# Artificial Intelligence - Introduction

## Can Machines Think?

This is a difficult question and to answer it we need to define intelligence and thinking.

Some aspects of intelligence:

• Planing
• Learning
• Reasoning
• Problem Solving
• Perception
In the 1950's, a test was proposed by Alan Turing to determine whether a machine is intelligent or not - The Turing Test.

### The Turing Test

The basis of the turing test is for a person to attempt to distinguish between a machine and a human being. For fairness, the machine and the human are isolated from the person carrying out the test and messages are exchanged via a keyboard and screen. If the person can not distinguish between the computer and the human being, then the computer must be intelligent.  Each year, there is a Turing test contest called the Loebner Prize. This is a transcript from the winner for 1998.

This test is often criticised because it only tests a limited aspect of intelligence.

Some people think that even if a machine could pass the Turing Test it may still not be intelligent.

### The Chinese Room Problem

A (non Chinese speaking) person is locked in a room and given the algorithm for a program that could pass the Turing test in Chinese. He/she is asked questions in Chinese, applies the algorithm by hand, and feeds the answers back. The room will appear intelligent but the person inside understands no Chinese, so is there any intelligence present?

This problem is criticised because, it may well be possible for the complete system to be intelligent (i.e. room and person inside) without the person being intelligent.

Some people say that passing the Turing test is sufficient to prove intelligence but it is not necessary to prove intelligence. In other words, a machine may fail the Turing Test but still be intelligent.

There are plenty of examples of computer systems that perform tasks that would require intelligence if they were performed by a human being.

One possible classification of AI tasks is into 3 classes: Mundane problems, Formal problems and Expert Problems.

• Perception
• Vision
• Speech
• Natural Language understanding, generation and translation
• Common-sense Reasoning
• Simple reasoning and logical symbol manipulation
• Robot Control

• Games
• Chess

• Deep Blue recently beat Gary Kasparov
• Backgammon
• Draughts
• GO
To solve these problems we must explore a large number of solutions quickly and choose the Best One.
• Mathematics
• Geometry and Logic

• Logic Theorist: It proved mathematical theorems. It actually proved several theorems from Classical Math Textbooks
• Integral Calculus

• Programs such as Mathematica and Mathcad and perform complicated symbolic integration and differentiation.
• Proving Properties of Programs e.g. correctness
• Manipulate Symbols and reduce problem (usually recursively), until the answer is obvious. That is, it can be looked up in a table.

• Engineering
• Design
• Fault finding
• Manufacturing
• Planning
• Scientific Analysis
• Medical Diagnosis
• Financial Analysis
Rule based systems -
if (conditions) then action

## AI's Underlying assumption:

The Physical Symbol System Hypothesis
A physical symbol system has the necessary and sufficient means for general intelligent action
In other words:

Computers (Turing Machines) have the power for general intelligent action.

## Example of AI problem solving.

Problem : A farmer has a hungry fox a fat goose and a bag of grain. The farmer needs to cross a river but his boat can only carry two things.

Constraints: Fox and goose cannot be left together Goose and grain cannot be left together.

How to cross the river?

English language representation is hard to solve.

Try visual/graphical representation:

To solve this problem we need only follow the tree from its root node to any leaf node.