Categories of AI

 

 

   In this blog page,we learn more about the categories of AI 

Categories of AI :-

AI divides roughly into two schools of thought:

• Conventional AI.
• Computational Intelligence (CI).

Conventional AI :-

Conventional AI mostly involves methods now classified as machine learning, characterized by formalism and statistical analysis. This is also known as symbolic AI, logical AI, neat AI and Good Old Fashioned Artificial Intelligence (GOFAI).

• Methods include:

Expert systems: apply reasoning capabilities to reach a conclusion. An expert system can process large amounts of known information and provide conclusions based on them.

Case based reasoning

Bayesian networks

Behavior based AI: a modular method of building AI systems by hand. 

Computational Intelligence (CI) :-

Computational Intelligence involves iterative development or learning (e.g. parameter tuning e.g. in connectionist systems). Learning is based on empirical data and is associated with non-symbolic AI, scruffy AI and soft computing.

• Methods include:

Neural networks: systems with very strong pattern recognition capabilities.

Fuzzy systems: techniques for reasoning under uncertainty, has been widely used in modern industrial and consumer product control systems.

Evolutionary computation: applies biologically inspired concepts such as populations, mutation and survival of the fittest to generate increasingly better solutions to the problem. These methods most notably divide into evolutionary algorithms (e.g. genetic algorithms) and swarm intelligence (e.g. ant algorithms).