By AK PACHAURI ITM ALIGARH

1. Artificial Intelligence
A Presentation By
Ashok Kumar Pachauri
ITM ALIGARH

2. Our Working Definition of AI
Artificial intelligence is the study of how to make
computers do things that people are better at or
would be better at if:
• they could extend what they do to a World Wide
Web-sized amount of data and
• not make mistakes.

3. Why AI?
"AI can have two purposes. One is to use the power of
computers to augment human thinking, just as we use
motors to augment human or horse power. Robotics
and expert systems are major branches of that. The
other is to use a computer's artificial intelligence to
understand how humans think. In a humanoid way. If
you test your programs not merely by what they can
accomplish, but how they accomplish it, they you're
really doing cognitive science; you're using AI to
understand the human mind."
- Herb Simon

4. The Dartmouth Conference and the
Name Artificial Intelligence
J. McCarthy, M. L. Minsky, N. Rochester, and C.E.
Shannon. August 31, 1955. "We propose that a 2
month, 10 man study of artificial intelligence be
carried out during the summer of 1956 at
Dartmouth College in Hanover, New Hampshire.
The study is to proceed on the basis of the
conjecture that every aspect of learning or any
other feature of intelligence can in principle be
so precisely described that a machine can be
made to simulate it."

5. Time Line – The Big Picture
50 60 70 80 90 00 10
1956 Dartmouth conference.
1981 Japanese Fifth Generation project launched as the
Expert Systems age blossoms in the US.
1988 AI revenues peak at $1 billion. AI Winter begins.
academic $ academic and routine

6. The Origins of AI Hype
1950 Turing predicted that in about fifty years "an average
interrogator will not have more than a 70 percent chance of
making the right identification after five minutes of
questioning".
1957 Newell and Simon predicted that "Within ten years a
computer will be the world's chess champion, unless the rules
bar it from competition."

7. Evolution of the Main Ideas
•Wings or not?
•Games, mathematics, and other knowledge-poor tasks
•The silver bullet?
•Knowledge-based systems
•Hand-coded knowledge vs. machine learning
•Low-level (sensory and motor) processing and the resurgence
of subsymbolic systems
•Robotics
•Natural language processing

8. Symbolic vs. Subsymbolic AI
Subsymbolic AI: Model
intelligence at a level similar to
the neuron. Let such things as
knowledge and planning emerge.
Symbolic AI: Model such
things as knowledge and
planning in data structures that
make sense to the
programmers that build them.
(blueberry (isa fruit)
(shape round)
(color purple)
(size .4 inch))

9. The Origins of Subsymbolic AI
1943 McCulloch and Pitts A Logical Calculus of the Ideas
Immanent in Nervous Activity
“Because of the “all-or-none” character of nervous
activity, neural events and the relations among them can
be treated by means of propositional logic”

10. Interest in Subsymbolic AI
40 50 60 70 80 90 00 10

11. The Origins of Symbolic AI
• Games
•Theorem proving

12. Games
• 1950 Claude Shannon published a paper describing how
a computer could play chess.
• 1952-1962 Art Samuel built the first checkers program
• 1957 Newell and Simon predicted that a computer will
beat a human at chess within 10 years.
• 1967 MacHack was good enough to achieve a class-C
rating in tournament chess.
• 1994 Chinook became the world checkers champion
• 1997 Deep Blue beat Kasparpov
• 2007 Checkers is solved
• Summary

13. Games
• AI in Role Playing Games – now we need knowledge

14. Logic Theorist
• Debuted at the 1956 summer Dartmouth conference, although
it was hand-simulated then.
• Probably the first implemented A.I. program.
• LT did what mathematicians do: it proved theorems. It
proved, for example, most of the theorems in Chapter 2 of
Principia Mathematica [Whitehead and Russell 1910, 1912,
1913].
• LT began with the five axioms given in Principia
Mathematica. From there, it began to prove Principia’s
theorems.

15. Logic Theorist
• LT used three rules of inference:
• Substitution (which allows any expression to be
substituted, consistently, for any variable):
• From: A ∧ B → A, conclude: fuzzy ∧ cute → fuzzy
• Replacement (which allows any logical connective to be
replaced by its definition, and vice versa):
• From A → B, conclude ¬A ∨ B
• Detachment (which allows, if A and A → B are theorems,
to assert the new theorem B):
• From man and man → mortal, conclude: mortal

16. Logic Theorist
In about 12 minutes LT produced, for theorem 2.45:
¬(p ∨ q) → ¬p (Theorem 2.45, to be proved.)
1. A → (A ∨ B) (Theorem 2.2.)
2. p → (p ∨ q) (Subst. p for A, q for B in 1.)
3. (A → B) → (¬B → ¬A) (Theorem 2.16.)
4. (p → (p ∨ q)) → (¬(p ∨ q) → ¬p) (Subst. p for A, (p ∨ q) for B in 3.)
5. ¬(p ∨ q) → ¬p (Detach right side of 4, using 2.)
Q. E. D.

17. Logic Theorist
The inference rules that LT used are not complete.
The proofs it produced are trivial by modern standards.
For example, given the axioms and the theorems prior to it, LT
tried for 23 minutes but failed to prove theorem 2.31:
[p ∨ (q ∨ r)] → [(p ∨ q) ∨ r].
LT’s significance lies in the fact that it opened the door to the
development of more powerful systems.

18. Mathematics
1956 Logic Theorist (the first running AI program?)
1961 SAINT solved calculus problems at the college
freshman level
1967 Macsyma
Gradually theorem proving has become well enough
understood that it is usually no longer considered AI.

19. Discovery
AM “discovered”:
• Goldbach’s conjecture
• Unique prime factorization theorem

20. What About Things that People Do
Easily?
• Common sense reasoning
• Vision
• Moving around
• Language

21. What About Things People Do
Easily?
• If you have a problem, think of a past situation where you
solved a similar problem.
• If you take an action, anticipate what might happen next.
• If you fail at something, imagine how you might have done
things differently.
• If you observe an event, try to infer what prior event might
have caused it.
• If you see an object, wonder if anyone owns it.
• If someone does something, ask yourself what the person's
purpose was in doing that.

22. They Require Knowledge
•Why do we need it?
•How can we represent it and use it?
•How can we acquire it?
Find me stuff about dogs who save people’s lives.

23. Why?
•Why do we need it?
•How can we represent it and use it?
•How can we acquire it?
Find me stuff about dogs who save people’s lives.
Two beagles spot a fire.
Their barking alerts
neighbors, who call 911.

24. Even Children Know a Lot
A story described in Charniak (1972):
Jane was invited to Jack’s birthday party. She wondered if
he would like a kite. She went into her room and shook her
piggy bank. It made no sound.

25. We Divide Things into Concepts
• What’s a party?
• What’s a kite?
• What’s a piggy bank?

26. What is a Concept?
Let’s start with an easy one: chair

27. Chair?

28. Chair?

29. Chair?

30. Chair?

31. Chair?

32. Chair?

33. Chair?

34. Chair?

35. Chair?

36. Chair?

37. Chair?

38. Chair?

39. Chair?

40. Chair?

41. Chair?
The bottom line?

42. How Can We Teach Things to Computers?
A quote from John McCarthy:
In order for a program to be capable of learning something,
it must first be capable of being told it.
Do we believe this?

43. Some Things are Easy
If dogs are mammals and mammals are animals, are dogs
mammals?

44. Some Things Are Harder
If most Canadians have brown eyes, and most brown eyed people
have good eyesight, then do most Canadians have good eyesight?

45. Some Things Are Harder
If most Canadians have brown eyes, and most brown eyed people
have good eyesight, then do most Canadians have good eyesight?
Maybe not for at least two reasons:
It might be true that, while most brown eyed people have good
eyesight, that’s not true of Canadians.
Suppose that 70% of Canadians have brown eyes and 70% of
brown eyed people have good eyesight. Then assuming that
brown-eyed Canadians have the same probability as other brown-
eyed people of having good eyesight, only 49% of Canadians are
brown eyed people with good eyesight.

46. Concept Acquisition
Pat Winston’s program (1970) learned concepts in the
blocks micro-world.

47. Concept Acquisition
The arch concept:

48. Further Complications from How
Language is Used
• After the strike, the president sent them away.
• After the strike, the umpire sent them away.
The word “strike” refers to two different concepts.

49. When Other Words in Context Aren’t
Enough
• I need a new bonnet.
• The senator moved to table the bill.

50. Compiling Common Sense
Knowledge
• CYC (http://www.cyc.com)
• UT (http://www.cs.utexas.edu/users/mfkb/RKF/tree/ )
• WordNet (http://www.cogsci.princeton.edu/~wn/)

51. Distributed Knowledge Acquisition
• Acquiring knowledge for use by people
• Oxford English Dictionary
(http://oed.com/about/contributors/ )
• Wikipedia
• Acquiring knowledge for use by programs
• ESP (http://www.espgame.org/)
• Open Mind (http://commons.media.mit.edu:3000/)
• CYC (http://www.cyc.com)

52. Reasoning
We can describe reasoning as search in a space of
possible situations.

53. Breadth-First Search

54. Depth-First Search

55. The British Museum Algorithm
A simple algorithm: Generate and test
When done systematically, it is basic depth-first search.
But suppose that each time we end a path, we start over at the
top and choose the next path randomly. If we try this long
enough, we may eventually hit a solution. We’ll call this
The British Museum Algorithm or
The Monkeys and Typewriters Algorithm
http://www.arn.org/docs2/news/monkeysandtypewriters051103.htm

56. A Version of Depth-First Search:
Branch and Bound
Consider the problem of planning a ski vacation.
Fly to A $600 Fly to B $800 Fly to C $2000
Stay D $200
(800)
Stay E $250
(850)
Total cost
(1200)

57. Problem Reduction
Goal: Acquire TV
Steal TV Earn Money Buy TV
Or another one: Theorem proving in which we reason backwards
from the theorem we’re trying to prove.

58. Hill Climbing
Problem: You have just arrived in Washington, D.C.
You’re in your car, trying to get downtown to the
Washington Monument.

59. Hill Climbing – Some Problems

60. Hill Climbing – Is Close Good Enough?
A
B
Is A good enough?
• Choose winning lottery numbers

61. Hill Climbing – Is Close Good Enough?
A
B
Is A good enough?
• Choose winning lottery numbers
• Get the cheapest travel itinerary
• Clean the house

62. Expert Systems
Expert knowledge in many domains can be captured as rules.
Dendral (1965 – 1975)
If: The spectrum for the molecule has two peaks at masses x1 and
x2 such that:
• x1 + x2 = molecular weight + 28,
• x1 -28 is a high peak,
• x2 – 28 is a high peak, and
• at least one of x1 or x2 is high,
Then: the molecule contains a ketone group.

63. To Interpret the Rule
Mass spectometry
Ketone group:

64. Expert Systems
1975 Mycin attaches probability-like numbers to rules:
If: (1) the stain of the organism is gram-positive, and
(2) the morphology of the organism is coccus, and
(3) the growth conformation of the organism is clumps
Then: there is suggestive evidence (0.7) that the identity of
the organism is stphylococcus.

65. Expert Systems – Today: Medicine
One example domain, medicine, has expert systems whose
tasks include:
• arrhythmia recognition from electrocardiograms
• coronary heart disease risk group detection
• monitoring the prescription of restricted use antibiotics
• early melanoma diagnosis
• gene expression data analysis of human lymphoma
• breast cancer diagnosis

66. Expert Systems – Today: Build Your Own
eXpertise2Go: http://www.expertise2go.com/
AcquiredIntelligence: http://www.aiinc.ca/demos/
(whales, graduate school)

67. Taking the AP Chemistry Exam
QUESTION: Sodium azide is used in air bags to rapidly produce
gas to inflate the bag. The products of the decomposition reaction
are:(a) Na and water.(b) Ammonia and sodium metal.(c) N2 and
O2(d) Sodium and nitrogen gas.(e) Sodium oxide and nitrogen
gas.
(d) Sodium and nitrogen gas.

68. SYSTEM-GENERATED ANSWER:
# What are the products of the given decomposition reaction?
•By definition, oxidation-reduction reactions occur when electrons are
transferred from the atom that is oxidized to the atom that is reduced. We need to
look for changes in the oxidation states of the elements in the reaction.
•In the reactants, the oxidation state(s) of the element Na is/are (1). In the
product, the oxidation state(s) is/are (0).
Therefore, the reaction causes a change in oxidation state.
•Therefore, this is an oxidation reduction reaction.
•By definition, a Binary Ionic-Compound Decomposition Reaction occurs when
a binary ionic compound is heated.
•Therefore, this reaction is a Binary-Ionic Compound Decomposition reaction.
•In general, a Binary Ionic-Compound Decomposition Reaction converts a
binary ionic-compound into basic elements.
•In this reaction, NaN3 reacts to produce Na and N2.
# The products of the decomposition reaction are:
(d) Sodium and nitrogen gas.
The work of Bruce Porter et al here at UT