The document provides an introduction to artificial intelligence (AI), covering its definition, history, and related academic disciplines. It discusses various properties of intelligent systems, such as interaction with the real world, reasoning, learning, and adaptation, along with classifications of AI based on human-like thinking and behavior. The Turing Test is highlighted as a measure of a machine's intelligence, based on its ability to be indistinguishable from human responses.

1. Introduction to Artificial
Intelligence

2. In This Lecture:
• What is intelligence? What is artificial
intelligence?
• Brief introduction of Artificial Intelligence
• A very brief history of AI
• AI in practice

3. What is Intelligence?
• Intelligence:
“the capacity to learn and solve problems”
• in particular, the ability to solve novel
problems
• the ability to act rationally
• the ability to act like humans

4. Artificial Intelligence
• Artificial intelligence is that branch of science
which makes machines perform tasks which
would require intelligence when performed by
humans (Marvin Minsky)
• It is the science and engineering of making
intelligent machines, especially intelligent
computer programs.

5. Academics Disciplines relevant in AI
• Philosophy Logic, methods of reasoning, mind as physical
system, foundations of learning, language,
rationality.
• Mathematics Formal representation and proof, algorithms,
computation, (un)decidability, (in)tractability
• Probability/Statistics modeling uncertainty, learning from data
• Economics utility, decision theory, rational economic agents
• Neuroscience neurons as information processing units.
• Psychology/ how do people behave, perceive, process cognitive
Cognitive Science information, represent knowledge.
• Computer building fast computers
engineering
• Control theory design systems that maximize an objective
function over time
• Linguistics knowledge representation, grammars

6. History of AI
• 1943: early beginnings
– McCulloch & Pitts: Boolean circuit model of brain
• 1950: Turing
– Turing's "Computing Machinery and Intelligence“
• 1956: birth of AI
– Dartmouth meeting: "Artificial Intelligence“ name adopted
• 1950s: initial promise
– Early AI programs, including
– Samuel's checkers program
– Newell & Simon's Logic Theorist
• 1955-65: “great enthusiasm”
– Newell and Simon: GPS, general problem solver
– Gelertner: Geometry Theorem Prover
– McCarthy: invention of LISP

7. History of AI
• 1966—73: Reality dawns
– Realization that many AI problems are intractable
– Limitations of existing neural network methods identified
• Neural network research almost disappears
• 1969—85: Adding domain knowledge
– Development of knowledge-based systems
– Success of rule-based expert systems,
• E.g., DENDRAL, MYCIN
• But were brittle and did not scale well in practice
• 1986-- Rise of machine learning
– Neural networks return to popularity
– Major advances in machine learning algorithms and applications
• 1990-- Role of uncertainty
– Bayesian networks as a knowledge representation framework
• 1995-- AI as Science
– Integration of learning, reasoning, knowledge representation
– AI methods used in vision, language, data mining, etc

8. Properties of Intelligent System
• Ability to interact with the real world
– to perceive, understand, and act
– e.g., speech recognition and understanding
– e.g., image understanding
– e.g., ability to take actions, have an effect
• Reasoning and Planning
– modeling the external world, given input
– solving new problems, planning, and making decisions
– ability to deal with unexpected problems, uncertainties
• Learning and Adaptation
– we are continuously learning and adapting
– our internal models are always being “updated”
• e.g., a baby learning to categorize and recognize animals

9. More About Artificial Intelligence
• Actually definitions of artificial intelligence can
be grouped in 4 main categories

10. More About Artificial Intelligence
• The previously mentioned categories of
definitions can be considered along 2
dimensions:

11. More About Artificial Intelligence
• The other dimensions can be the following

12. Explaining Definition
SYSTEMS THAT THINK LIKE HUMANS
• If we want to develop a computer that thinks
like humans we need to know how people
think.
• Cognitive science integrates computational
models developed in the area of artificial
intelligence with techniques from psychology
in order to develop theories about how the
human mental mind works

13. Explaining Definition
SYSTEMS THAT THINK RATIONALLY
• This approach is related to logics, that is,
logical rules make the mental mind of humans
For example, if we know that All people
have a head and Alex is one of people, than we
can conclude that Alex has a head.

14. Explaining Definition
SYSTEMS THAT ACT LIKE HUMANS
- In this approach computer capabilities are
compared with human capabilities
- For this purpose a special test of intelligent
behavior is defined. The test is called the
Turing test.

15. Turing Test
• The idea of the test is the following. There are
3 rooms. In the first one there is an artificial
intelligence, in the second room- a person or
natural intelligence, and in the third room
there is a tester. The tester asks questions to
both intelligences. If it is impossible to
determine which answers were given by the
person and which ones by the machine, than
the machine has intelligence.

16. Explaining Definition
SYSTEMS THAT ACT LIKE HUMANS
To pass the Turing test the computer must have
the following capabilities:
• Natural language processing
• Knowledge representation
• Automated reasoning
• Machine learning

17. How much Intelligent System We Can
Build?
• Chess Playing is a classic AI problem
– well-defined problem
– very complex: difficult for humans to play well

18. Golden Pearl 1
• DO NOT BEG FOR LITTLE THINGS
OTHERWISE IT WILL BECOME YOUR HABIT