Download A Sparse Texture Representation Using Affine

AI: Artificial Intelligence
Sanaullah Manzoor
CS&IT, Lahore Leads University
[email protected]
https://sites.google.com/site/engrsanaullahmanzoor/home
1
Text Book
Artificial Intelligence: Structures and
Strategies for Complex Problem Solving,
Fifth-Edition
By George F Luger
2
What is AI?
Some possible definitions from the textbook:
1. Thinking humanly
2. Acting humanly
3. Thinking rationally
4. Acting rationally
3
What is Intelligence?
Intelligence:
• “the capacity to learn and solve problems” (Websters
dictionary)
• in particular,
– the ability to solve novel problems
– the ability to act rationally
– the ability to act like humans
Artificial Intelligence
• build and understand intelligent entities or agents
• 2 main approaches: “engineering” versus “cognitive
modeling”
4
AI definition 1: Thinking humanly
• Need to study the brain as an information
processing machine: cognitive science and
neuroscience
5
AI definition 2: Acting humanly
Turing test was proposed in 1950.
It is a test to decide whether or not a
particular machine is intelligent.
Machine
Human
Player
6
AI definition 2: Acting humanly
• The Turing Test
• What capabilities would a computer need to have to pass
the Turing Test?
•
•
•
•
Natural language processing
Knowledge representation
Automated reasoning
Machine learning
7
AI definition 3: Thinking rationally
• Idealized or “right” way of thinking
• Logic: patterns of argument that always yield correct
conclusions when supplied with correct premises
• Logicist approach to AI: describe problem in formal
logical notation and apply general deduction
procedures to solve it
8
AI definition 4: Acting rationally
• A rational agent acts to optimally achieve its goals
• Goals are application-dependent and are expressed in terms
of the utility of outcomes
• Being rational means maximizing your utility
• This definition of rationality only concerns the
decisions/actions that are made, not the cognitive
process behind them
9
History of AI?
1950 Turning describes his test for machine intelligence
1955
Bernstein develops first working chess program
1956
McCarthy coins the term Artificial Intelligence
1957 McCarthy invents LISP
1965
Development of the first expert system
1967
Greenblatt develops the first competent chess
program
1970
PROLOG language is invented
1972
Natural language processing program SHRDLU
1982 Japanese 5th generation AI research effort begins
10
Major AI Areas
1. Expert Systems
An ES is a computer program designed to act as an
expert in a particular domain (area of expertise). It
typically includes a sizeable knowledge base, consisting
of facts about the domain and rules for application
to
those facts. Medical, chemical,
geological
2. Natural Language Processing
Goal is to enable people and computers
communicate in ordinary or natural English.
to
- Comprehension of natural language:
Keyboard input, speech recognition
- Generation of natural language.
11
Major AI Areas
3. Robotics and Computer Vision
Factory automation
Autonomous vehicles
Robots: Electromechanical devices programmed to
perform manual tasks. Not all robots are
intelligent.
Some
are
pre-programmed
by
conventional techniques and are dumb. An intelligent
robot usually includes some kind of
sensory
apparatus that allows it to respond to
changes in its
environment.
Computer Vision: Comprehension of surroundings
(interpretation of images)
12
Major AI Areas
4. Game Playing
e.g. Chess
5. Languages and Environment of AI
6. Planning and Decision Support
Intelligent programs designed to provide active
assistance in the planning process. They are helpful
to managers with decision making responsibilities
13
Major AI Areas
7. Automatic Programming
The aim is a computer system that could develop
programs by itself, in response to and in
accordance with the specifications of a program
developer
8. Intelligent Computer Aided Instruction
Computerised tutors that shape their teaching
techniques to fit the learning patterns of individual
students
9. Machine Learning & Emergent Computation
e.g. Neural Networks, Genetic algorithm.
14
AI PREHISTORY
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
Psychology adaptation phenomena of perception and
motor control experimental techniques
(psychophysics, etc.)
15
AI PREHISTORY
Economics formal theory of rational decisions
Linguistics knowledge representation grammar
Neuroscience plastic physical substrate for
mental activity
Control theory homeostatic systems, stability
simple optimal agent designs
16