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Transcript of Lecture#1-Introduction to AI
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Department of Computer & Information Sciences
Pakistan Institute of Engineering and Applied Sciences
Intelligence
Chapter 1
1
Umar Faiz
http://www.pieas.edu.pk/umarfaiz
Artificial Intelligence
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Outline
Understand the definition of artificial intelligence
n ers an e eren acu es nvo ve w
intelligent behavior Examine the different ways of approaching AI
Trace briefly the history of AI
Study types of problems that can be currently solved
ability.
Summary
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What is AI?
Intelligence A property of mind that encompasses many related
abilities: The capacities to reason, to plan, to solve problems, to think
abstractly, to comprehend ideas, to use language, and tolearn.
Creativity, personality, character, knowledge, or wisdom.
3Source:Wikipedia
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What is AI ?
Artificial Intelligence is concerned with the designo n e gence n an ar c a ev ce.
The term was coined by McCarthy in 1956.
There are two ideas in the definition.1. Intelli ence
2. Artificial device
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What is AI ?
What is intelligence? Something that characterizes humans from all other
beings?
Criteria to measure intelli ence or an absolutestandard of judgment for intelligence?
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What is AI?
What is intelligence? Regarding intelligence, there are two possibilities:
A system with intelligence is expected to behave asintelligently as a human.
A system with intelligence is expected to behave in the bestpossible manner.
Regarding behavior, are we are interested in The thought process or reasoning ability of the system, or
The final manifestations of the system in terms of its actions.
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Intro to AI
Major Areas of AI Deduction, reasoning, problem solving
Knowledge representation
Learning
Natural language processing Motion and manipulation
Social intelligence
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Intro to AI
Tools of AI Search
Logic
Clustering and classification
Neural networks Genetic algorithms
Reasoning tools
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Intro to AI
AI Languages Scheme / LISP
Functional
Simple knowledge representation (list)
Prolog
Logic-based
Built-in search engine
Specialized languages Rule lan ua es e. . CLIPS
Planning languages (e.g. STRIPS)
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Intro to AI
Definitions four ma or
combinations Based on thinking or
acting.
Based on activity likeSystems
that think
Systems
that think
rational way.
Systems Systems
humans rationally
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Intro to AI
1. Acting Humanly Turing Test
Who is Turing? Inventor of modern computers
Turing Thesis Algorithms Turing machines Systems that
think like
Systems that
think
Systems that Systems that
act likehumans
act rationally
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Intro to AI
1. Acting Humanly e ur ng es as ree
participants -- two subjects and ajudge. One of the subjects is a
erson and the other is a com uter.Both subjects are hidden from theview of the judge. They communicate
with the judge via text-only channels.
which text channel corresponds tothe human and which corresponds tothe com uter. If the ud e cannot
determine this, then the computerpasses the test.
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Intro to AI
An Application of the Turing Test - CAPTCHA:CAPTCHA:
CompletelyAutomatic Public Turing tests to tell Computersand HumansApart
e.g.: Display visually distorted words
Ask user to recognize these words xamp e o app ca on: ave on y umans open ema
accounts
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Intro to AI
An Application of the Turing Test - CAPTCHA:
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Intro to AI
1. Acting Humanly No program has yet passed Turing test!
(Annual Loebner competition & prize.)
A ro ram that succeeded would need to be ca ableof:
Natural language processing: To enable it to communicatesuccessfully in English.
Knowledge representation: To store what it knows or hears
Automated reasoning: To use the stored information toanswer questions and to draw new conclusions
Machine learning: To adapt to new circumstances and todetect and extrapolate patterns.
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Intro to AI
2. Thinking Humanly Try to understand how the mind
works - how do we think? Two ossible routes to find
answers: By introspection - we figure it out
ourselves! By experiment - draw upon
techniques of psychology to conductcontrolled experiments. (Rat in a
Systemsthat thinklike humans
Systems thatthinkrationally
. The discipline of cognitive
science: particularly influential in
Systemsthat act likehumans
Systems thatact rationally
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v s on, na ura anguage
processing, and learning.
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Intro to AI
2. Thinking Humanly
Human vs Machine Thinking Expert systems - AI success story in early 80's.
'computer program
Rule-based representation of knowledge
Medicine (INTERNIST, MYCIN, . . . )
Geology (PROSPECTOR)
Configuration of computers (R1)
Thinking humanly works!
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Intro to AI
2. Thinking Humanly
Human vs Machine Thinking Computer program playing chess
Tried by World champion M. Botvinnik (who also was a
programmer)
Computer way Sophisticated search algorithms
Vast databases Immense computing power
Human world champion beaten!!!
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Intro to AI
3. Thinking Rationally Laws of thought approach to AI
Trying to understand how we actually think is one route to AI -but how about how we should think.
Use logic to capture the laws of rational thought as symbols.
Reasoning involves shifting symbols according to well-defined
rules (like algebra). esu s ea se reason ng.
Systemsthat thinklike humans
Systemsthat thinkrationall
Systemsthat act like
humans
Systemsthat act
rationall
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Intro to AI
3. Thinking Rationally Logicist approach theoretically attractive.
Lots of problems: Transduction: How to ma the environment to s mbolic
representation;
Representation: How to represent real world phenomena
(time, space, . . . ) symbolically; Reasoning: How to do symbolic manipulation tractably - so it
can be done by real computers!
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Intro to AI
4. Acting Rationally Acting rationally = acting to achieve one's goals, given
one's beliefs. Desi n a rational a ent a roach to AI
An agent is just something that acts. Computer agents areexpected to have other attributes that distinguish them from
mere "programs, for example Operating under autonomous control
Perceiving their environment
Persisting over a prolonged time period
Systems that
think likehumans
Systems that
thinkrationall
Systems thatact likehumans
Systems thatact rationally
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Intro to AI
4. Acting Rationally Emphasis shifts from designing theoretically best
decision making procedure to best decision makingprocedure possible in circumstances.
Achieving perfect rationality (making the best decisiontheoretically possible) is not usually possible, due to
Limited resources
Limited time
Limited computational power
Limited or uncertain information about environment
The trick is to do the best with what you've got!
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Intro to AI
1950 ur ng pre c e a n a ou y years an average
interrogator will not have more than a 70 percent chanceof making the right identification after five minutes ofquestioning".
1957 Newell and Simon predicted that "Within ten years a
computer will be the world's chess champion, unless the" .
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Intro to AI
4. Acting Rationally Design a rational agent approach to AI
Rational agent is one that acts so as to achieve the bestoutcome or, when there is uncertainty, the best expectedou come.
Making correct inferences is sometimes part of being a rationalagent, because one way to act rationally is to reason logically tothe conclusion that a given action will achieve one's goals andthen to act on that conclusion.
On the other hand, correct inference is not all of rationality,because there are often situations here there is no provably
, .
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Can Machines Act/Think Intelligently?
Yes, if intelligence is narrowly defined as information.
AI has made impressive achievements showing thattasks initially assumed to require intelligence can beau oma e .
But each success of AI seems to push further the limits
of what we consider intelligence.
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Typical AI Problems
While studying the typical range of tasks that wem g expec an n e gen en y o per orm, we
need to consider both common tasks as well asex ert tasks.
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Typical AI Problems
Common tasks include ecogn z ng peop e, o ec s.
Communicating (through natural language). Navigating around obstacles on the streets.
These tasks are done matter of factly and routinely bypeople and some other animals.
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Typical AI Problems
Expert tasks include: Medical diagnosis
Mathematical problem solving
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Typical AI Problems
Computer systems have been able to performsop s ca e as s e me ca agnos s,
performing symbolic integration, proving theoremsand la in chess.
However, on the other hand, it has proved to be
very hard to make computer systems performmany rou ne as s a a umans an a o oanimals can do.
without running into things, catching prey and avoidingpredators. Humans and animals are also capable of
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.
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Intelligent Behaviour
Some of the tasks and applications that shown e gen e av our are:
Perception involving image recognition and computervision
Reasoning
Learning n ers an ng anguage nvo v ng na ura anguageprocessing, speech processing
Solving problems
Robotics
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Approaches to AI
Strong AI It aims to build machines that can truly reason and
solve problems. These machines should be self-awareand their overall intellectual ability needs to beindistinguishable from that of a human being.
Strong AI maintains that suitably programmed
machines are ca able of co nitive mental states.
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Approaches to AI
Weak AI It deals with the creation of some form of computer-
based artificial intelligence that cannot truly reason andsolve problems, but can act as if it were intelligent.
Weak AI holds that suitably programmed machines cansimulate human cognition.
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Approaches to AI
Applied AI It aims to produce commercially viable "smart" systems
For example, a security system that is able to recognise thefaces of people who are permitted to enter a particularu ng.
Applied AI has already enjoyed considerable success.
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Approaches to AI
Cognitive AI Computers are used to test theories about how the
human mind works. For example, theories about how we recognise faces and
other objects, or about how we solve abstract problems.
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Main Areas of AI
Knowledge representation
earc , espec a y eur s c
search (puzzles, games) Planning
Robotics
Perception
Reasoning under uncertainty,including probabilistic
reasonin
Search
eason ng
Learning
Learning
Agent architectures
Knowledgerep.Planning
Constraintsatisfaction
Robotics and perception Natural language processing
Natural... Expert
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Systems
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What can AI systems do (limited success) ?
In Com uter vision the s stems are ca able of face reco nition In Robotics, we have been able to make autonomous vehicles.
In Natural language processing, we have systems that are capableof simple machine translation.
Expert systems can carry out medical diagnosis in a narrow domain Speech understanding systems are capable of recognizing several
thousand words continuous speech ann ng an sc e u ng sys ems a een emp oye n sc e u ngexperiments with the Hubble Telescope.
The Learning systems are capable of doing text categorization intoabout a 1000 to ics
In Games, AI systems can play at the Grand Master level in chess(world champion), checkers, etc.
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What can AI systems NOT do yet?
n ers an na ura anguage ro us y e.g., rea an
understand articles in a newspaper) Surf the web
Interpret an arbitrary visual scene
Learn a natural language
ons ruc p ans n ynam c rea - me oma ns
Exhibit true autonomy and intelligence
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Foundations of AI
The following disciplines contributed ideas,v ewpo n s, an ec n ques o .
Philosophy (428 B .C.-present) Can formal rules be used to draw valid conclusions?
How does the mental mind arise from a physical brain?
Where does knowledge come from?
How does knowled e lead to action?
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Foundations of AI
Philosophy (428 B.C .-present) - . .
set of laws governing the rational part of the mind. He developed an informal system for proper reasoning that
allowed one to generate conclusions mechanically, given initialpremises.
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Foundations of AI
Philosophy (428 B .C .-present) -
the distinction between mind and matter and of the problemsthat arise.
It held that there is a part of the human mind (or soul or spirit)
that is outside of nature (exempt from physical laws). Animals, on
the other hand, did not possess this dual quality; they could be.
An alternative to dualism is materialism, which holds that thebrain's operation according to the laws of physics constitutes themind.
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Foundations of AI
Philosophy (428 B .C .-present) , ,
philosophical doctrine formulated in Vienna in the 1920s,according to which scientific knowledge is the only kind offactual knowledge and all traditional metaphysical doctrinesare to be rejected as meaningless.
42Source: http://www.britannica.com/EBchecked/topic/346336/logical-positivism
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Foundations of AI
Mathematics (c. 800-present)- , ,
introduced Arabic numerals and algebra.
George Boole (1815-1864) began mathematical development
logic.
Gottlob Frege (1848-1925) extended Boole's logic to include
objects and relations, creating the first-order logic (used todayin basic knowledge representation).
Euclid proposed the first nontrivial algorithm for computinggreatest common denominators.
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F d ti f AI
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Foundations of AI
Mathematics (c. 800-present) , -
problems that he correctly predicted would occupymathematicians for the bulk of the century.
-theorem showed that in any language expressive enough todescribe theproperties of the natural numbers, there are true
statements that are undecidable in the sense that their truthcanno e es a s e y any a gor m.
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Foundations of AI
Mathematics (c. 800-present) -
functions are capable of being computed. The Church-Turingthesis, which states that the Turing machine is capable ofcomputing any computable function. Turing also showed thatthere were some functions that no Turing machine cancompute.
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F d ti f AI
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Foundations of AI
Economics (1776-present)
How should we do this when others may not go along? How should we do this when the payoff may be far in the
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Foundations of AI
Economics (1776-present)-
treatment of "preferred outcomes7' or utility and was improvedby Frank Ramsey (193 1) and later by John von Neumann andOskar Morgenstern.
Decision theory, which combines probability theory with utilitytheory, provides a formal and complete framework for
decisions (economic or otherwise) made under uncertainty
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Foundations of AI
Neuroscience (1861-present)
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Foundations of AI
Neuroscience (1861-present)
Neuroscience is the study of the nervous system, particularlythe brain. The exact wa in which the brain enables thou ht isone of the great mysteries of science.
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Foundations of AI
Neuroscience (1861-present)' -
brain-damaged patients in 1861 persuaded the medicalestablishment of the existence of localized areas of the brainresponsible for specific cognitive functions.
Hans Berger (1929) invented electroencephalograph (EEG)for the measurement of intact brain activity.
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Foundations of AI
Psychology (1879-present)
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Foundations of AI
Psychology (1879-present) -
Wilhelm Wundt (1832-1920) applied the scientific method tothe study of human vision.
Wundt o ened the first laborator of ex erimental s cholo atthe University of Leipzig.
John Watson (1878-1958) initiated Behaviorism movement
that studies objective measures of the percepts (or stimulus)g ven o any an ma an s resu ng ac ons or response . Mental constructs such as knowledge, beliefs, goals, and
reasoning steps were dismissed as unscientific "folk psychology."
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Foundations of AI
Psychology (1879-present) -
brain as an information-processing device.
Kenneth Craik (1943) specified the three key steps of a-
(1) the stimulus must be translated into an internalrepresentation,
(2) the representation is manipulated by cognitive processeso er ve new n erna represen a ons, an
(3) these are in turn retranslated back into action.
Anderson 1980: A cognitive theory should be like a computer.
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Foundations of AI
Computer Engineering (1940 present)
ow can we u an e c en compu er
For artificial intelli ence to succeed we need twothings: intelligence and an artifact.
The computer has been the artifact of choice.
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Foundations of AI
Computer Engineering (1940 present)
Pascaline: Mechanical adder & substractor (Pascal; mid1600s)
,
Analytic Engine: universal computation; never completed(ideas: addressable memory, stored programs, conditional
jumps) Charles Babbage (1792-1871), Ada Lovelace
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Foundations of AI
Computer Engineering (1940 present)
computer built by Alan Turing team in1940, England. Deciphering German messages.
-
Konrad Zuse 1941, Germany
ABC: First electronic computer built by John Atanasoff 1940-
42 US ENIAC: First general-purpose, electronic, digital computer built
by John Mauchy & John Eckert
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Birth of AI
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Birth of AI
Dartmouth 1956 workshop for 2 months Term artificial intelligence
Fathers of the field introduced
Alan Newell & Herbert Simon
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Birth of AI
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Birth of AI
Early Enthusiasm (1952-69) Claims: computers can do X
General Problem Solver, Newell & Simon Intentionall solved uzzles in a similar wa as humans do.
Geometry Theorem Prover, Herbert Gelernter, 1959
Arthur Samuels learning checkers program, 1952
, t me s ar ng, v ce ta er: c art y
Integration, IQ geometry problems
, , ,
Adalines [Widrow & Hoff 1960], perceptronconvergence theorem [Rosenblatt 1962]
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A Dose of Reality (1966-74) Simple syntactic manipulation did not scale
Intractability
Perceptrons book with negative result onrepresentation capability of 1-layer ANNs [Minsky &
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Knowledge-based systems (1969-79) DENDRAL: molecule structure identification
[Feigenbaum et al.] Knowledge intensive
Mycin: medical diagnosis [Feigenbaum, Buchanan,Shortliffe]
450 rules knowled e from ex erts no domain theor Better than junior doctors
Certainty factors
Domain knowledge in NLP Knowledge representation: logic, frames...
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AI becomes an industry (1980-88) R1: first successful commercial expert system,
configured computer systems at DEC; saved40M$/year
1988: DEC had 40 expert systems, DuPont 100...
1981: Japans 5th generation project
,Inference, Intellicorp, Teknowledge
LISP-specific hardware: LISP Machines Inc, TI,ym o cs, erox
Industry: few M$ in 1980 -> 2B$ in 1988
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Recent events (1987-) ea -wor app ca ons ra er an oy oma ns
Building on existing work e.g. speech recognition oc, rag e me o s n s
Hidden Markov models now
e.g. planning (unified framework helped progress)
Belief networks & probabilistic reasoning
Reinforcement learning
Multia ent s stems
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AI Prehistory
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Philosophy Logic, methods of reasoning, mind as physicalsystem foundations of learning, language,rat ona ty
Mathematics Formal representation and proof algorithms,computation, (un)decidability, (in)tractability,robabilit
Economics utility, decision theory Neuroscience physical substrate for mental activity
Psychology phenomena of perception and motor control,exper men a ec n ques Computer building fast computers
engineering
function over time Linguistics knowledge representation, grammar
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Abridged History of AI
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1943 McCulloch & Pitts: Boolean circuit model of brain 1950 Turin 's "Com utin Machiner and Intelli ence" 1956 Dartmouth meeting: "Artificial Intelligence" adopted
195269 Look, Ma, no hands! 1950s Early AI programs, including Samuel's checkers
program, ewe mon s og c eor st,Gelernter's Geometry Engine
1965 Robinson's complete algorithm for logical reasoning
Neural network research almost disappears
196979 Early development of knowledge-based systems 1980-- AI becomes an industry 1986-- Neural networks return to popularity 1987-- AI becomes a science 1995-- The emergence of intelligent agents
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State of the art
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IBM Deep Blue:
champion Garry Kasparov in 1997.
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Proof of Robbins Conjecture:
unsolved for decades.
Dr. Wil liam McCune at
,
his office with computer.The "Proof of Robbins
Conjecture" problem is
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on the screen.
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Autonomous Control:
to keep it following a lane.
It was placed in CMU's NAVLAB computer-controlled minivan-
miles it was in control of steering the vehicle 98% of the time.A human took over the other 2%, mostly at exit ramps.
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Logistics Planning: ,
planning and scheduling program that involved up to 50,000
vehicles, cargo, and people. NASA's on-board autonomous planning program controlled.
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Language Understanding and Problem Solving:
puzzles better than most humans, using constraints on
possible word fillers, a large database of past puzzles, and avariety of information sources including dictionaries and onlinedatabases such as a list of movies and the actors that appear.
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AI and Ethics
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Ethical Concerns: Robot behavior
How can we ensure they do so?
Asimovs Three Laws of Robotics:. , ,
allow a human being to come to harm.
2. A robot must obey orders given it by human beings except
where such orders would conflict with the First Law.3. A robot must protect its own existence as long as suchprotection does not conflict with the First or Second Law.
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Ethical Concerns: Human behavior
constraints?
As a secondary question, would it be possible to do so?
them from having free will??
Will intelligent systems have consciousness? (Strong AI)
If the do, will it drive them insane to be constrained b artificialethics placed on them by humans?
If intelligent systems develop their own ethics and morality, willwe like what they come up with?
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Summary
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Different people think of AI differently. Two important
or behavior? Do you want to model humans or work
from an ideal standard? n s oo , we a op e v ew a n e gence s
concerned mainly with rational action. Ideally, anintelligent agent takes the best possible action in as ua on.
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The history of AI has had cycles of success, misplaced,
funding. There have also been cycles of introducing
new creative approaches and systematically refining.
AI has advanced more rapidly in the past decadebecause of greater use of the scientific method inexper men ng w an compar ng approac es.
The subfields of AI have become more integrated, andAI has found common ground with other disciplines.
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