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Transcript
Artificial Intelligence
CIS 342
The College of Saint Rose
David Goldschmidt, Ph.D.
What is Artificial Intelligence?
Definitions of Intelligence

Essential English Dictionary, Collins, London, 1990:
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Ability to understand and learn things
Ability to think and understand instead of doing
things by instinct or automatically
Random House Unabridged Dictionary, 2006:
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Capacity for learning, reasoning, understanding
Aptitude in grasping truths, relationships, facts, etc.
The Turing Test
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Alan Turing, British mathematician (1912-1954)
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“Computing machinery and intelligence” paper in 1950
Can machines think?
The Turing Test (a.k.a. Turing imitation game):
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A computer passes the Turing test if
human interrogators cannot distinguish
the machine from a human based on
answers to their questions
The Turing Test

Turing Test
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Objective standard view on intelligence
Test is independent of the details of the experiment
(i.e. numerous variations)
Provides basis for verification and validation of
intelligent systems
A program thought intelligent in some narrow area of
expertise is evaluated by comparing its performance to
human performance
The Turing Test in Action…
History of AI

Warren McCulloch & Walter Pitts (1943):
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Research on the human central nervous system led to a
model of neurons of the brain
Birth of Artificial Neural Networks (ANN)
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Binary model
Non-linear model
John von Neumann
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ENIAC, EDVAC, etc.
History of AI

Claude Shannon, MIT, Bell Labs (1950):
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Computers playing chess
Chess game involved about 10120 possible moves!
Even examining one move per microsecond would
require 3 x 10106 years to make its first move
Need to incorporate intelligence via heuristics
History of AI

John McCarthy, Dartmouth, MIT (1950s):
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Defined LISP
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Only two years after FORTRAN
LISP is based on formal logic
“Programs with Common Sense” paper (1958)
Marvin Minsky, Princeton, MIT:
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Anti-logical approach to knowledge representation
and reasoning called frames (1975)
Evolution of
Programming Languages
History of AI

Great expectations during 1950s and 1960s
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But very limited success
Researchers focused too much on all-purpose
intelligent machines with goals to learn and reason
with human-scale knowledge (and beyond)
Refocus on specific problem domains (1970s)
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Domain-specific expert systems with facts, rules, etc.
Analyze chemicals, medical diagnoses, etc.
History of AI
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Evolutionary computation (1970s-today):
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Natural intelligence is a product of evolution
Can we solve problems by simulating
biological evolution?
Survival of the fittest
Genetic programming
Evolutionary computing
History of AI

Rebirth of neural networks (1980s-today):
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Adaptive resonance theory (Grossberg, 1980) incorporated
self-organization principles
Hopfield networks (Hopfield, 1982)
introduced neural networks with
feedback loops
Back-propagation learning algorithm
(Bryson and Ho, 1969) for training
multilayer perceptrons
History of AI

Knowledge engineering (1980s-today):
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Fuzzy set theory (Zadeh, 1965) associates words
with degrees of truth or value
Rule-based knowledge systems
Combine information from multiple experts
Semantic Web
Numerous hybrid approaches exist