Artificial IntelligenceArtificial IntelligenceArtificial IntelligenceArtificial Intelligence

Introductory LectureIntroductory Lecture

Who Am I?• Prof. Dr. Adeel Akram• BSc Electrical Engg.• MSc Computer Engg.• PhD in Adhoc Wireless Networks• Telecom Engineering Dept. UET, Taxila• adeel.akram@uettaxila.edu.pk

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 perception• reasoning learning

Pre-history of AI• the quest for understanding & automating intelligence has

deep roots

4th cent. B.C.: Aristotle studied mind & thought, defined formal logic14th–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)

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 …

• 1950's – birth of AI, optimism on many frontsgeneral 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

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 upso, 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

AIIntelligent Agents & Distributed AI

Planning

Learning & Knowledge Discovery

Communicating,Perceiving and

Acting

Coping with Vague,Incomplete and

Uncertain Knowledge

Knowledge-basedand Expert Systems

Searching Intelligently

Logical Reasoning& Theorem Proving

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 outline1. Logic and Prolog

predicate calculus logic programming, Prolog

2. Problem-solving as search state spaces search strategies, heuristics game playing

3. Knowledge representation & reasoning representation structures (semantic nets, frames, scripts, …) expert systems, uncertainty automated reasoning

4. Machine learning symbolic models: candidate elimination, ID3 connectionist models: neural nets, backprop, associative memory emergent models: genetic algorithms, artificial life

5. Selected AI topics 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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