry some technical risks, call for organizational changes, and demand skilled perception management to prevent unrealistic expectations from developing in potential users. Of course, management’s dedication is crucial. Start the development process now if the possibility is viable and an ES can be implemented.
Expert systems can be utilized as an extension of a subject matter expert, freeing up the expert to focus on more difficult problem-solving, or they can be delegated to a less-experienced person.
How to manage expectations?
ESs only sometimes produce accurate results. People frequently have much greater expectations for a computer program’s performance than they would ever have for a human expert, yet these unrealistic expectations are a recipe for disaster. Consistency from an ES is expected; however, ESs imitate both the weaknesses and the virtues of human judgment.
In addition, determining whether a response is “right” is frequently subjective. Both in computer configuration and credit authorization, this is true. Numerous combinations will function, but selecting the ideal one requires discretion.
Take Away
The range of potential advantages and disadvantages of man-machine systems is yet unknown to us, but we are starting to understand them. As we’ve seen, expert systems have already enabled beginners to carry out jobs often reserved for experts. They have occasionally assisted firms in comprehending crucial procedures and methods.
The artificial intelligence and ESs that come after this could be even more potent and valuable. The management of the technology, as much as the technology itself, will determine whether the potential of this increasing mix of human and machine knowledge is realized.
