Artificial Intelligence

Artificial Intelligence is the study of algorithms capable of performing human-like operations or simulating it.

There are three main types of AI:

  1. Artificial Narrow Intelligence (ANI), also called Weak AI, is a type of AI that is designed to perform specific tasks or solve specific problems. Examples include voice assistants, image recognition software used in security camera systems, and prediction tools.
  2. Artificial General Intelligence (AGI), also called Strong AI, refers to a system or machine that exhibits human-like intelligence and can perform any intellectual task that a human can do. AGI does not yet exist and is still purely theoretical.
  3. Artificial Superintelligence (ASI) refers to a hypothetical form of AI that surpasses human intelligence in every way. ASI is also theoretical and has not yet been developed.

Standard approaches to Artificial Intelligence:

  • Symbolic AI(GOFAI: Good Old-Fashioned AI): Collection of methods using high-level symbolic (human-readable) representations of cognitive problems, such as Knowledge Representation and Reasoning.

    Symbolic AI systems are based on rules and knowledge, and their inner processes and results are interpretable. They have high logic and reasoning capabilities and low learning capabilities.

  • None-Symbolic AI(Connectionist AI): Computational system inspired by the human brain leveraging high performance of current hardware for pattern recognition via mathematical, statistical or Artificial Neural Networks (ANN).

    None-Symbolic AI is difficult to understand and interpret, however they are have high learning capabilities(easy to train) and can perform well on narrow tasks given enough data and proper model development.

Interesting Branches, interdisciplinary and related topics:

  • Machine Learning: Most None-Symbolic AI solutions are Machine Learning.
  • Data Science: Used for gathering, managing, and preparing data for machine learning training process.
  • Knowledge Engineering: Knowledge Engineering is a subset of engineering methods and questions in artificial intelligence field that seeks to create systems that emulate the judgment, behavior, and expertise of human experts.

Learning Material: