Artificial Intelligence

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Artificial Intelligence
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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.

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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
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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."
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Time Line The Big Picture
academic academic and routine
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.
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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."
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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

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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))
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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
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Interest in Subsymbolic AI
40 50 60 70 80 90
00 10
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The Origins of Symbolic AI

• Games
• Theorem proving

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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

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Games

• AI in Role Playing Games now we need knowledge

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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
  Principias theorems.

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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

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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.
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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. LTs
significance lies in the fact that it opened the
door to the development of more powerful systems.
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Mathematics
1956 Logic Theorist (the first running AI
program?) 1961 SAINT solved calculus problems at
the college freshman level 1967 Macsyma Gradu
ally theorem proving has become well enough
understood that it is usually no longer
considered AI.
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Discovery

• AM discovered
• Goldbachs conjecture
• Unique prime factorization theorem

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What About Things that People Do Easily?

• Common sense reasoning
• Vision
• Moving around
• Language

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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.

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They Require Knowledge

• Why do we need it?

Find me stuff about dogs who save peoples lives.

• How can we represent it and use it?
• How can we acquire it?

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Why?

• Why do we need it?

Find me stuff about dogs who save peoples lives.
Two beagles spot a fire. Their barking alerts
neighbors, who call 911.

• How can we represent it and use it?
• How can we acquire it?

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Even Children Know a Lot
A story described in Charniak (1972) Jane was
invited to Jacks birthday party. She wondered
if he would like a kite. She went into her room
and shook her piggy bank. It made no sound.
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We Divide Things into Concepts

• Whats a party?
• Whats a kite?
• Whats a piggy bank?

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What is a Concept?
Lets start with an easy one chair
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Chair?
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Chair?
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Chair?
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Chair?
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Chair?
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Chair?
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Chair?
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Chair?
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Chair?
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Chair?
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Chair?
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Chair?
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Chair?
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Chair?
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Chair?
The bottom line?
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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?
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Some Things are Easy
If dogs are mammals and mammals are animals, are
dogs mammals?
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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?
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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, thats
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.
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Concept Acquisition
Pat Winstons program (1970) learned concepts in
the blocks micro-world.
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Concept Acquisition
The arch concept
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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.
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When Other Words in Context Arent Enough

• I need a new bonnet.
• The senator moved to table the bill.

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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/)

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Distributed Knowledge Acquisition

• Acquiring knowledge for use by people
• Oxford English Dictionary (http//oed.com/abou
  t/contributors/ )
• Wikipedia
• Acquiring knowledge for use by programs
• ESP (http//www.espgame.org/)
• Open Mind (http//commons.media.mit.edu3000/)
• CYC (http//www.cyc.com)

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Reasoning
We can describe reasoning as search in a space of
possible situations.
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Breadth-First Search
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Depth-First Search
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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. Well call
this The British Museum Algorithm or The
Monkeys and Typewriters Algorithm http//www.arn.o
rg/docs2/news/monkeysandtypewriters051103.htm
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A Version of Depth-First SearchBranch 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)
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Problem Reduction
Goal Acquire TV
Steal TV
Earn Money
Buy TV
Or another one Theorem proving in which we
reason backwards from the theorem were trying to
prove.
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Hill Climbing
Problem You have just arrived in Washington,
D.C. Youre in your car, trying to get downtown
to the Washington Monument.
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Hill Climbing Some Problems
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Hill Climbing Is Close Good Enough?
B
A

• Is A good enough?
• Choose winning lottery numbers

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Hill Climbing Is Close Good Enough?
B
A

• Is A good enough?
• Choose winning lottery numbers
• Get the cheapest travel itinerary
• Clean the house

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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.

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To Interpret the Rule
Mass spectometry Ketone group
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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.
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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

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Expert Systems Today Build Your Own
eXpertise2Go http//www.expertise2go.com/ Acquir
edIntelligence http//www.aiinc.ca/demos/
(whales, graduate school)
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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.
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• 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