AI Knowledge Map: How To Classify AI Technologies

An effort to draw an architecture to access knowledge on AI and follow emergent dynamics – the AI Knowledge Map (AIKM).

On the axes, you will find two macro-groups, i.e., the AI Paradigms and the AI Problem Domains. The AI Paradigms (X-axis) are the approaches used by AI researchers to solve specific AI-related problems (it includes up to date approaches). On the other side, the AI Problem Domains (Y-axis) are historically the type of problems AI can solve. In some sense, it also indicates the potential capabilities of an AI technology.

  • Logic-based tools: tools that are used for knowledge representation and problem-solving
  • Knowledge-based tools: tools based on ontologies and huge databases of notions, information, and rules
  • Probabilistic methods: tools that allow agents to act in incomplete information scenarios
  • Machine learning: tools that allow computers to learn from data
  • Embodied intelligence: engineering toolbox, which assumes that a body (or at least a partial set of functions such as movement, perception, interaction, and visualization) is required for higher intelligence
  • Search and optimization: tools that allow intelligent search with many possible solutions.

The vertical axis instead lays down the problems AI has been used for, and the classification here is quite standard:

  • Reasoning: the capability to solve problems
  • Knowledge: the ability to represent and understand the world
  • Planning: the capability of setting and achieving goals
  • Communication: the ability to understand language and communicate
  • Perception: the ability to transform raw sensorial inputs (e.g., images, sounds, etc.) into usable information.

Read more here: AI Knowledge Map: How To Classify AI Technologies

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