stem/AI/Properties.md
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Three Key Components

  1. Representation
    • Declarative & Procedural knowledge
    • Typically human-readable symbols
  2. Reasoning
    • Ability to solve problems
    • Express and solve range of problems and types
    • Make explicit and implicit information known to it
    • Control mechanism to decide which operations to use if and when, when a solution has been found
  3. Learning

An AI system must be able to

  1. Store knowledge
  2. Apply knowledge to solve problems
  3. Acquire new knowledge through experience

!ai-nested-subjects.png

Expert Systems

  • Usually easier to obtain compiled experience from experts than duplicate experience that made them experts for network

Information Processing

Inductive

  • General patterns and rules determined from data and experience
  • Similarity-based learning

Deductive

  • General rules are used to determine specific facts
  • Proof of a theorem

Explanation-based learning uses both

Classical AI vs Neural Nets

Level of Explanation

  • Classical has emphasis on building symbolic representations
    • Models cognition as sequential processing of symbolic representations
  • Neural nets emphasis on parallel distributed processing models
    • Models assume information processing takes place through interactions of large numbers of neurons

Processing style

  • Classical processing is sequential
    • Von Neumann Machine
  • Neural nets use parallelism everywhere
    • Source of flexibility
    • Robust

Representational Structure

  • Classical emphasises language of thought
    • Symbolic representation has quasi-linguistic structure
    • New symbols created from compositionality
  • Neural nets have problem describing nature and structure of representation

Symbolic AI is the formal manipulation of a language of algorithms and data representations in a top-down fashion

Neural nets bottom-up

!ai-io.png