1. What is AI?

2. AI Sources Philosophy Mathematics Psychology Economics Linguistics
Neuroscience
Control theory

3. Approaches: What does in mean to be AI?
THINK like HUMANS
THINK rationally
ACT like HUMANS
ACT rationally

4. History of the field
1870’s - Ada wrote that machines might compose music and do other creative things – but not think.
1943 McCulloch & Pitts – a neural model
1948 Cybernetics (Norbert Wiener)
1950 Turing’s “Computing Machinery and Intelligence” The Test
1950’s Early AI
Samuel’s checkers
Newell and Simon’s Logic Theorist
Gelernter’s Geometry Engine
1956 Dartmouth meeting

5. History (cont.) 1962 Perceptrons (Rosenblatt)
AI encounters computational complexity
Early knowledge based approaches
Expert Systems and the Japanese 5’th Generation – boom years
Expert System bust – “AI winter”
Return of neural systems
Parallel Distributed Processing – (Rummelhart and McCelland 1986)

6. History (cont.) 1988- 1995 - Agents Resurgence of probability
“Nouvelle AI”
Alife
Genetic Algorithms
Agents

7. Stan Franklin’s Three Questions
Is AI possible? Can a machine ‘think”?
Is AI necessarily symbolic?
Neural Systems
Is representation necessary?
(“Nouvelle AI”)
Reference: “Artificial Minds” by Stan Franklin
Publisher: MIT Press ISBN:

8. Fundamental Problems! “TOY” problems Solutions that don’t scale
Solving the wrong problem
Over hype!

9. Critics Mind–Body problem Herbert Dreyfus Roger Penrose John Searle
The Moralists
From the field itself!

10. Herbert Dryfus
“If background understanding is indeed a skill and if skills are based on whole patterns and not on rules, we would expect symbolic representations to fail to capture our commonsense understanding”

11. Roger Penrose Views mind as having Quantum Theory Effects
Does not think a consciousness will emerge from a computer program.

12. Searle’s Chinese Room Questions Story Script Searle Chinese Room
(English)
Searle
(Does not know
Chinese)
Chinese Room
Answer
(Chinese)

13. The Moralists Weizenbaum – Jaron Lanier Bill Joy Eliza
“Computer Power and Human Reason”
Jaron Lanier
Bill Joy

14. David Marr’s Comment
Two problem types in AI as a study of information processing problems
Type 1 – separates into two parts
Theory that explains the problem solution method
Implementation (algorithm)
Type 2 – lacks Type 1 theory
May require many interacting parts with no simpler description
Within this view – the field has no progressed much!
Mimicry versus Exploration

15. Agents

16. Agents Agents imply an environment + entity
Robots are agents, so are web crawlers
Agents provide a excellent tool for study
Environments:
Artificial
Real World
A taxonomy of agents

17. Environment Observable Deterministic vs. Stochastic
Episodic vs. Sequential
Static vs. Dynamic
Discrete vs. Continuous
Single vs. Multiple agents

18. Simple Reflex Agents

23. An Agent System Rossum’s Playhouse Projects Simulation Java based
abstraction of the world of a Robot
Java based
Client – Server System
World is a Room
Projects
to build agents using AI techniques

24. What do we put inside the agent?
Logic
Representational systems
Frames
Semantic nets
Neural Nets

25. Readings “The Cambridge Quintet” by John L. Casti
“Arguing A.I. The Battle for Twenty-first Century Science” by Sam Williams