# 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]]