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---
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
### OneNote
It is possible to use OneNote integrated with Blackboard in this course.
Students will then share a OneNote notebook, in which you add pages and notes.
For example, if given a group assignment in class, each group could be asked to answer some questions or enter some information in OneNote.
You could also use it as a wiki and for example share your thoughts on AI-related topics, how you solved a particular problem, etc.
You can easily draw sketches and diagrams, insert links to useful online information, insert videos, files, pdfs, and more.
For motivation, think of OneNote as a shared resource bank that can be useful both as we progress and when studying for the final oral exam!
## 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*
- *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
