1. Artificial Intelligence
Introductory Lecture

2. Who Am I? Prof. Dr. Adeel Akram BSc Electrical Engg.
MSc Computer Engg.
PhD in Adhoc Wireless Networks
Telecom Engineering Dept. UET, Taxila

3. What is artificial intelligence?
Science and engineering of making intelligent machines, especially intelligent computer programs
Using computers to understand human intelligence
Not confined to methods that are biologically observable

4. What is intelligence?
Intelligence is the computational part of the ability to achieve goals in the world.
Degree of intelligence vary in people, animals and machines

5. Tighter definition of AI
AI is the science of making machines do things that would require intelligence if done by people. (Minsky)
At least we have experience with human intelligence
Possible definition: intelligence is the ability to form plans to achieve goals by interacting with an information-rich environment
Intelligence encompasses abilities such as:
understanding language perception
reasoning learning

6. Pre-history of AI
the quest for understanding & automating intelligence has deep roots
4th cent. B.C.: Aristotle studied mind & thought, defined formal logic
14th–16th cent.: Renaissance thought built on the idea that all natural or artificial processes could be mathematically analyzed and understood
18th cent.: Descartes emphasized the distinction between mind & brain (famous for "Cogito ergo sum")
19th cent.: advances in science & understanding nature made the idea of creating artificial life seem plausible
Shelley's Frankenstein raised moral and ethical questions
Babbage's Analytical Engine proposed a general-purpose, programmable computing machine -- metaphor for the brain
19th-20th cent.: saw many advances in logic formalisms, including Boole's algebra, Frege's predicate calculus, Tarski's theory of reference
20th cent.: advent of digital computers in late 1940's made AI a viable
Turing wrote seminal paper on thinking machines (1950)

7. Pre-history of AI (2)
birth of AI occurred when Marvin Minsky & John McCarthy organized the Dartmouth Conference in 1956
brought together researchers interested in "intelligent machines"
for next 20 years, virtually all advances in AI were by attendees
Minsky (MIT), McCarthy (MIT/Stanford), Newell & Simon (Carnegie),…

8. AI aims to put the human mind into the computer?
Some researchers say that !!!
maybe they are using it metaphorically
Human mind has a lot of peculiarities
Nobody is serious about imitating them all

9. What is the Turing test?
Alan Turing's 1950 article Computing Machinery and Intelligence discussed conditions for considering a machine to be intelligent
He argued that if the machine could successfully pretend to be human to a knowledgeable observer then you certainly should consider it intelligent
This test would satisfy most people but not all philosophers
The observer could interact with the machine and a human by teletype
The human would try to persuade the observer that it was human and the machine would try to fool the observer

10. Turing test (2) Turing test is a one-sided
A machine that passes the test should certainly be considered intelligent,
But a machine could still be considered intelligent without knowing enough about humans to imitate a human
e.g., Weizenbaum's ELIZA program
J. Weizenbaum, ”ELIZA -- A computer program for the study of natural language communication between man and machine", Communications of the ACM 9(1):36-45, 1966.

11. Aim of AI?
The ultimate effort is to make computer programs that can solve problems and achieve goals in the world as well as humans do.
However, many people involved in particular research areas are much less ambitious

12. human-level intelligence?
A few people think that human-level intelligence can be achieved by writing large numbers of programs (of the kind people are now writing) and assembling vast knowledge bases of facts (in the languages now used for expressing knowledge)

13. human-level intelligence? When will it happen?
Most AI researchers believe that new fundamental ideas are required, and therefore it cannot be predicted when human level intelligence will be achieved

14. History of AI (1) the history of AI research is a continual cycle of
optimism & hype  reality check & backlash  refocus & progress  …
1950's – birth of AI, optimism on many fronts
general purpose reasoning, machine translation, neural computing, …
first neural net simulator (Minsky): could learn to traverse a maze
GPS (Newell & Simon): general problem-solver/planner, means-end analysis
Geometry Theorem Prover (Gelertner): input diagrams, backward reasoning
SAINT(Slagle): symbolic integration, could pass MIT calculus exam

15. History of AI (2)
1960's – failed to meet claims of 50's, problems turned out to be hard!
so, backed up and focused on "micro-worlds"
within limited domains, success in: reasoning, perception, understanding, …
ANALOGY (Evans & Minsky): could solve IQ test puzzle
STUDENT (Bobrow & Minsky): could solve algebraic word problems
SHRDLU (Winograd): could manipulate blocks using robotic arm, explain self
STRIPS (Nilsson & Fikes): problem-solver planner, controlled robot "Shakey"
Minsky & Papert demonstrated the limitations of neural nets

16. History of AI (3)
1970's – results from micro-worlds did not easily scale up
so, backed up and focused on theoretical foundations, learning/understanding
conceptual dependency theory (Schank)
frames (Minsky)
machine learning: ID3 (Quinlan), AM (Lenat)
practical success: expert systems
DENDRAL (Feigenbaum): identified molecular structure
MYCIN (Shortliffe & Buchanan): diagnosed infectious blood diseases

17. History of AI (4) 1980's – BOOM TOWN!
cheaper computing made AI software feasible
success with expert systems, neural nets revisited, 5th Generation Project
XCON (McDermott): saved DEC ~ $40M per year
neural computing: back-propagation (Werbos), associative memory (Hopfield)
logic programming, specialized AI technology seen as future

18. History of AI (5) 1990's – again, failed to meet high expectations
so, backed up and focused : embedded intelligent systems, agents, …
hybrid approaches: logic + neural nets + genetic algorithms + fuzzy + …
CYC (Lenat): far-reaching project to capture common-sense reasoning
Society of Mind (Minsky): intelligence is product of complex interactions of simple agents
Deep Blue (formerly Deep Thought): defeated Kasparov in Speed Chess in 1997

19. Philosophical extremes in AI (1)
Neats vs. Scruffies
Neats focus on smaller, simplified problems that can be well-understood, then attempt to generalize lessons learned
Scruffies tackle big, hard problems directly using less formal approaches

20. Philosophical extremes in AI (2)
GOFAIs vs. Emergents
GOFAI (Good Old-Fashioned AI) works on the assumption that intelligence can and should be modeled at the symbolic level
Emergents believe intelligence emerges out of the complex interaction of simple, sub-symbolic processes

21. Philosophical extremes in AI (3)
Weak AI vs. Strong AI
Weak AI believes that machine intelligence need only mimic the behavior of human intelligence
Strong AI demands that machine intelligence must mimic the internal processes of human intelligence, not just the external behavior

22. Criteria for success (1)
long term: Turing Test (for Weak AI)
as proposed by Alan Turing (1950), if a computer can make people think it is human (i.e., intelligent) via an unrestricted conversation, then it is intelligent
Turing predicted fully intelligent machines by 2000, not even close
Loebner Prize competition, extremely controversial

23. Criteria for success (2)
short term: more modest success in limited domains
performance equal or better than humans
e.g., game playing (Deep Blue), expert systems (MYCIN)
real-world practicality $$$
e.g., expert systems (XCON, Prospector), fuzzy logic (cruise control)

24. Criteria for success (3)
AI is still a long way from its long term goal
but in ~50 years, it has matured into a legitimate branch of science
has realized its problems are hard
has factored its problems into subfields
is attacking simple problems first, but thinking big

25. Criteria for success (4)
surprisingly, AI has done better at "expert tasks" as opposed to "mundane tasks" that require common sense & experience
hard for humans, not for AI:
e.g., chess, rule-based reasoning & diagnosis,
easy for humans, not for AI:
e.g., language understanding, vision, mobility, …

26. Course Material Text book:
Artificial Intelligence: Structures and Strategies for Complex Problem Solving (4th ed.), George F. Luger, Addison-Wesley, 2002.
Artificial Intelligence, A Modern Approach, Stuart Russell and Peter Norvig, Prentice Hall, ’95

27. AI Knowledge-based and Expert Systems Planning Coping with Vague,
Incomplete and
Uncertain Knowledge
Searching
Intelligently AI
Logical Reasoning
& Theorem Proving
Communicating,
Perceiving and
Acting
Intelligent Agents
& Distributed AI
Learning & Knowledge Discovery

28. Course goals
Survey the field of Artificial Intelligence, including major areas of study and research
Study the foundational concepts and theories that underlie AI, including search, knowledge representation, and sub-symbolic models
Contrast the main approaches to AI: symbolic vs. emergent
Provide practical experience developing AI systems using the logic programming language Prolog

29. Course outline Logic and Prolog Problem-solving as search
predicate calculus
logic programming, Prolog
Problem-solving as search
state spaces
search strategies, heuristics
game playing
Knowledge representation & reasoning
representation structures (semantic nets, frames, scripts, …)
expert systems, uncertainty
automated reasoning
Machine learning
symbolic models: candidate elimination, ID3
connectionist models: neural nets, backprop, associative memory
emergent models: genetic algorithms, artificial life
Selected AI topics
student presentations

30. Next Lecture… logic & logic programming Read Chapters 1, 2 & 14
propositional calculus, predicate calculus
logic programming
Prolog basics
Read Chapters 1, 2 & 14
Be prepared for a quiz on
today’s lecture (moderately thorough)

31. Questions & Answers
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