What are Expert Systems? How Can They Help You?
Every day, we use Artificial intelligence (AI) for numerous things ranging from day-to-day activities to complex tasks. The trend of AI has taken over the world; however, if you think it’s new, you might be mistaken. AI in the form of expert systems was pioneered in the 1970s by computer scientist and Harvard professor Edward Feigenbaum. In a 1988 manuscript, he states that the world was transitioning from ‘data processing’ to ‘knowledge processing’(1).
What this meant for the world of computers was that they could now do complex tasks rather than just simple calculations. We have taken giant leaps in AI, and understanding this evolution sheds light on the capabilities of expert systems and underscores the influence of early AI research. This helps in shaping our current and future technological landscape.
What is Meant by Expert Systems?
Expert systems are one of the most interesting branches of artificial intelligence. It is designed to mimic human decision-making abilities in a specific field or domain. When we talk about expert systems, we need to think of them as computerized advisors who can solve any problems related to a specific domain by mimicking human reasoning. They can achieve this because they use knowledge-based reasoning to solve any problem. Moreover, these systems typically consist of two main components:
1. Knowledge base: It is a big library of information and rules about a specific topic.
2. Inference engine: It is the ‘brain’ that applies logic and reasoning to the existing knowledge base and gives you a solution or answer to the topic.
These systems find widespread use in fields like medicine, finance, engineering, and customer service, where quick and informed decisions are crucial. For example, in medicine, a medical expert system might analyze symptoms and recommend potential diagnoses, helping doctors make informed decisions.
Additionally, what’s interesting is that these systems can continuously learn and improve over time, quite similar to what humans do. Although they are called ‘expert systems’, they do not possess the ability to replace human experts in any field. At the most, they would increase efficiency and accuracy, eliminating human error from the process.
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What are Examples of Expert Systems?
Here are 5 interesting examples of expert systems that you should know.
1. MYCIN
One of the earliest examples of expert systems is MYCIN. Researchers developed it in the 1970s for the medical field to diagnose infectious diseases in patients. MYCIN is used to analyze patients based on their symptoms and medical history and to recommend suitable antibiotic treatments for the same. Although it wasn’t widely used in practice, it laid a solid foundation for expert systems in the medical field.
2. DENDRAL
DENDRAL is another important example of an expert system in chemistry. This system helps chemists to identify molecular structures with the help of mass spectrometry data. DENDRAL analyzes chemical information and generates possible molecular structures for the said chemical. This makes it a very valuable tool in the chemistry domain.
3. CLIPS
NASA developed CLIPS software to create expert systems by defining rules and facts. Furthermore, it made it flexible for various industries to inculcate this and build their own expert systems.
4. XCON
Digital Equipment Corporation developed XCON to assist with computer system configuration orders. In essence, it ensures that the right parts are selected and assembled correctly. This speeds up the manufacturing process.
5. Prospector
Prospector is an expert system designed for geological purposes like mineral exploration. It uses geological data to help geologists analyze sites for mineral exploration. It does this by looking at the rock types and mineral contents present in the rocks. With Prospectors, the data becomes more accurate, and the chances of success become high since the exploration is done with tech. Moreover, looking at the monetary benefit, it also becomes cost-efficient.
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Is ChatGPT an Expert System?
To answer simply, ChatGPT is not an expert system. However, it is crucial to note that it does share some similarities with expert systems. Essentially, expert systems solve problems in specific fields by mimicking human reasoning based on their knowledge base.
In contrast, ChatGPT is an AI language model trained on various texts and sources. It predominantly generates human-like text and engages itself in conversations ranging from multiple topics and domains. It does not necessarily have expertise in one particular domain, it acts more like a ‘jack of all trades.’ Although it is important to note that it can provide answers to your questions—it doesn’t operate on a specific set of rules or structured knowledge base. On the other hand, it relies on patterns of data to generate responses for you. Therefore, it is safe to say that even though ChatGPT is impressive, it doesn’t serve the purpose of an expert system.
What is the Expert System Method?
The expert system method refers to a structured approach used to develop expert systems. This structured approach has six key steps, which are as follows:
1. Knowledge Acquisition
The very first step for building expert systems is to gather ‘knowledge’ i.e. information. You can gather this information through interviews, questionnaires, observations, etc. The aim of this remains to identify and understand the rules, facts, and heuristics. This is what experts use to come to an informed decision regarding a topic.
2. Knowledge Representation
After collecting and storing the knowledge, you must organize it in a way that the computer can understand and process it effectively. Some of the common methods for this include rule-based conditional systems like ‘if-then’ statements, frames, and semantic networks. Moreover, this representation captures the essence of relationships between different concepts and the logic that takes charge for the decision-making.
3. Inference Engine Development
The inference engine is the ‘brain’ of these expert systems. This is the core that applies logic to the knowledge and derives conclusions or gives you solutions according to your needs. To browse the knowledge base and draw conclusions, it employs two techniques: goal-driven forward chaining and data-driven backward chaining.
4. User Interface Design
User interface design, as the name suggests, is the UI design for the expert system. An effective and efficient UI is crucial for the end user. It should allow users to input data effortlessly and receive output comprehensively. This interface bridges the gap between the end user and the system.
5. Testing and Validation
Once the UI design is in place, developers must thoroughly check, test, and validate the expert system to ensure it gives accurate and expected results. This step before deployment involves comparing its outputs with those of human experts and modifying the knowledge base if needed.
6. Maintenance and Updating
After the deployment, the only thing that keeps the expert system ‘expert’ in that domain is its constant updation and maintenance. It improves its performance, and recieves crucial feedback from end users.
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In conclusion, the principles behind expert systems will always be at the forefront of influencing the development of AI. While models like ChatGPT offer versatility, they serve a different purpose than expert systems. If you want to learn more about expert systems, you can explore Emeritus’ Artificial Intelligence and Machine Learning courses and take a deeper plunge into the world of AI. Join Emeritus and be future-ready as you embark on your AI and ML journey.
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