Expert systems are software programs that interact with a database of information gathered by human experts. They are critical components of functional AI, which can be developed using different methodologies such as fuzzy logic programming. Watson computer is an example of AI and expert systems at work. The next generation of AI is built using heuristics, allowing it to grow and learn over time.
Artificial intelligence (AI) and expert systems are related as the development of artificial intelligence is usually based on a number of expert systems. Expert systems are software programs that interact with a database of information gathered by human experts with different viewpoints and inference engines to quantify and analyze it. In order for artificial intelligence and expert systems to work together seamlessly and mimic the capabilities of human thinking, they are often built on an array of microprocessors. These processors work in parallel to analyze and compare stored data with real-world data and yield meaningful results in a reasonable amount of time.
A good example of artificial intelligence and expert systems at work is the Watson computer created by the IBM® company in the USA, over the course of three years. Watson is an internal networked computer system of 2,880 microprocessors and 16 Terabytes of RAM memory that processes 500 gigabytes of data per second to analyze human speech. This is the equivalent of being able to read and analyze 1,000,000 books every second. More than 100 different expert systems techniques are run on Watson to compile meaningful answers to questions. The system accesses data from contemporary encyclopedias, literature, and news articles, and uses neural networks and other adaptive expert systems software methods to understand a rudimentary artificial intelligence that finds meaning in human speech patterns.
However, AI programming can be based on a number of different design methodologies. General human intelligence AI systems, known as “strong AI,” are the ones that tap the most into the need for multiple expert systems running in tandem. One of the methods of developing artificial intelligence and expert systems in this way is the use of fuzzy logic programming, which is software that attempts to quantify the vague nature of the real world that humans are good at understanding, but computers digital no. Fuzzy expert systems work well where machines need to adapt to rapidly changing real-world conditions, such as in automatic transmissions in cars, dishwashers, cameras, nuclear power plants, and so on. Computer intelligence in Japan has made far more use of fuzzy logic programming than elsewhere, which may explain the nation’s ability to lead the market in advanced AI robotics.
Expert systems, therefore, are a critical component of any functional AI. Combined and expert systems attempt to work around the hurdles that traditional computers encounter, where every decision must include a distinct yes/no, true/false answer. They do this by processing queries dynamically instead of following a predetermined program path and weighing the values of each potential response against each other. Building AI and expert systems using heuristics, or the form of trial-and-error analysis that humans routinely use on a one-on-one conditional basis, instead of simply applying specific stored knowledge, is the next generation of AI that has the ability to grow and learn over time.
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