Download Artificial Intelligence

Artificial Intelligence
Introductory Lecture
Who Am I?
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Prof. Dr. Adeel Akram
BSc Electrical Engg.
MSc Computer Engg.
PhD in Adhoc Wireless Networks
Telecom Engineering Dept. UET, Taxila
[email protected]
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
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
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
• reasoning
perception
learning
Pre-history of AI
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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
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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
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Turing wrote seminal paper on thinking machines (1950)
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),…
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
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
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.
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
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)
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
History of AI (1)
• the history of AI research is a continual cycle of
optimism & hype  reality check & backlash  refocus & progress
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• 1950's – birth of AI, optimism on many fronts
general purpose reasoning, machine translation, neural computing,
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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
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
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
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
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
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
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
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
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
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)
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
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, …
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
Knowledge-based
and Expert Systems
Planning
Coping with Vague,
Incomplete and
Uncertain Knowledge
Logical Reasoning
& Theorem Proving
Searching
Intelligently
AI
Intelligent Agents
& Distributed AI
Learning & Knowledge Discovery
Communicating,
Perceiving and
Acting
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
Course outline
1.
Logic and Prolog
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Problem-solving as search
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Knowledge representation & reasoning
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Machine learning
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Selected AI topics
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predicate calculus
logic programming, Prolog
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state spaces
search strategies, heuristics
game playing
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representation structures (semantic nets, frames, scripts, …)
expert systems, uncertainty
automated reasoning
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symbolic models: candidate elimination, ID3
connectionist models: neural nets, backprop, associative memory
emergent models: genetic algorithms, artificial life
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student presentations
Next Lecture…
• logic & logic programming
– propositional calculus, predicate calculus
– logic programming
– Prolog basics
• Read Chapters 1, 2 & 14
• Be prepared for a quiz on
– today’s lecture (moderately thorough)
Questions & Answers
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