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---
title: Introduction
categories: session
---
# Materials
In general, you are supposed to read through the reading material
and watch any videos assigned *before* class. Since this is the first
class, it may not have been possible in this case, but please do it if
you can.
+ **Reading** Russel & Norvig, Chapter 1.
+ [Short Video Introduction to AI](https://www.youtube.com/watch?v=61RbzGBpBIs)
+ [Longer Video Introduction](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/lecture-videos/lecture-1-introduction-and-scope/) 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.
## (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
### 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
See [Introductory Lecture]().
1. Lethal autonomous weapons.
2. Surveillance and persuasion.
3. Biased decision making.
4. Job redundancy.
5. Safety-critical applications.
6. Cybersecurity.
# Programming
Norbert Wiener: machines which strive for human objectives, without
a pre-programmed goal. [R&N:52]
https://www.codingame.com/training/easy
**Discussion** could the machine take control?
# Homework
## (4) Programming
1. Read through the reading material for this class once more.
2. Do the preparation for the next lecture, on [Rational Agents]().