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Artificial Intelligence
What is Artificial Intelligence ?
Attempts to understand and build intelligent entities
Introduction
There are two main ingredients in the many definition of
Artificial Intelligence:
Chapter 1 & 26
– Thought processes and reasoning (Thinking)
– Behavior and performance (Acting)
Both can either use humans as ideal or an optimal
rationality as ideal
AI Involves Many Sciences
Neuroscience (biology)
– “how does the brain work”
Cognitive science (behavior science)
– “what is intelligence”
Mechanically science (engineers)
– “how do we build robots”
Artificial Life (ecology)
– “how do build lives”
Adaptation (mathematic)
– “how can a system learn”
Rationality (economic)
– “how should we act”
Why Study AI?
One reason to study it is to learn more about ourselves
In this course we will
talk about:
Another reason is that these constructed intelligent entities
are interesting and useful in their own right
AI has produced many significant and impressive products
Building agents!
Goals of AI
It is clear that computers with human-level intelligence
would have huge impact on our everyday lives
Four Definitions of AIntelligence
The engineering goal is to develop the concepts and
practices of building intelligent machines. Emphasis is on
Acting humanly: The Turing test approach
system building
Thinking humanly: The cognitive modeling approach
The science goal is to develop concepts, theory, and
mechanisms to model intelligence. Emphasis on
understanding and automating intelligent behavior
Thinking rationally: The laws of thought approach
Acting rationally: The rational agent approach
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Four Faces of AI Research
Human vs. Rational
Thinking
Thinking rationally:
The laws of thought
approach
Acting humanly:
The Turing test approach
Acting rationally:
The rational agent approach
Should computers emulate human thought?
Rationally
Humanly
Thinking humanly:
The cognitive modeling
approach
– We don’t even know how that works!!
– Maybe we should just approximate thought by making
computers arrive at conclusions in a rational way
Should computers emulate human behavior?
– Behaviorist approach: exemplified by the Turing Test
Acting
Acting Humanly:
The Turing Test
Thinking vs. Acting
Does the machine need to actually think?
Playing a game of tic-tac-toe or chess
Speech recognition for airline reservations
Amazon.com’s product suggestions
Medical diagnosis system
Automated steering of a vehicle
Etc…
The Interrogator
–
–
–
–
–
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Am I talking to a
computer or to a
human being?
Can machines think?
Can machines behave intelligently?
Problem: Turing test is not reproducible, constructive or amenable to
mathematical analysis
Thinking Humanly:
Cognitive Science
Thinking Rationally:
Law of Thoughts
Study human (and animal) behavior
“Socrates is a man; all men are mortal; therefore Socrates
is mortal”
Initiated the field called “logic”
1960s “cognitive revolution”: information-processing
psychology replaced prevailing orthodoxy of behaviorism
“Logicist” tradition
Requires scientific theories of internal activities of the brain
Problems:
Both approaches (roughly, Cognitive Science and Cognitive
Neuroscience) are now distinct from AI
– Not all intelligent behavior is mediated by logical
deliberation
– Solve problems in principal – practice. It needs guidance
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Acting Rationally
Rational Agents
Rational behavior: Doing the right thing
An agent is an entity that perceives and acts
This course is about designing rational agents
Abstractly, an agent is a function from percept histories to
actions:
The right thing: that which is expected to maximize goal
achievement, given the available information
f : P* → A
Does not necessary involve thinking – e.g., - blinking
reflex- but should be in the service of rational action
Rational Agents
For any given class of environment and tasks, we seek
the agent (or class of agents) with the best
performance
Caveat: computational limitations make perfect
rationality unachievable -> design best program for
given machine resources
The Foundations of AI
Philosophy (428 B.C.-present)
– Logic, methods of reasoning
– Mind as physical systems
Mathematics(c.800-present)
– Formal representation and proof
– Algorithms, computation, (un)decidability, (in)tractability
– Probability
Economics(1776-present)
– Formal theory of rational decisions
Neuroscience(1861-present)
– Memory, Cognitive processes
The Foundations of AI
Psychology(1879-present)
– Behaviorism
– Cognitive psychology
– Cognitive science
Computer engineering(1940-present)
Control Theory(1948-present)
– Simple optimal agent designs
History of AI
The gestation of AI
– McCulloch, Pitts (1943) artificial neurons
• artificial neurons with binary state
• can compute any Boolean function
– Hebbian learning
– SNARC: first neural network computer
– Alan Turing (1950s) Turing Test
Linguistics(1957-present)
– Knowledge representation
– Grammar
The first AI event:
– Workshop at Dartmouth College 1956: naming “AI”
– Logic Theorist: first reasoning system
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Early Enthusiasm (1952-1969)
Early Enthusiasm (1952-1969)
“Look, Ma, no hands” era:
“Look, Ma, no hands” era:
John McCarthy:
Gelernter:
– LISP, Advice Taker, named the field AI.
Marvin Minsky:
– Microworlds (incl. IQ-Tests, Blocks World). p.20 fig 1.5
Arthur Samuel:
– Game of checkers
Newell, Simon:
– Geometry Theorem Prover
Widrow:
– Adalines
Rosenblatt:
– Perceptrons (convergence theorem)
– GPS: General Problem Solver
– Physical symbol system hypothesis
– Imitates human problem solving
A Dose of Reality (1966-1973)
Difficulties with early systems:
KB Systems: The Key to Power? (1969-1979)
Knowledge-based systems:
– DENDRAL: chemical analysis
– Programs contained little or no knowledge of their
subject matter
– Intractability of many of the addressed problems
– Fundamental limitations on the basic structures
• First knowledge-intensive system
Expert systems:
– MYCIN: medical diagnosis
• Rule-based system with uncertainty factors
Frames:
– Taxonomic hierarchies
AI Becomes an Industry (19801988)
AI becomes an industry
– First commercially successful expert system: R1
This fuelled interest in AI, both in the academia and the industry.
Disillusionment concerning expert systems:
AI Becomes an Industry (19801988)
The return of neural networks
– Reinvention of back-propagation learning
AI becomes a science
– Hypothesis + rigorous experiments = results
– Too much work to fill them with relevant rules
Intelligent agents
Overall, The AI industry boomed from a few million in 1980 to billions
of dollars in 1988. Soon after that came a period called
“AI winter” – companies suffered as they failed to deliver on the
extravagant promises – squeezed funding for research
– Return to the “whole agent” problem
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Recent Events (1987-
The State of the Art
New systems are built on existing theories and not on brand new
ones.
Planning and scheduling: NASA
Examples:
− Speech recognition (HMMs)
− Character recognition
Game playing: Deep Blue (Chess playing)
− Planning and Reasoning (efficient representation, and
learning -> rigorous reasoning with uncertain
knowledge)
– Beats human grandmasters Kasparov
Autonomous control: ALVINN
Next step:
Integrating to complete agents: SOAR, Newell (1990)
The State of the Art
Diagnosis
Application: Game Playing
IBM’s Deep Blue
Logistics planning: DART
– First AI to beat a human chess champion: Garry Kasparov,
1997
Blondie24
Robotics: HipNav
Language understanding and problem-solving:
PROVERB
Application: Logistics Planning
Trip itineraries
– Engines such as MapQuest use AI to propose driving directions
from one location to another
Dynamic Analysis and Replanning Tool (DART)
– Used during the 1991 Persian Gulf crisis to assist in managing
military resources (over 50,000 people, vehicles, and cargo
shipments)
– Machine learning program that won a checker’s tournament
Commercial game AI
– Increase in more sophisticated AI work for “non-academic”
games
Application: Speech Recognition
Airline reservation systems
– Often robust to many different voice pitches and accents
Automatic transcription
– Monitor language and content for live radio and television
– Assist in the transcribing of closed-captioned television
programs
Airline flight scheduling
– If flights are delayed or re-routed, AI planners are used to figure
the best way to re-schedule departures and arrivals
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Application: Text Processing
Automated language translation
– Altavista’s Babelfish server
Application: Biology & Medicine
Diagnosis systems
– Specialists often use statistical AI tools to diagnose a patient
has a disease based on his/her symptoms
Information retrieval
– Google search engine
Text classification and organization
– Google news, SPAM filtering
Genome analysis software
– Now that the human and other genomes are complete, AI is
used to identify new genes, infer biochemical pathways, and
compare genomes of multiple species
Document summarization
– Columbia University’s Newsblaster
Application: Vision
Handwriting recognition
– US Postal Service automatically sorts mail
Face recognition
Government/bank security systems
Autonomous Land Vehicle In a Neural Network (ALVINN)
– Uses camera data to automatically steer a car on a highway
at speeds up to 65 mph (from Washington, DC to San Diego
and back!)
Conscious Computers?
Weak AI - for the hypothesis that machines could
possibly behave intelligently
– Conscious Computers? “No way!”
Strong AI - for the hypothesis that such machines would
count as having actual minds (as opposed to simulated minds)
– Conscious Computers? “Of course”
Alan Turing rejected the question:
– Can machines think?" - replaced it with a behavioral test
– He anticipated many objections to the possibility of
thinking machines
The Ethics and Risk
Should We Develop AI?
Philosophical Foundation
Few AI researchers pay attention to the Turing test –
concentrate on their systems' performance on practical
tasks, rather than the ability to imitate humans
Arguments for and against strong AI are inconclusive
Consciousness remains a mystery
Six potential threats to society posed by AI and related
technology:
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People might lose their jobs
People might have too much (or too little) leisure time
People might lose their sense of being unique
People might lose some of their privacy rights
The use of AI systems might result in a loss of
accountability
– The success of AI might mean the end of human race
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Next!
Intelligent Agents!!
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