Materials

• Reading Russel & Norvig, Chapter 1.
• Short Video Introduction to AI
• Longer Video Introduction with Prof. Patrick H. Winston at MIT, 2010
  • it is particularly interesting to note his take on models and representation
• See also the Reading List

Briefing

This module is more than hard facts. While Artificial Intelligence solves a lot of important problems very well, it is also a technology full of surprises, ambiguities, and moral dilemmas. For this reason you have to piece together the material yourselves and look for a meaning whih is relevant to you. It is important to read and hear different perspectives on the subject, and therefore I provide a range of video links and a long reading list.

• Wiki + BlackBoard
• Practical Information at the Front Page
• Compulsory Assignments
• Exam Format

Debate: Key recurring questions

• What is intelligence?
• What is AI?
• Is AI possible?

Background

Reading Russel & Norvig Chapter 1

What is AI?

• Rational versus Human
• the Turing Test
• Six areas of AI

Foundations

• Philosophy - Mathematics - Economics
• Neuroscience - Psychology
• Computer Engineering - Control Theory - Cybernetics
• Linguistics

Epochs

• Dawn
  • McCulloch & Pitt 1943: Neural Networks
  • Hebb 1949: Hebbian Learning
  • Minsky & Edmonds 1950: first computer running ANN - 40 neurons
  • Turing 1950: the imitation game
• Birth of AI as a separate field 1956
  • Simon & Newell: Logic Theorist
• Enthusiasm and Expectation 1952-59
  • coincides with Scientification
  • Lisp 1958, Eliza, GPL
  • Machine Evolution 1958
• Reality 1966-73
  • optimistic ten-year predictions fail
  • three challenges
    1. AI has no domain knowledge
    2. intractible problems
    3. linmited representations (two perceptrons do not suffice)
• Knowledge-based systems 1969-79
  • Previous limitation: weak methods which do not scale. Brute force?
  • Solution: knowledge bases
• From 1980: Industrialisation
  • first commercial expert system 1982 (DEC)
• From 1986: ANN returns - connectionist opposed to symbolic and logicist
• From 1987: Scientific method - experiments and statistics
  • machine learning
• From 1995: Intelligent Agents - rebirth of the vision of the whole agent
• From 2001: Big data
• From 2013?: Vector Processors and Computing Power
  • reinforcement learning
  • deep learning

Programming