---
title: Introduction
categories: lecture
---

[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