CIS 5603. Artificial Intelligence
Overview of AI
1. Origin
In the 1940s, various computers were invented initially for numerical calculation, though they were also used in non-numerical computation or symbol manipulation, as anticipated in models like the Turing machine.
Several visionaries had noticed important similarities between the brain/mind and the machine:
AI as a research field started in 1956 at the Dartmouth meeting, and was strongly influenced by
John McCarthy,
Marvin Minsky,
Allen Newell,
Herbert A. Simon.
2. The big picture
The general goal of AI is to build computer systems that work like the human mind.
AI has a science aspect (related to cognitive science) and an engineering aspect. A complete AI project normally has three related levels:
- Theory
- Model
- Implementation
Basic questions:
- What is AI?
- Can AI be built?
- How to build AI?
- Should AI be built?
3. Working definitions
In which aspect an AI is like a human mind:
- [Structure]
Rationale: Human intelligence is produced by the human brain.
Background: Neuroscience, biology, etc.
Challenge: There may be biological details that are neither possible nor necessary to be reproduced in AI systems.
-
[Behavior]
Rationale: Human intelligence is displayed in how the human beings behave.
Background: Psychology, linguistics, etc.
Challenge: There may be psychological or social factors that are neither possible nor necessary to be reproduced in AI systems.
-
[Capability]
Rationale: Human intelligence is evaluated by problem-solving capability.
Background: Computer application guided by domain knowledge
Challenge: There is no defining problems of intelligence, and the special-purpose solutions lack generality and flexibility.
-
[Function]
Rationale: Human intelligence is associated to a collection of cognitive functionality.
Background: Computer science
Challenge: The AI techniques developed so far are highly fragmented and rigid, and it is hard for them to work together on novel problems.
-
[Principle]
Rationale: Human intelligence represents a form of rationality or optimality.
Background: Logic, mathematics, etc.
Challenge: There are too many things in intelligence and cognition to be explained and reproduced by a simple theory.
These approaches are related, though different. See a detailed discussion.
4. The agent framework
The mainstream opinion takes intelligence as a set of functions integrated in an intelligent agent framework, consisting of
- common substance: knowledge representation and organization
- cognitive facilities: problem solving, searching, reasoning, planning, decision making, learning, etc.
- interface with the environment: natural language (understanding and generating), perception (vision, hearing, touch, etc.), action, robotics
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