Introduction

Overview -> Introduction -> lecture

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.

(1) Practical Information

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

Sessions

  • Typical session format.
    • Briefing 8.15-9.15
    • Break
    • Exercise 9.30-11.30
    • Lunch
    • Status 12.15-12.45
    • Exercise 12.45-13.15
    • Break
    • Status 13.30-14.00
  • Today, the briefing will take longer.
  • I’ll try to stick with 12.15 and 13.30 as the times to gather, especially in digital sessions.
  • I will be available most of the exercise time to help and discuss, and I may make plenary addresses if important questions arise.

(2) Debate: Key recurring questions

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

(3) Lecture: Background and Introduction

Reading Russel & Norvig Chapter 1

What is AI?

  • Rational versus Human
  • the Turing Test
  • Six areas of AI
  • value alignment problem

Reason

  • Theoretical versus Practical reason
    • instrumental reason
  • Hume’s is-ought problem
  • Deduction, Induction, Abduction, Analogy

Foundations

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

Parallel development.

  • Computerisation of scientific disciplines.
  • Disciplines provide models for AI
  • Development of AI leads to better Algorithms

Philosophy

Epistemology

  • How do we know? How do we know that we know?
  • Hume’s is-ought problem

Goals

We deliberate not about ends, but about means. For a doctor does not deliberate whether he shall heal, (Aristotle, Ethics)

Simon & Newell implemented Aristotle’s principle as the General Problem Solver.

Schön in The Reflective Practitioner observed a doctor who had to deliberate. The treatment might help one disease and cause another.

Mathematics and Computing

  • Logic
  • Gödel’s incompleteness theorem
  • Computability (NP-completeness)

Neuro-science

  • Modelling the brain
  • Simulating the brain

Psychology

  • Cognition
  • Computer Models explain Psychology

Computer Engineering

  • Computing power

Control Theory

  • Cybernetics - navigator/helmsman

Epochs

  • 19th C: Ada Lovelace
    • the analytical engine can only do what we tell it to do
  • 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 and probabilistic reasoning
  • From 1995: Intelligent Agents - rebirth of the vision of the whole agent
  • From 2001: Big data
  • From 2011: Vector Processors and Computing Power
    • reinforcement learning
    • deep learning

Applications

  • Autonomous Ships: path planning/path selection (Ottar, Robin, etc.)
    • also autonomous flying drones (Erlend)
  • Medical Image Processing (Hans Georg, Kjell-Inge)
    • Diagnosis, e.g. cancer detection
    • Image segmentation to make 3D models of the patient
  • Cosmology: detect dark matter (Hans Georg, Ben David)
  • Information Security (previously - Hans Georg)
    • detect clandestine communication (steganalysis)
    • intrusion detection

Concerns

  1. Lethal autonomous weapons.
  2. Surveillance and persuasion.
  3. Biased decision making.
  4. Job redundancy.
  5. Safety-critical applications.
  6. Cybersecurity.

Norbert Wiener: machines which strive for human objectives, without a pre-programmed goal. [R&N:52]

Discussion could the machine take control?

(4) Programming