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
- [[Properties+Capabilities|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
- [[Properties+Capabilities|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
- [[Properties+Capabilities|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]]