1. A Brief History of Artificial Intelligence
Chapter 1
A Brief History of Artificial Intelligence

2. Chapter 1 Contents What is Artificial Intelligence?
Strong AI and Weak AI
Strong Methods and Weak Methods
From Aristotle to Babbage
Alan Turing and the 1950s
The 1960s to the 1990s
Philosophy
Linguistics
Human Psychology and Biology
Prolog
LISP

3. What is Artificial Intelligence? (1)
A more difficult question is: What is intelligence?
This question has puzzled philosophers, biologists and psychologists for centuries.
Artificial Intelligence is easier to define, although there is no standard, accepted definition.

4. What is Artificial Intelligence? (2)
A simple definition might be as follows:
Artificial intelligence is the study of systems that act in a way that to any observer would appear to be intelligent.
In fact, Artificial Intelligence techniques are often used to solve relatively simple problems, or complex problems that are internal to more complex systems.
This may lead us to another definition of Artificial Intelligence, as follows:
Artificial Intelligence involves using methods based on the intelligent behavior of humans and other animals to solve complex problems.

5. Strong AI and Weak AI
There are two entirely different schools of Artificial Intelligence:
Strong AI:
This is the view that a sufficiently programmed computer would actually be intelligent and would think in the same way that a human does.
Weak AI:
This is the use of methods modeled on intelligent behavior to make computers more efficient at solving problems.
This course is concerned with Weak AI.
Strong AI is currently the stuff of science fiction, although there are many that believe that machines will indeed be capable of real thought at some point in the future.

6. Strong Methods and Weak Methods
Not to be confused with Strong AI and Weak AI.
Strong methods use knowledge about the world to solve problems.
Weak methods use logic and other symbolic systems.
Strong method systems rely on weak methods, as knowledge is useless without a way to handle that knowledge.
Weak methods are in no way inferior to strong methods – they simply do not employ world knowledge.

7. From Aristotle to Babbage
Aristotle’s study of logic was vital to the study of logic which is an important part of Artificial Intelligence.
Aristotle’s work was expanded on by the likes of Peter Abelard, Gottfried Leibniz and George Boole.
Charles Babbage was the inventor of the first computer –the Analytic Engine, in the 19th Century.
This computer was not actually built until the 20th Century, but Babbage’s work provided an important basis for the early work in Artificial Intelligence.

8. Alan Turing and the 1950s
Alan Turing is often seen as the father of Artificial Intelligence.
He invented the Turing Test, designed to determine if a computer system can be called an artificial intelligence or not, based on whether it can fool a human into thinking it is human too.
No system has yet passed the Turing Test.
Around this time, in the 1950’s, systems were being developed that could play checkers, engage in conversation and solve other problems.
The term Artificial Intelligence was coined in 1956 by John McCarthy.
Machine translation was considered to be a solvable problem.

9. The 1960s to the 1990s
During this time, the optimism of the 1950s was replaced with realism.
Artificial Intelligence replaced as its goal the building of an intelligent robot with the goal of using heuristics and other techniques to solve complex problems.

10. Philosophy
Philosophy provides an important background to a study of AI.
Descartes’ dualism described a universe consisting of two separate things: mind and matter.
Descartes believed that humans possessed minds, but that animals were simply biological machines.
The work of Aristotle, Descartes and more recently Daniel Dennett are worth studying.

11. Linguistics
For computers to interact with humans properly, they need to understand human language.
Noam Chomsky’s work on grammars has informed the study of natural language processing (NLP).
Knowledge representation which is fundamental to AI is essential to understanding language.

12. Human Psychology and Biology
While most AI techniques do not map neatly onto real biological systems, some, such as neural networks, do.
Cognitive Psychology has many links with AI: It involves the idea that the human brain uses processing methods on knowledge to solve problems.
This contrasts with behaviorism, which is the view that behavior is linked directly to stimuli.

13. Prolog PROLOG (PROgramming in LOGic): Facts: Rules:
A language designed to build databases of facts and rules, and then to have the system answer questions by a process of logical deduction using the facts and rules in the database.
Facts:
tasty (cheese).
made_from (cheese, milk).
Rules:
contains (X, Y) :- made_from (X, Z), contains (Z, Y).
Prolog is not an efficient language like C++, but it is the language of choice when building systems based on logic.

14. LISP LISP (LISt Programming): (A B C)
A language which more closely resembles the imperative programming languages such as C++ than does PROLOG.
As its name suggests LISP is based around handling of lists of data. A list in LISP is contained within brackets, such as:
(A B C)
Lists represent data and also programs, meaning LISP programs can manipulate other programs, and it is even possible to write self-modifying LISP programs.