Artificial intelligence expert system is one of the most prominent domains of research in the tech world today. Before we delve into the topic, let us first understand what artificial intelligence is.
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Artificial intelligence or AI is a branch of computer science that deals with making machines perform the tasks that typically require human intelligence. This interdisciplinary field of research was born in a Dartmouth College workshop in 1956. And over the years, AI has gained importance in virtually every sector of the technology industry. It is creating ripples and bringing a paradigm shift in workplaces worldwide. Artificial Intelligence Salary is one of the highest in the industry. Learn more about the applications of artificial intelligence.
If we look around, we can spot various applications of AI in our everyday lives. Chatbots like Microsoft’s Cortana, Apple’s Siri, and Amazon’s Alexa are some of the most common examples. Ride-sharing apps like Uber use AI to estimate your time of arrival at your destination, and e-commerce websites use it to recommend products based on your browsing history.
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AI-based chatbots are proving extremely helpful for businesses in the service industry as they take care of the routine tasks like answering queries, stimulating conversations, scheduling appointments, taking orders, billing etc. Even banks and financial institutions have adopted AI-based systems to serve their customers better.
Read: How to make chatbot in Python?
HDFC bank’s EVA (Electronic Virtual Assistant) can answer questions in less than 0.4 seconds after sifting through thousands of knowledge sources! Learn more about the significant benefits of artificial intelligence.
How does AI work?
AI combines large amounts of data and further processes it using iterative and intelligent algorithms. Then, the program automatically learns from the data features, patterns, and stored information – just like humans learn from their experiences.
Take the example of SIRI, which is an intelligent, voice-based personal assistant application. It can identify a user’s voice and instructions through natural speech processing. The application of AI comes into the picture when the software can learn and recall the user’s preferences over time.
So, AI-powered systems are slowly shaping the society that we live in and how we interact. Now, let us move on to an expert system in artificial intelligence.
Read: If you are a beginner and looking for AI project topics and ideas.
Defining Expert Systems in Artificial Intelligence
Ever since the time of mathematician Alan Turing, the fundamental question has been “Can machines think like humans?” Equipping machines to mimic certain human tasks and decision-making abilities are the primary goal of AI as well.
An expert system is a computer application that can think like an expert and solve complex problems related to a specific field. Just like qualified professionals use their knowledge and experience to give advice, an expert system is a computer-based system that uses both facts and heuristics to provide solutions.
AI expert systems are highly reliable as they are reasoning-based, understandable, responsive, and high-performance. So, they exhibit intelligent behaviour and expertise in a distinct area. The Computer Science Department at Stanford University, USA, introduced this research domain of AI.
Expert System in Artificial Intelligence: Applications
The Pathfinder system built by Stanford researchers in 1980 is the first expert system approved by the American Medical Association. It can diagnose more than 100 symptoms and over 60 diseases, including lymph-node diseases.
More recent examples of expert systems used in the medical field include MYCIN, DENDRAL, DXplain, CaDet, etc. The expert systems ROSS is commonly used in business settings. It is a self-learning system that mimics the human brain using techniques like data mining, deep learning, pattern recognition, and natural language processing.
Mainly, any artificial intelligence expert system carries out the following activities:
- Interpretation: Applying high-level intelligence to draw conclusions based on data
- Prediction: Charting out probable outcomes and making projections
- Designing: Determining the best configurations, arrangements, and combinations based on set criteria
- Planning: Helping the achievement of organizational goals by proposing a series of actions
- Monitoring: Observing actual outcomes and comparing them with standard or expected behaviour
- Repairing: De-bugging, suggesting and implementing remedial steps
- Controlling: Undertaking overall governance of a system
- Diagnosis: Finding out causes of malfunction and detecting diseases
- Teaching: Aiding instruction and helping students in learning
AI Expert System: Components
In this section, we will understand how an expert system in artificial intelligence works. Typically, a rule-based system has components like Knowledge Base, Working Memory, Inference Engine, Explanation System, User Interface, and Knowledge Editor.
Knowledge is the foundation of an expert system. Just like human beings acquire know-how from observation, books and education and develop their skills through practice and experience, AI enables machines to gather knowledge and emulate human intelligence. An expert system collects data, information, and experience to create a knowledge base.
This involves extracting and data from various sources. The knowledge is then organized and formalized in the form of ‘If-Then-Else’ rules. Instead of merely using conventional procedural code, it is about selecting the most suitable structures to represent the knowledge. After this, the knowledge is tested to ensure that it is complete and accurate. So, designing an expert system involves three critical steps of knowledge acquisition, representation, and validation.
Next, the Inference Engine fetches and manipulates knowledge from this base to arrive at a particular solution. It repeatedly applies rules to the facts derived from earlier rule applications, adds new knowledge to the database whenever required, and also resolves conflicts among multiple rules. To do this, it uses the strategies of Forward and Backward Chaining.
Forward Chaining means going through facts and sorting them out to determine what can happen next. In this case, the expert system follows a chain of conditions to derive a solution. Such an inference engine strategy is useful for predicting market prices of stocks and property rates. As for Backward Chaining, the inference engine tries to answer why something could have happened in a particular domain. For example, this method can be helpful in medical diagnoses.
Therefore, the typical architecture of an expert system in AI includes a knowledge base and inference engine. A user interface allows a non-expert to interact with the system.
Like any technology branch, an artificial intelligence expert system has pros as well as cons. Some of them are discussed below.
Advantages of AI Expert Systems
- An expert system in AI is capable of storing and maintaining a vast knowledge base. So, holding a large amount of information is one of the pros of AI expert systems.
- Expert systems centralize decision-making and avoid delays by eliminating lengthy, time-consuming procedures.
- Since the expert system can solve complex problems in a particular domain on its own, there is no need to provide specialised training to employees. This results in cost-saving for the business.
- AI expert systems also help businesses in achieving efficiencies. They bring down the time and costs and bring in more precision by minimizing errors.
- An AI expert system takes care of repetitive tasks, giving more time to the staff for core decisions and functions. It also smoothens out routine decision making by removing emotions from the equation.
- Making decisions with the help of expert systems can serve as a competitive advantage. Quick decision making and problem-solving are especially beneficial for functioning in a fast-paced industry.
- An AI expert system can combine expertise in multiple areas. It is difficult to find the right ‘human’ fit with such a knowledge base and intelligence.
Disadvantages of Expert Systems
- Machines, as we know them today, cannot provide creative answers. Humans get a unique ingredient to the table in the form of creativity and innovation, which AI expert systems lack.
- Unlike a human instructor, expert systems may not be able to explain the logic after concluding a solution to a specific problem.
- An ES may not recognize the situations when there is no answer. It also lacks common sense.
- AI is extremely beneficial in automating routine tasks but poses a limitation when dealing with complex processes.
- An ES may not be as robust in adapting to fast-changing environments as humans.
- Expert systems are not free from errors. They can provide inaccurate predictions based on wrong input data. The performance of the system is highly dependent upon precise knowledge acquisition.
- The maintenance costs of an AI-based system can be high, especially for organizations that are just starting out.
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Concluding Thoughts
AI is continuously evolving as a research field. Therefore, it has become essential to keep learning about its technical approaches and advancements. By understanding the concept of the expert system in artificial intelligence, you can be one step ahead!
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