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Introduction

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---
title: Introduction
categories: session
---


# Materials

+ **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.  

## Key recurring questions
+ 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?

## Exam Format
## 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