2023-12-22 16:39:03 +00:00
|
|
|
---
|
|
|
|
tags:
|
|
|
|
- ai
|
|
|
|
---
|
2023-05-22 17:32:00 +01:00
|
|
|
# Three Key Components
|
|
|
|
|
|
|
|
1. Representation
|
2023-06-06 11:48:49 +01:00
|
|
|
- Declarative & Procedural [knowledge](Neural%20Networks/Neural%20Networks.md#Knowledge)
|
2023-05-22 17:32:00 +01:00
|
|
|
- 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
|
|
|
|
|
2023-06-05 17:01:29 +01:00
|
|
|
![ai-nested-subjects](../img/ai-nested-subjects.png)
|
2023-05-22 17:32:00 +01:00
|
|
|
|
|
|
|
# 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
|
2023-06-06 11:48:49 +01:00
|
|
|
- [Neural nets](Neural%20Networks/Properties+Capabilities.md) emphasis on parallel distributed processing models
|
2023-05-22 17:32:00 +01:00
|
|
|
- Models assume information processing takes place through interactions of large numbers of neurons
|
|
|
|
|
|
|
|
## Processing style
|
|
|
|
- Classical processing is sequential
|
|
|
|
- Von Neumann Machine
|
2023-06-06 11:48:49 +01:00
|
|
|
- [Neural nets](Neural%20Networks/Properties+Capabilities.md) use parallelism everywhere
|
2023-05-22 17:32:00 +01:00
|
|
|
- Source of flexibility
|
|
|
|
- Robust
|
|
|
|
|
|
|
|
## Representational Structure
|
|
|
|
- Classical emphasises language of thought
|
|
|
|
- Symbolic representation has quasi-linguistic structure
|
|
|
|
- New symbols created from compositionality
|
2023-06-06 11:48:49 +01:00
|
|
|
- [Neural nets](Neural%20Networks/Properties+Capabilities.md) have problem describing nature and structure of representation
|
2023-05-22 17:32:00 +01:00
|
|
|
|
|
|
|
Symbolic AI is the formal manipulation of a language of algorithms and data representations in a top-down fashion
|
|
|
|
|
|
|
|
Neural nets bottom-up
|
|
|
|
|
2023-06-05 17:01:29 +01:00
|
|
|
![ai-io](../img/ai-io.png)
|