Difference between AI and Expert System

Artificial Intelligence: AI manages more comprehensive issues of automating a system. This computerization should be possible by utilizing any field such as image processing, cognitive science, neural systems, machine learning, etc. AI manages the making of machines, frameworks, and different gadgets savvy by enabling them to think and do errands as all people generally do. Expert System: An expert system is an AI software that uses knowledge stored in a knowledge base to solve problems that would usually require a human expert thus preserving a human expert’s knowledge in its knowledge base. They can advise users as well as provide explanations to them about how they reached a particular conclusion or advice.

Let’s see the difference between AI and Expert systems:

Artificial Intelligence	Expert System
AI is the ability of a machine or a computer program to think, work, learn and react like humans.	Expert systems represent the most successful demonstration of the capabilities of AI.
AI involves the use of methods based on the intelligent behavior of humans to solve complex problems.	Experts systems are computer programs designed to solve complex decision problems.
Characteristics of AI- Facial Recognition Automate Simple and Repetitive Tasks Chatbots Natural language processing Imitation Of Human Cognition Deep Learning Cloud Computing	Characteristics of Expert System- High Efficiency and Accuracy Highly responsive Understandable Reliability
Components of AI: Natural Language Processing (NLP) Knowledge representation Reasoning Problem solving Machine learning	Components of Expert System: Inference engine Knowledge base User interface Knowledge acquisition module
AI is the study is systems that act in a way to any observer would appear to be intelligent.	Expert system represent the most successful demonstration of the capabilities of AI
AI systems are used in a wide range of industries, from healthcare to finance, automotive, data security, etc.	Expert systems provide expert advice and guidance in a wide variety of activities.
Categories of Problems that can be solved- Look for trends, patterns, and connections. Look for inefficiencies. Result forecasting on the basis of historical trends Make informed decisions based on facts Put plans into action. Improve yourself by learning new things.	Categories of Problems that can be solved- Using classification and diagnosis object is identified based on stated qualities. To exemplify, Medical condition diagnosis Monitoring entails comparing data to recommended behavior on a regular basis. Prediction: For example, forecasting the state of the stock market. Configuring a system according to standards is known as design.
Applications- E-Commerce Education Lifestyle Navigation Robotics Human Resource Healthcare Gaming and others	Applications- Hospitals  Medical facilities Help desks management Loan analysis Warehouse optimization Stock market trading Airline scheduling & cargo schedules and others
Examples- Natural Language Processing (NLP) tools, Proactive healthcare management, Automated financial investing, Virtual travel booking agents, Self-driving cars, Manufacturing robots, Conversational marketing bots, and others.	Examples- DENDRAL, MYCIN, and others.

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