Major Subfields of AI - Knowledge Engineering

 Knowledge engineering is not an important subfield of AI per se, but a process that facilitates other areas such as expert systems. It deals with acquisition, modeling, and management of knowledge required to construct intelligent systems, and as such, constitutes an important part of developing AI applications such as expert systems, which apply the knowledge of a human expert to solve problems. 

What it entails: It involves the design and construction of AI systems, which involves encoding human expert knowledge into a form accessible to the computer.

Key tasks:

• Learning process: Obtaining information about human experts.
• Knowledge representation: Organizing and storing the data in a computer readable format.
• Knowledge deployment: Applying the knowledge to AI system problem solving.

Example applications:

• Expert Systems: An artificial intelligent (AI) application that emulates something that a person who is a professional expert in a given area would make, such as a financial advisor or a medical diagnosis system.
• Other AI applications: Knowledge engineering is also applied in other applications, including machine learning model development or natural language processing applications.

Skills involved:

• Information processing and categorizing.
• Natural language processing.
• Machine learning.
• Systems design.

Role in AI: Knowledge engineering gives knowledge that the AI systems require to be able to function, be intelligent agents, and make decisions.