Introduction of AI, Definition of AI Turing Test, History of AI, Application of AI

1. Artificial Intelligence
Instructor: Subash Chandra Pakhrin (Kathmandu, Nepal)
MSC in Information and Communication Engineering
Basic course on Data Science
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2. Lecture 1: Introduction
- Understand the definition of artificial intelligence
- Discuss the different faculties involved with intelligent
behavior
- Examine the different ways of approaching AI
- Look at some example systems that use AI
- Trace briefly the history of AI
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3. Lecture 1: Introduction
• On taking this lesson you should be
⁻ Familiar with the different ways of defining artificial
intelligence
⁻ Understand what are the different components of intelligent
behavior
⁻ Develop an appreciation of the vast scope of AI and the
intellectual challenges in this field
⁻ Have a fair idea of the types of problems that are currently
solved by computers and those that are as yet beyond its
ability.
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4. Lecture 1: Introduction
• Definition of AI
• Example Systems
• Approaches of AI
• Brief history
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5. What is AI ?
• Artificial Intelligence
⁻ Is concerned with the design of intelligence in an artificial
device
⁻ Term coined by McCarthy in 1956
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6. What is AI ?
• Artificial Intelligence is concerned with the design of intelligence in an
artificial device.
• What is intelligence ?
Humans ?
⁻ Behave as intelligently as human
⁻ Behave in the best possible manner
⁻ Thinking ?
⁻ Acting ?
• Intelligence is:
• The ability to reason
• The ability to understand
• The ability to create
• The ability to learn from experience
• The ability to plan and execute
complex tasks
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7. Definitions of AI
• What to look at:
thought processes/reasoning vs. behavior
• How to measure performance:
human – like performance vs. ideal performance
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8. Definitions of AI
• Artificial intelligence is about the science and engineering necessary
to create artifacts that can
• Acquire knowledge, i.e., can learn and extract knowledge; and
• Reason with knowledge (leading to doing tasks such as planning, explaining,
diagnosing, acting rationally, etc.),
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9. Formal Definitions
• Barr and Feigenbaum
• “AI is the part of computer science concerned with designing intelligence
computer systems, that is, systems that exhibit the characteristics we
associate with intelligence in human behavior.”
• Elaine Rich
• “AI is the study of how to make computers do things at which, at the moment,
people are better”
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10. Approaches of AI
Thought / Reasoning
Cognitive Science
Systems that act
like humans
Systems that think
rationally
Laws of Thought/
Logic
System that think
like humans
Turing Test
Ideal
Performance
(rationality)
Human-like
performance
Behavior
Rational Agents
Systems that act
rationally
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11. Alan Turing (23rd June 1912 – 7th June 1954)
• He was an English mathematician, computer
scientist, logician, cryptanalyst, philosopher and theoretical.
• Turing is widely considered to be the father of theoretical
computer science and artificial intelligence.
• Despite these accomplishments, he was never fully
recognized in his home country during his lifetime, due to
his homosexuality, which was then a crime in the UK.
• He devised a number of techniques for speeding the
breaking of German ciphers, an electromechanical machine
that could find settings for the Enigma machine.
• Turing played a pivotal role in cracking intercepted coded
messages that enabled the Allies to defeat the Nazis in
many crucial engagements.
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12. Turing Test
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13. The Turing Test: Result
• If the interrogator cannot reliably distinguish the human from the
computer
• Then the computer does possess (artificial) intelligence.
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14. To pass a Turing test, a computer must have
following capabilities:
• Natural Language processing: Must be able to communicate
successfully in English
• Knowledge representation: To store what it knows and hears
• Automated reasoning: Answer the questions based on the stored
information
• Machine learning: Must be able to adapt on new circumstances
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15. The Total Turing Test
• It includes video signals and manipulation capability so that the
interrogator can test subject’s perceptual abilities and object
manipulation ability. To pass the total Turing test computer must have
following additional capabilities:
• Computer vision: To perceive objects
• Robotics: To manipulate objects and move
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16. Typical AI Problems
• Intelligent entities (or “agents”) need to be able to do both
“mundane” and “expert” tasks:
• Mundane tasks:
⁻ Planning route, activity.
⁻ Recognizing (through vision) people, objects.
⁻ Communicating (through natural language).
⁻ Navigating round obstacles on the street
• Expert tasks:
⁻ Medical diagnosis.
⁻ Mathematical problem solving.
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17. Intelligent behavior
• Perception
• Reasoning
• Learning
• Understanding Language
• Solving problems
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18. What’s easy and what’s hard ?
• It has been easier to mechanize many of the high-level tasks
we usually associate with “intelligence” in people.
⁻ Symbolic integration,
⁻ Proving theorems,
⁻ Playing chess,
⁻ Medical diagnosis
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19. What’s easy and what’s hard ?
• It has been very hard to mechanize tasks that lots of animals can do
⁻ Walking around without running into things
⁻ Catching prey and avoiding predators
⁻ Interpreting complex sensory information
⁻ Modeling the internal states of other animals from their behavior
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20. Applications
• Computer vision
• Medical Image Segmentation
• Robot Navigation
• Image Recognition
• Robotics
• Language processing / Machine Translation
• Speech processing
• Autonomous planning and scheduling
• Game playing
• Autonomous control
• Expert systems
• Logistics Planning
• Stock price prediction
• Exploration of Universe
• Product recommendation
• Face Detection 20AI, Subash Chandra Pakhrin

21. Computer Vision (Image Segmentation) / Uses of
Deep Learning In Chest Abnormality Detection
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22. Stock price prediction
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23. Exploration of Universe
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24. Speech processing
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25. Face Detection
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26. Expert systems (https://fourkind.com/work/finavia-optimal-airport/)
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27. Practical Impact of AI
• AI components are embedded in numerous devices e.g. copy
machines.
• AI systems are in everyday use
⁻ Detecting credit card fraud
⁻ Configuring products
⁻ Aiding complex planning tasks
⁻ Advising physicians.
• Intelligent tutoring systems provide students with personalized
attention.
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28. Autonomous Land Vehicle In a Neural Network
• 1989 – Dean Pomerleau at CMU
created ALVINN
• The system drove a car coast-
to-coast under computer
control for all but about 50 of
the 2850 miles.
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29. Deep Blue
• 1997: The Deep Blue chess program
beats the current world chess
champion, Gary Kasparov, in a widely
followed match.
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30. Machine Translation
• Immediate translations between people speaking different language
would be a remarkable achievement of enormous economic and
cultural benefit.
• Universal translation is one of 10 emerging technologies that will
affect our lives and work ‘in revolutionary ways’ within a decade,
Technology Review says.
• Carneige Mellon is working on its own ‘Speechlator’ for use in doctor-
patient interviews.
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31. Example of Machine Translation: Wonder
Women
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32. Autonomous agents
• In space exploration, robotic space probes autonomously monitor
their surroundings, make decisions and act to achieve their goals.
• NASA’s Mars rovers: 1. The Spirit rover 2. Opportunity
• The Spirit rover is exploring a range of Martian hills that took two
months to reach. It is finding curiously eroded rocks that may be new
pieces to the puzzle of the region’s past.
• Spirit’s twin, Opportunity, is also negotiating sloped ground. It is
examining exposed rock layers inside a crater informally named
“Endurance.”
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33. Internet Agents
• The explosive growth of the internet has also led to growing interest
in internet agents to
⁻ Monitor user’s tasks
⁻ Seek needed information
⁻ Learn which information is most useful
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34. Approaches to AI
• Strong AI aims to build machines that can truly reason and solve
problems which is self aware and whose overall intellectual ability is
indistinguishable from that of a human being.
⁻ Human like
⁻ Non- human like
• Excessive optimism in the 1950s and 1960s concerning strong AI has
given away to an appreciation of the extreme difficulty of the
problem.
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35. Approaches to AI
• Weak AI: deals with the creation of some form of computer-based
artificial intelligence that cannot truly reason and solve problems, but
can act as if it were intelligent.
• Weak AI holds that suitably programmed machines can simulate
human cognition.
• Strong AI maintains that suitably programmed machines are capable
of cognitive mental states.
• Applied AI: aims to produce commercially viable “smart” systems-
such as, for example, a security system that is able to recognize the
faces of people who are permitted to enter a particular building.
Applied AI has already enjoyed considerable success.
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36. AI Topics
• Core Areas
• Knowledge representation
• Reasoning
• Machine Learning
• Perception
• Vision
• Natural Language
• Robotics
• Uncertainty
• Probabilistic approaches
• General algorithms
• Search
• Planning
• Constraint satisfaction
• Applications
• Game playing
• AI and education
• Distributed agents
• Decision theory
• Reasoning with symbolic
data
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37. Approaches to AI
• Cognitive AI: computers are used to test theories about how the
human mind works – for example, theories about how we recognize
faces and other objects, or about how we solve abstract problems.
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38. What can AI systems do
• Computer vision: face recognition
• Robotics: autonomous (mostly) automobile
• Natural Language processing: simple machine translation
• Expert systems: medical diagnosis in a narrow domain
• Planning and scheduling: Hubble Telescope experiments
• Learning: text categorization into ~ 1000 topics
• Games: Grand Master level in chess (world champion), checkers, etc.
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39. What can’t AI systems do yet ?
• Understand natural language robustly (e.g., read and understand
articles in a newspaper)
• Surf the web
• Lear a natural language
• Construct plans in dynamic real-time domains
• Exhibit true autonomy and intelligence
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40. Foundations of AI
Artificial Intelligence
Psychology
Philosophy
Mathematics
Biology
Computer Science
Economics
Linguistics
Neuroscience
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41. Foundations of AI:
• Philosophy
• Logic, reasoning, mind as a physical system, foundations of learning, language
and rationality.
• Where does knowledge come from ?
• How does knowledge lead to action ?
• How does mental mind arise from physical brain ?
• Can formal rules be used to draw valid conclusions ?
• Economics:
• Formal theory of rational decisions, game theory, operation research.
• How should we make decisions so as to maximize payoff ?
• How should we do this when others may not go along ?
• How should we do this when the payoff may be far in future ?
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42. Foundations of AI:
• Mathematics:
• Formal representation and proof algorithms, computation, un decidability,
intractability, probability.
• What are the formal rules to draw the valid conclusions ?
• What can be computed ?
• How do we reason with uncertain information ?
• Psychology:
• Adaptation, phenomena of perception and motor control.
• How humans and animals think and act ?
• Linguistics:
• Knowledge representation, grammar
• How does language relate to thought ?
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43. Foundation of AI
• Neuroscience:
• Physical substrate for mental activities.
• How do brains process information ?
• Control Theory:
• Homeostatic systems, stability, optimal agent design
• How can artifacts operate under their own control ?
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44. AI History
• The dream of making a computer imitate us began many centuries ago …
• Intellectual roots of AI stretch back thousands of years into the earliest studies of the nature of
knowledge and reasoning.
• The concept of Intelligent machines is found in Greek mythology.
• 8th century: Pygmalion
• Hephaestus created a huge robot, Talos to guard Crete.
• Philosophers have analyzed the nature of knowledge and have explored formal frameworks for
developing conclusions.
• Mathematical formalizations in logic, computation and probability
• Economists developed decision theory
• How does the brain process information?
• Psychologists have long studied human cognition
- Knowledge about the nature of human intelligence.
• How do we build an efficient computer ?
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45. AI History
• Aristotle (384-322 BC) developed an informal system of syllogistic logic, the first deductive
reasoning system.
• 13th century: Ramon Lull
A Spanish theologian, invented the idea of a machine that would produce all knowledge, by putting together
words at random. He even tried to build it.
• Early in the 17th century, Descartes proposed that bodies of animals are nothing more than
complex machines.
• Pascal (1642) – the first mechanical digital calculating machine.
• Leibniz (1673) improved Pascal’s machine.
• ADA LOVELACE (1842) – The world’s first programmer
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46. Background
• 19th Century: George Boole developed a binary algebra representing
(some) “laws of thought.”
• Charles Babbage & Ada Byron worked on programmable mechanical
calculating machines.
• In the late 19th century and early 20th century, mathematical philosophers
like
• Gottlob Frege,
• Bertram Russell,
• Alfred North Whitehead, and
• Kurt Godel
built on Boole’s initial logic concepts to develop mathematical
representations of logic problems.
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47. Advent of Computer
• The advent of electronic computers provided a revolutionary advance
in the ability to study intelligence.
• 1943 McCulloch & Pitts: Boolean circuit model of brain.
• “A Logical Calculus of Ideas Immanent in Nervous Activity” is
published.
• Explaining for the first time how it is possible for neural network to compute.
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48. SNARC
• Marvin Minsky and Dean Edmonds built SNARC in 1951
• A neural network computer
• Used 3000 vacuum tubes
• Network with 40 neurons
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49. Turing
• 1950 Turing’s “Computing Machinery and Intelligence” – articulated a
complete vision of AI
• Solving problems by searching through the space of possible
solutions, guided by heuristics.
• Illustrated his ideas on machine intelligence by reference to chess.
• Propounded the possibility of letting the machine alter its own
instructions so that machines can learn from experience.
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50. History
• 1952-1956: Samuel’s checkers program
• 1956: Newell & Simon’s Logic Theorist, widely considered to be the first AI program.
GPS (General Problem Solver)
• The birth of AI (1956)
• Dartmouth Workshop bringing together top minds on automata theory, neural nets and the study
of intelligence
• 1959: Gelernter’s Geometry Engine
• 1961: James Slagle (Ph.D. dissertation, MIT) wrote (in Lisp) the first symbolic integration
program, SAINT, which solved calculus problems at the college freshman level.
• 1963: Thomas Evan’s program Analogy to solve IQ test type analogy problems.
• 1963: Edward A. Feigenbaum & Julian Feldman published Computers and Thoughts, the
first collection of articles about artificial intelligence.
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51. History
• 1964: Danny Bobrow shows that computers can understand natural language well
enough to solve algebra word problems correctly
• ELIZA (1964 to 1966) is an early natural language processing computer program
which demonstrated the superficiality of communication between humans and
machines.
• Analogy (1960 – 1965) : Analogy
• 1965: J. Allen Robinson invented a mechanical proof procedure, the Resolution
Method, which allowed programs to work efficiently with formal logic as a
representation language.
• 1966-74 AI discovers computational complexity,
• 1967: DENDRAL program (Feigenbaum, Lederberg, Buchanan, Sutherland at
Standford) demonstrated to interpret mass spectra on organic chemical
compounds. First successful knowledge-based program for scientific reasoning.
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52. Analogy (1960 – 1965)
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53. History
• 1968- Marvin Minsky & Seymour Paper publish Perceptrons,
demonstrating limits of simple neural nets.
• 1969: SRI robot, Shakey, demonstrated combining locomotion, perception
and problem solving.
• 1969 – 79 Early development of knowledge based systems
• 1974: MYCIN (Standford) demonstrated the power of rule based systems
for knowledge representation and inference in medical diagnosis and
therapy (Diagnosis of blood infection)
• 1975: Sacerdoti developed a planning programs, ABSTRIPS
• Minsky – Frames as a representation of knowledge
• 1978 – Herbert Simon wins the Nobel Prize in Economics for his theory of
bounded rationality.
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54. History
• 1980s: Lisp Machines developed and marketed. First expert system
shells and commercial applications.
• 1985-95 Neural networks return to popularity
• 1988 – resurgence of probabilistic and decision-theoretic methods
Rapid increase in technical depth of mainstream AI, “Nouvelle AI”:
ALife, Gas, soft computing.
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55. History
• Early AI systems used general systems, little knowledge.
• Specialized knowledge required for rich tasks to focus reasoning.
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56. History
• 1990’s: Major advances in all areas of AI
• Machine learning, data mining
• Intelligent tutoring
• Case-based reasoning,
• Multi-agent planning, scheduling,
• Uncertain reasoning,
• Natural language understanding and translation,
• Vision, virtual reality, games, and other topics.
• Rod Brook’s COG project at MIT, with numerous collaborators, makes
significant progress in building a humanoid robot
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57. History
• Interactive robot pets (“smart toys”) become commercially available,
realizing the vision of the 18th cen. Novelty toy makers.
• 2000: the Nomad robot explores remote regions of Antarctica looking
for meteorite samples.
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