3. Grading
• Grade Distribution
– Midterm 1 - 20
– Midterm 2 – 20
– Project – 20
– Final Exam – 40
• Midterm 1 Date
– Mod 3/1/1435
• Midterm 2 Date
– Mod 3/3/1435
• Project
– Due in Last Week
4. Warning!!!
Any form of cheating is not tolerated and can result in getting an F
in the class
5. Important Notes
• No class next week - Week of Sep 8
• Tutorials may not be held on its scheduled time
• We may have lectures on the tutorial sessions
or tutorials on lecture sessions
6. AI in Fiction
An intelligent killing robot
Smart machines that took over
the human race and made
them live in a simulated world
7. What’s interesting with AI
Search engines
Labor
Science
Medicine/
Diagnosis
Appliances
slide mostly borrowed from Laurent Itti
Movies Recommendation
8. What’s interesting with AI
• Honda AISMO
• Advanced Step in Innovation MObility
• Humanoid Robot
• Capable of recognizing:
• Moving objects
• Postures
• Gestures
• Handshake
• Sounds
• Capable of walking and running
http://en.wikipedia.org/wiki/ASIMO
9. What’s interesting with AI
Darpa Grand Challenge
• To nurture the development of autonomous ground vehicles
• Competition of Driverless vehicles
• 2004
• 1 million
• Mojave Desert
• Follows a route of 240 km
• No one won: best completed 12 km
• 2005
• 2 million dollar prize
• 3 narrow tunnels, 100 sharp turns
• Twisted pass with a drop-off one one side
• Five succeeded
• Winner: 6:54 hours, Stanford Racing Team – Stanely
Urban Grand Challenge
• 2007
• 2 million dollar
• AirForce Base
• To obey to all traffic rules
• 96 km within less than 6 hours
• CMU team won – with 4:10
http://en.wikipedia.org/wiki/DARPA_Grand_Challenge
stanely
10. What’s interesting with AI
• 1996, Deep Blue first machine to beat chess world champion
• But lost in the series – 4 to 2
• 1997, won the series 3.5 to 2.5
• Search 6 to 8 moves a head
• The evaluation function is set by the system after examining thousands of master
games
http://en.wikipedia.org/wiki/Deep_Blue_(chess_computer)
11. Syllabus - Tentative
1. Introduction (Chapter.1)
2. Intelligent Agents (Chapter.2)
3. Solving Problems by Search (Chapter.3 and chapter.4)
4. Constraint satisfaction Problems (Chapter.6).
5. Game Playing(Chapter.5)
6. Logical Agents (Chapter.7)
7. First Order Logic (Chapter.8)
8. Inference in logic (Chapter.9)
9. Classification
13. AI Definition
• The exciting new effort to make computers thinks …
machine with minds, in the full and literal sense” (Haugeland
1985)
• The automation of activities that we associate with human
thinking, activities such as decision-making, problem
solving, learning,…(Bellman, 1978)
Think Like Humans
14. AI Defintion
• “The art of creating machines that perform functions that
require intelligence when performed by people” (Kurzweil,
1990)
• “The study of how to make computers do things at which, at
the moment, people do better”, (Rich and Knight, 1991)
Act Like Humans
15. AI Definition
• “The study of mental faculties through the use of
computational models”,(Charniak et al. 1985)
• “The study of the computations that make it possible to
perceive, reason and act”,(Winston, 1992)
Think Rationally
16. AI Definition
• “Computational Intelligence is the study of the design of
intelligent agents” (Poole et al, 1998)
• “AI….is concerned with intelligent behavior in artifact”,
(Nilsson, 1998)
Act Rationally
17. 17
How to Achieve AI?
AI
Acting
humanly
Thinking
rationally
Acting
rationally
Thinking
humanly
18. CSC 361 Artificial Intelligence 18
Acting Humanly: The Turing Test
• To be intelligent, a program should simply act like a human
Alan Turing
1912-1954
http://en.wikipedia.org/wiki/Turing_test
19. The Turing Test - Example
http://www.ai.mit.edu/projects/infolab/
http://aimovie.warnerbros.com
slide mostly borrowed from Laurent Itti
20. The Turing Test - Example
http://www.ai.mit.edu/projects/infolab/
http://aimovie.warnerbros.com
slide mostly borrowed from Laurent Itti
21. The Turing Test - Example
http://www.ai.mit.edu/projects/infolab/
http://aimovie.warnerbros.com
slide mostly borrowed from Laurent Itti
22. The Turing Test - Example
http://www.ai.mit.edu/projects/infolab/
http://aimovie.warnerbros.com
slide mostly borrowed from Laurent Itti
23. The Turing Test - Example
http://www.ai.mit.edu/projects/infolab/
http://aimovie.warnerbros.com
slide mostly borrowed from Laurent Itti
24. 24
Acting Humanly
• To pass the Turing test, the computer/robot needs:
– Natural language processing to communicate successfully.
– Knowledge representation to store what it knows or hears.
– Automated reasoning to answer questions and draw conclusions using
stored information.
– Machine learning to adapt to new circumstances and to detect and
extrapolate patterns.
– These are the main branches of AI.
25. Acting Humanly: The Turing Test
CSC 361 Artificial Intelligence 25
• To be intelligent, a program should simply act like a human
Alan Turing
1912-1954
http://en.wikipedia.org/wiki/Turing_test
+ physical interaction =>
Total Turing Test
- Recognize objects
and gestures
- Move objects
26. Acting Humanly – for Total Turing
• To pass the Turing test, the computer/robot needs:
– Natural language processing to communicate successfully.
– Knowledge representation to store what it knows or hears.
– Automated reasoning to answer questions and draw conclusions using stored
information.
– Machine learning to adapt to new circumstances and to detect and extrapolate patterns.
– Computer vision to perceive objects. (Total Turing test)
– Robotics to manipulate objects and move. (Total Turing test)
– These are the main branches of AI.
27. 27
Thinking Humanly
• Real intelligence requires thinking think like a
human !
• First, we should know how a human think
– Introspect ones thoughts
– Physiological experiment to understand how someone thinks
– Brain imaging – MRI…
• Then, we can build programs and models that think like
humans
– Resulted in the field of cognitive science: a merger between
AI and psychology.
28. 28
Problems with Imitating Humans
• The human thinking process is difficult to
understand: how does the mind raises from
the brain ? Think also about unconscious tasks
such as vision and speech understanding.
• Humans are not perfect ! We make a lot of
systemic mistakes:
29. 29
Thinking Rationally
• Instead of thinking like a human : think rationally.
• Find out how correct thinking must proceed: the laws of
thought.
• Aristotle syllogism: “Socrates is a man; all men are mortal,
therefore Socrates is mortal.”
• This initiated logic: a traditional and important branch of
mathematics and computer science.
• Problem: it is not always possible to model thought as a set
of rules; sometimes there uncertainty.
• Even when a modeling is available, the complexity of the
problem may be too large to allow for a solution.
30. 30
Acting Rationally
• Rational agent: acts as to achieve the best outcome
• Logical thinking is only one aspect of appropriate behavior:
reactions like getting your hand out of a hot place is not the result
of a careful deliberation, yet it is clearly rational.
• Sometimes there is no correct way to do, yet something must be
done.
• Instead of insisting on how the program should think, we insist on
how the program should act: we care only about the final result.
• Advantages:
– more general than “thinking rationally” and more
– Mathematically principled; proven to achieve rationality unlike human
behavior or thought
31. 31
Acting Rationally
This is how birds fly Humans tried to mimic
birds for centuries
This is how we finally
achieved “artificial flight”
32. CSC 361 Artificial Intelligence 32
Relations to Other Fields
• Philosophy
– Logic, methods of reasoning and rationality.
• Mathematics
– Formal representation and proof, algorithms, computation, (un)decidability, (in)tractability, probability.
• Economics
– utility, decision theory (decide under uncertainty)
• Neuroscience
– neurons as information processing units.
• Psychology/Cognitive Science
– how do people behave, perceive, process information, represent knowledge.
• Computer engineering
– building fast computers
• Control theory
– design systems that maximize an objective function over time
• Linguistics
– knowledge representation, grammar
slide mostly borrowed from Max Welling
33. AI History
• Gestation of AI (1934 - 1955)
– In 1943, proposed a binary-based model of neurons
– Any computable function can be modeled by a set of neurons
– A serious attempt to model brain
– 1950, Turing’s “Computing Machinery and Intelligence ”: turing test, reinforcement learning
and machine learning
• The Inception of AI (1956)
– Dartmouth meeting to study AI
– an AI program ”Logic Theorist” to prove many theorems
• Early Enthusiasm and great Expectation (1952-1969)
– General Problem Solver imitates the human way of thinking
– LISP (AI programming language) was defined
– 1965, Robinson discovered the resolution method – logical reasoning
• AI Winter (1966-1973)
– Computational intractability of many AI problems
– Neural Network starts to disappear
34. AI History
• Knowledge-based systems (1969-1979)
– Use domain knowledge to allow for stronger reasoning
• Becomes an Industry (1980-now)
– Digital Equipment Corporation selling R1 “expert sytem”
– From few million to billions in 8 years
• The return of neural network (1986-now)
– With the back-propagation algorithm
• AI adopts scientific method (1987-now)
– More common to base theorems on pervious ones or rigorous evidence rather than
intuition
– Speech recognition and HMM
• Emergence of intelligent agent (1995-now)
– search engines, recommender systems,….
• Availability of very large data sets (2001 – now)
– Worry more about the data
35. The State of the Art
• Robotics Vehicle
– DARPA Challenge
• Speech Recognition
– United Airlines
• Autonomous Planning and Scheduling
– Remote Agent: Plan and control spacecraft
– MAPGEN: daily planning of operations on NASA’s exploration Rover
• Game Playing
– IBM Deep Blue
• Spam Fighting
• Logistic Planning
– DART – Dynamic Analysis and Replacing Tool
– Gulf War 1991
– To plan the logistic for transportation of 50k vehicles, cargo and people
– Generated in hour a plan that could take weeks
• Robotics
• Machine Translation
– Statistical models
36. CSC 361 Artificial Intelligence 36
Summary
• This course is concerned with creating rational agents: artificial
rationality.
• AI has passed the era of infancy and is now attacking real life,
complex problems, and it is succeeding in many of them.
• The history of AI has had a turbulent history with many ups and
downs, phenomenal successes and deep disappointments
resulting in fund cutbacks and economic losses.
• AI has flourished in the last two decades and it the researchers
mentality shifted towards a rigorous scientific methodology:
Firm theoretical basis & Serious experiments
