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Stottler Henke - Artificial Intelligence History
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History
~3000BC
A papyrus, that was bought in a
Luxor antique shop by Edwin Smith in 1882, was prepared representing
48 surgical observations of head wounds. The observations were stated
in symptom-diagnosis-treatment-prognosis combinations as: IF a patient
has this symptom, THEN he has this injury with this prognosis if this
treatment is applied. This was the first known expert system.
13th C
Ramón Lull invented the Zairja,
the first device that systematically tried to generate ideas by mechanical
means.
1651
Leviathan, written by Thomas Hobbes
(15881679), was published. In it he proposes that humans collectively,
by virtue of their organization and use of their machines, would create
a new intelligence. George B. Dyson refers to Hobbes as the patriarch
of artificial intelligence in his book, "Darwin Among the Machines:
The Evolution of Global Intelligence," p7, 1997.
17th C
Leibnitz and Pascal invented mechanical
computing devices. Pascal was 19 years old in 1642 when he invented
an eight-digit calculator, the Pascaline. In 1694, Gottfried Liebnitz
invented the Liebnitz Computer, which multiplied by repetitive addition,
an algorithm still in use today. Leibnitz also conceived of a 'reasoning
calculator' for interpreting and evaluating concepts, but realized
the problem was immense because of the great interconnectedness of
concepts.
1726
Jonathan Swift anticipated an automatic
book writer in Gulliver's Travels.
1805
Joseph-Marie Jacquard invented the
first truly programmable device to drive looms with instructions provided
by punched cards.
1832
Charles Babbage designed the 'analytical
engine,' a mechanical programmable computer. He had earlier designed
a more limited Difference Engine in 1822, which he never finished
building.
1847
George Boole developed a mathematical
symbolic logic (later called Boolean algebra) for reasoning about
categories (i.e., sets) of objects, which is also applicable to manipulating
and simplifying logical propositions.
1879
Gottlob Frege went beyond Boole
in his treatment of logic with his invention of predicate logic, making
it possible to prove general theorems from rules. However, the meaning
of the words being manipulated by this logic is still only what the
user intended, and therefore not conveyed by his representation of
the logic.
~1890
Hand-driven mechanical calculators
became available.
1890
Herman Hollerith patented a tabulating
machine to process census data fed in on punched cards. His company,
the Tabulating Machine Company, eventually merged into what was to
become IBM.
Late 1800s
Leonardo Torres y Quevedo invented
a relay-activated automaton that played end games in chess.
1898
Behaviorism was expounded by psychologist
Edward Lee Thorndike in "Animal Intelligence." The basic idea is that
all actions, thoughts, or desires are reflexes triggered by a higher
form of stimulus, with humans just reacting to a higher form of stimulus.
Mind, according to behaviorism, becomes a trivial concept, a passive
associative mechanism.
1921
Karel Capek, a Czech writer, invented
the term robot to describe intelligent machines that revolted against
their human masters and destroyed them.
1928
John von Neumann introduced the
minimax theorem, which is still used as a basis for game-playing programs.
1931
Vannevar Bush's mechanical differential
analyzer (a mechanical analog computer) was able to solve differential
equations.
1937
Alan Turing conceived of a universal
Turing machine that could mimic the operation of any other computing
machine. However, as did Godel, he also recognized that there exists
certain kinds of calculations that no machine could perform. Even
recognizing this limit on computers, Turing still did not doubt that
computers could be made to think.
1937
Alan Turing and Alonzo Church independently
arrived at the same thesis, the Church-Turing thesis, that all problems
that a human can solve can be reduced to a set of algorithms.
~1938
Claude Shannon showed that calculations
could be performed much faster using electromagnetic relays than they
could be performed with mechanical calculators. He applied Boolean
algebra. Electromechanical relays were used in the world's first operational
computer, Robinson, in 1940. Robinson was used by the English to decode
messages from Enigma, the Germans' enciphering machine.
1941
A leading German aeronautical research center deployed the Zuse Z3, a general-purpose
electromechanical computer. It performed several instructions per second,
and the program was entered by using a movie reel with punched holes representing instructions.
1943
Vacuum tubes replaced electromechanical
relays in calculators. These were used in 1943 in Colossus, a faster
successor to Robinson, to decipher increasingly complex German codes.
1943
Walter Pitts and Warren McCullock
showed how artificial neural networks could compute, relying on the
use of feedback loops.
1945
John von Neumann designed the basic
computer architecture still used today, in which the memory stores
instructions as well as data, and instructions are executed serially.
He described this in a 1945 paper.
1945
ENIAC (Electronic Numerical Integrator
and Calculator), which was to run 1,000 times faster than the relay-operated
computers, was ready to run in late 1945. It was the first general
purpose, fully electronic, programmable computer. John W. Mauchley and John Presper
Eckert were its inventors.
1945—1956
Symbolic artificial intelligence
emerged as a specific intellectual field. Key developments included
Norbert Wiener's development of the field of cybernetics, in which
he invented a mathematical theory of feedback in biological and engineered
systems. This work helped clarify the concept that intelligence is
the process of receiving and processing information to achieve goals.
1947
The transistor was invented by William
Shockley, Walter Brattain, and John Bardeen.
1948
Nobert Wiener published Cybernetics,
a landmark book on information theory. "Cybernetics" means "the science
of control and communication in the animal and the machine."
1949
Donald O. Hebbs suggested a way
in which artificial neural networks might learn.
1950
Turing proposed his test, the Turing
test, to recognize machine intelligence.
1951
EDVAC, the first von Neumann computer,
was built.
1951
Marvin Minsky and Dean Edmonds build
the first artificial neural network that simulated a rat finding its
way through a maze.
7 June 1954
Turing suicided in mysterious circumstances
by eating a cyanide-laced apple following a conviction for homosexuality
in 1953.
1950s
It became clear that computers could
manipulate symbols representing concepts as well as numerical data.
1955 - 1956
Logic Theorist, the first AI program,
was written by Allen Newell, Herbert Simon, and J.C. Shaw. It proved
theorems using a combination of searching, goal-oriented behavior,
and application of rules. It used a list-processing technique in a
new computer language, IPL (Information Processing Language) that
they developed to write Logical Theorist. IPL provided pointers between
related pieces of information to mimic associative memory; and catered
to creating, changing, and destroying interacting symbolic structures
on the fly.
1955
John McCarthy names the new discipline,
"Artificial Intelligence" in a proposal for the Dartmouth conference.
~1956
IBM released the 701 general purpose
electronic computer, the first such machine on the market. It was
designed by Nathaniel Rochester.
1956
A two-month summer conference on
thinking machines was held at Dartmouth University. The attendees
included John McCarthy, Marvin Minsky, Claude Shannon, Nathaniel Rochester,
Ray Solomonoff, Oliver Selfridge, Trenchard More, Arthur Samuel, Herbert
Simon, and Allen Newell. It did not result in a consensus view of
AI.
"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." according to a statement of the Dartmouth conference
participants, that expresses the physical symbol hypothesis.
1956
George Miller published "The Magic
Number Seven" on the limits of short-term memory.
1956 - 1963
Two main themes emerge in AI:
improved search methods in trial-and-error
problems
making computers learn by themselves
(e.g., to play checkers better than the program's author)
1957
Newell and Simon ran the General
Problem Solver incorporating "means-ends analysis." Means-ends analysis
seeks to reduce the difference between the predicted outcome and desired
outcome by changing controlling factors. GPS and later AI programs
were really quite limited in their problem solving ability as the
programmer had to feed information to it in a highly stylized way.
They also had to work hard to define each new problem, and the program
made only a small contribution to the solution. Also, these programs
contributed nothing to providing motivation for solving a problem,
still an open issue today.
Edward Feigenbaum's EPAM (Elementary Perceiver and Memorizer), provided
a model of how people memorize nonsense syllables.
Herbert Gelernter wrote the Geometry Theorem Prover which used information
to prune a search with a billion alternatives (for a 3-step proof
of a geometry theorem) to only 25 alternatives. He was the first to
demonstrate "model referencing."
Arthur Samuel wrote a checkers-playing program that soon learned how
to beat him.
1957
Noam Chomsky, a linguist at MIT,
postulated that language could be analyzed without reference to its
content or meaning. In other words, syntax was independent of semantics.
This concept was enticing to AI people as it would mean knowledge
could be represented and analyzed without knowing anything about what
was being said. Experience has shown that this concept doesn't apply
well to human languages.
1958
John McCarthy and Marvin Minsky
founded the Artificial Intelligence Laboratory at the Massachusetts
Institute of Technology.
1958
John McCarthy developed the LISP
program at MIT for AI work. It soon supplanted the earlier AI language,
IPL, and retained its popularity against a later language, COMIT,
developed in 1962.
Early 1960s
AI researchers concentrated on means
of representing related knowledge in computers, a necessary precursor
to developing the ability of computers to learn.
1961
Mortimer Taube, an engineer, authored
the first anti-AI book, "Computers and Common Sense: The Myth of Thinking
Machines." It did not receive much attention.
1962
The world's first industrial robots
were marketed by a U.S. company.
1963
Tom Evans, under Marvin Minsky's
supervision, created the program, ANALOGY. It was designed to solve
problems that involved associating geometric patterns that occurred
in a past case with the pattern in a current case. ANALOGY could solve
shape problems of the kind, figure C is to which of several alternative
figures as figure A is to figure B.
1963
The Stanford University founded
the Artificial Intelligence Laboratory under John McCarthy.
1965
Brothers, Herbert L. Dreyfus, a
philosopher, and Stuart E. Dreyfus, a mathematician, wrote a strongly
anti-AI paper, "Alchemy and AI," which was published reluctantly by
the RAND Corporation for whom Herbert was consulting.
1965
Herbert Simon predicted that machines
will be capable of doing any work a man can do by 1985.
1965
The Robotics Institute was started
at Carnegie Mellon University under Raj Reddy.
1965 to ~1975
Edward Feigenbaum and Robert K.
Lindsay at Stanford built DENDRAL, the first expert system. Its expertise
was in mapping the structure of complex organic chemicals from data
gathered by mass spectrometers. After DENDRAL's rules grew to a certain
size, its tangled set of statements became difficult to maintain and
expand.
Middle and late 1960s
Marvin Minsky and Seymour Papert
directed the Blocks Microworld Project at MIT AI Laboratory. This
project improved computer vision, robotics, and even natural language
processing enough for computers to view and manipulate a simple world
of blocks of different colors, shapes, and sizes. Similar experiments
proceeded at Stanford under John McCarthy and at Edinburgh University.
1966
National Research Council ended
all support for automatic translation research, a field closely related
to AI.
1968
The tradition of mad computers is
continued with the release of the film, 2001: A Space Odyssey, directed
by Stanley Kubrick, from Arthur C. Clarkes' book. The computer's name,
HAL, is a reminder of the giant computer company, IBM. (Form a word
from the letters that come after H, A, and L in the alphabet.)
1968 & 1969
Terry Winograd, a doctoral student
under Seymour Papert, wrote SHRDLU (a word used in MAD magazine for
mythical monsters and other oddities). SHRDLU created a simulated
block world and robotic arm on a computer about which a user could
ask questions and give commands in ordinary English. It has gradually
been realized that the techniques employed in SHRDLU would not work
beyond artificially defined toy worlds or restricted areas of expertise
because, to do so, the computer would have to know vast amounts of
knowledge that humans regard as common knowledge or common sense.
1969 - 1974
Roger Schank developed his "conceptual
dependency" theory which enabled computers to make more plausible
inferences about the meaning of the "semantic primitives" in sentences,
when words took on secondary meanings. For example, the meanings of
sentences such as "He gave her a present," and "Bill gave Joe a glancing
blow" could be distinguished. However, the theory was inadequate for
dealing with the complexities of meaning possible in linked sentences
narrating a sequence of events, when some of the events can only be
inferred from what is said. Typically, humans can readily infer the
unstated events in a specific sequence.
1969
A mobile robot called Shakey was
assembled at Stanford, that could navigate a block world in eight
rooms and follow instructions in a simplified form of English.
1969
Marvin Minsky and Seymour Papert
published their book, Perceptrons—An Introduction to Computational
Geometry. Until its publication work on artificial networks in the
U.S. was flourishing, which this book brought to a near halt until
the 1980s by disparaging some of this work.
1969
John McCarthy and Patrick J. Hayes
reappraised how AI might usefully proceed. They discounted possible
help from philosophy as "philosophers have not really come to agreement
in 2500 years." They identified two basic problems to overcome . One
is the "frame problem," that of managing all that is going on around
the central actors, a task that creates a heavy computational burden.
Next is the "qualification problem," meaning the need to deal with
all the qualifiers that can arise to stop an expected rule from being
followed exactly. For example, if the ignition key of a car is turned,
usually the engine starts. However, there are many exceptions, such
as when the car has no fuel, or the battery is discharged.
1970
William Wood at Bolte, Beranek &
Newman in Boston conceived a parsing scheme called the Augmented Transition
Network. By mixing syntax rules with semantic analysis, the scheme
could discriminate between the meanings of sentences such as "The
beach is sweltering" and "The boy is sweltering."
1970s
Earlier machine learning efforts
aimed at enabling computers to automatically optimize appropriate
weights for variables they had been told were important to solving
a problem. Now efforts were directed to automatically deriving those
variables themselves—in other words, automatic concept formation.
Douglas Lenat programmed Automated Mathematician (AM), a program to
rediscover number theory by itself. It combined a set of rudimentary
ideas, a sense of experimentation, and a sense of rightness of good
discoveries to guide its activities, the latter two capabilities expressed
in a number of rules (or heuristics). Despite some initial dramatic
success, it quickly reached limits for discovering new number theory.
Lenat realized that it was because the heuristics it had been given
were limited, and he decided it needed to be able to create new and
useful discovery heuristics for itself. Over five years he developed
this new ability in a successor program, EURISKO. EURISKO kept track
of the performance of the heuristics it used, and dropped the ones
that performed poorly, and modified and improved the better performing
ones. The program was successfully used to improve the design of 3D
computer chips. It even taught itself how to play a space-war game,
Traveller TCS, and became the national champion in 1982 and 1983 with
a radical approach to the game.
Early 1970s
DARPA's Speech Understanding Research
(SUR) program, for which Carnegie Mellon was the prime contractor
was brought to an abrupt end. Although goals were met, the product,
which has a limited grammar, was not considered practical.
AI researchers turned from research into the control and expression
of knowledge (such as was demonstrated in the Micro Worlds project)
to the manipulation of large amounts of knowledge. This was done in
recognition of the limitations of successful programs such as SHDLU
and GPS to be extended to tackle the more complex problems of the
real world in useful ways. The earlier work reflected overly simplistic
approximations of the ways the human mind works, and better approximations
were required. Manipulation of larger amounts of information was also
enabled by the increasing power of computers.
Early 1970s
First practical demonstration of
the use of fuzzy logic for process control. Abe Mamdani and his student,
Seto Assilian, at Queen Mary College (now Queen Mary and Westfield)
in London used fuzzy logic to control the operation of a small steam
engine.
1971-1972
Alain Colmerauer and Phillipe Roussel
wrote the computer language, PROLOG (for PROgrammation en LOGique).
It was revised in 1974 to force logical statements (i.e., IF ... THEN)
to be written only in the Horn clause format. This permitted it to
solve problems that required showing something was NOT true to be
concluded in a finite number of steps. PROLOG became the favored AI
language outside the U.S. where LISP still held sway.
1972 on
Edward Shortliffe, a Stanford doctoral
student under Bruce Buchanan, and others wrote MYCIN, an expert system
to diagnose infectious blood diseases and recommend antibiotics, with
dosage adjusted for patient's body weight. They also created the first
expert system shell, that contained the inference engine, which contained
the logic of how rules were to be applied. MYCIN could also deal with
probabilistic rules, which DENDRAL couldn't. MYCIN could outperform
human clinicians in some trials. A difficulty that arose during the
writing of these and subsequent expert systems has been the extraction
of the knowledge from human experts into the rules, the so-called
knowledge engineering.
1972
Herbert Dreyfus expanded his "Alchemy
and AI" paper into an aggressively anti-AI book, "What Computers Can't
Do."
1973
Sir James Lighthill, Cambridge University's
Lucasian Chair of Applied Mathematics, advised the British government
to cease most AI research in Britain.
1974
Funding for AI research at MIT,
Carnegie Mellon, and Stanford from DARPA was cut drastically as a
result of recent disappointing results.
By mid-1970s
Diverging specialties in AI field
emerged. These included Edward Feigenbaum's work on expert systems;
Roger Schank on language analysis; Marvin Minsky on knowledge representation;
Douglas Lenat on automatic learning and nature of heuristics; David
Marr on machine vision; and others developing PROLOG.
1974
Paul J. Werbos invented the back-propagation
algorithm, that enabled multilayer neural networks, that had the ability
to perform classification operations beyond simple Perceptrons. Back-propagation
was independently rediscovered in the early 1980s by David Rumelhat
and David Parker.
1975
Marvin Minsky published a paper,
"A Framework for Representing Knowledge," which he started with "It
seems to me that the ingredients of most theories in artificial intelligence
and in psychology have been on the whole too minute, local, and unstructured
to account ... for the effectiveness of commonsense thought." He proposed
that people thought in terms of generic "frames" within which we look
for expected features (he called them "terminals") with anticipated
properties ("markers"). Frames may be grouped or linked together into
systems. So, we would have idealized "house" frames with features
including walls, windows, doors and a roof, which we would use to
recognize real houses by frame matching. These and other frames, say,
of shops, churches, and schools, would build a town system.
~1977
Roger Schank and others augmented
the conceptual dependency theory with the use of scripts (short stories
of typical sequences of events that don't leave out any events) and
the use of knowledge of people's plans and goals to make sense of
stories told by people and to answer questions about those stories
that would require inferences to be made to answer them. This combination
resulted in successful language analysis programs such as Janet Kolodner's
CYRUS, that thought of itself as Cyrus Vance, learned about his life
from newspaper accounts, and could even surmise that Vance's wife
and the Israel prime minister Begin's wife met at a social occasion
to which it would be likely spouses would be invited. This in fact
happened.
Late 1970s
First commercial expert system was
developed. It was XCON. (for eXpert CONfigurer), developed by John
McDermott at Carnegie Mellon. He developed it for Digital Equipment
Company, which started using it in January 1980 to help configure
computer systems, deciding between all the options available for their
VAX system. It grew from containing about 300 rules in 1979 to more
than 3,000 and could configure more than 10 different computer systems.
End of 1970s
Practical, commercial applications
of AI were still rare.
July 1979
World champion backgammon player,
Luigi Villa of Italy, became the first human champion of a board game
to be defeated by a computer program, which was written by Hans Berliner
of Carnegie Mellon. The program evaluated its moves by evaluating
a weighted set of criteria that measured the goodness of a move. It
did not use the alternative process of searching amongst all possible
future moves and countermoves, a method used in chess, as there are
too many alternatives in backgammon.
1980s
Fuzzy logic was introduced in a
fuzzy predictive system used to operate the automated subway trains
in Sendai, Japan. This system, designed by Hitachi, reduced energy
consumption by 10% and lowered the margin of error in stopping the
trains at specified positions to less than 10 centimeters.
1980
First meeting of the American Association
for Artificial Intelligence held in Stanford, California.
1980
Commercial AI products were only
returning a few million dollars in revenue.
1980
First industrial application of
a fuzzy controller by Danish cement manufacturer, F.L. Smidth &
Co. A/S to regulate the operation of a cement kiln, which is a complex
process subject to random disturbances that made it difficult for
an operator to control.
1982
David Marr's book, "Vision," was
published posthumously, Marr died of leukemia in 1980. It provided
a new view of how the human brain used shading, steropsis, texture,
edges, color, and the frame concept to recognize things. While he
put vision firmly on the map as a major AI problem, many of his ideas
turned out to be wrong.
1982
John Hopfield showed how networks
of simple neurons could be given the ability to calculate.
Early to mid 1980s
A succession of early expert systems
were built and put in use by companies. These included:
a hydrostatic and rotary bacteria-killing
cooker diagnosis program at Campbell's Soup based on Aldo Cimino's
knowledge;
a lathe and grinder diagnosis analyzer
at GM's Saginaw plant using Charlie Amble's skills at listening
for problems based on sounds;
a mineral prospecting expert system
called PROSPECTOR that found a molybdenum deposit;
a Bell system that analyzed problems
in telephone networks, and recommended solutions;
FOLIO, an investment portfolio advisor;
and
WILLARD, a forecaster of large thunderstorms.
AI groups were formed in many large companies to develop expert systems.
Venture capitalists started investing in AI startups, and noted academics
joined some of these companies. 1986 sales of AI-based hardware and
software were $425 million. Much of the new business were developing
specialized hardware (e.g., LISP computers) and software (e.g., expert
system shells sold by Teknowledge, Intellicorp, and Inference) to
help build better and less expensive expert systems. Low quality,
but effective computer vision systems were also commercially launched
successfully.
1984
GE built an expert system based
on electric locomotive diagnosis knowledge of one expert, David Smith,
who was close to retirement. Called the Diesel Electric Locomotive
Troubleshooting Aid, it could diagnose 80% of breakdowns, and provide
repair instructions.
Mid 1980s
Resurgence of neural network technology,
with the publication of key papers by the Parallel Distributed Processing
Study Group. Demonstrations of neural networks in diverse applications
such as artificial speech generation, learning to play backgammon,
and driving a vehicle illustrated the versatility of the technology.
A build-up of commercial interest in neural nets followed, with over
300 small companies, mostly startup founded by researchers, competing
by 1989. The classic boom-bust cycle of expert systems was being repeated.
1985
MIT's Media Laboratory, dedicated
to researching media-related applications using computer science (including
artificial intelligence) and sociology, was founded under Jerome Weisner
and Nicholas Negroponte.
1985
Speech systems now able to provide
any of the following: a large vocabulary, continuous speech recognition,
or speaker independence.
1987
Etienne Wenger published his book,
"Artificial Intelligence and Tutoring Systems: Computational and Cognitive
Approaches to the Communication of Knowledge," a milestone in the
development of intelligent tutoring systems.
~1987
Rule-based expert systems start
to show limits to their commercially viable size. XCON, the Digital
Equipment Company expert system had reached about 10,000 rules, and
was increasingly expensive to maintain. Reasons for these limits include:
Inflexibility of these expert systems
in applying rules, and the tunnel vision implied in their limited
knowledge, that can result in poor conclusions. Expert systems
couldn't reverse their logical conclusions if later given contradictory
facts. For example, an expert system would conclude that Bill
Smith has ten toes because Bill Smith is a person and all people
have ten toes. However, it couldn't then deal with the fact that
Bill Smith lost three toes in an industrial accident. A human,
using "non-monotonic" reasoning, has no problem concluding Bill
Smith has only seven toes.
Rule-based expert systems couldn't
draw conclusions from similar past cases. Such analogical reasoning
is a common method used by humans. Extracting knowledge from experts
who reason analogically and converting that knowledge into rules
is problematic.
As new rules are added to expert systems,
it becomes increasingly difficult to decide the order in which
active rules ought to be acted upon. Unexpected effects may occur
as new rules are added. This behavior is called opacity.
Expert systems don't know what they
don't know, and might therefore provide wrong answers to questions
with answers outside their knowledge. This behavior is called
"brittleness."
Expert systems can't share their knowledge
between them because they really don't have any sense of the words
they manipulate, and the same words in different expert systems
may not be used in the same ways.
Expert systems can't learn, that is,
they can't establish correspondence and analogies between objects
and classes of objects.
It was gradually realized that expert systems are limited to "any
problem that can be and frequently is solved by your in-house expert
in a 10 to 30 minute phone call," as expressed by Morris W. Firebaugh
at the University of Wisconsin.
End of 1980s
Expert systems were increasingly
used in industry, and other AI techniques were being implemented jointly
with conventional software, often unnoticed but with beneficial effect.
1990s
Emphasis on ontology began. Ontology
is the study of the kinds of things that exist. In AI, the programs
and sentences deal with various kinds of objects, and AI researchers
study what these kinds are and what their properties are.
1990s and 2000s
AI applications of many, seemingly
unrelated kinds are quietly being commercialized in greater and greater
numbers. Not all these applications work as well as desired, but they
are continually improving. These include:
Automatic scheduling software to automatically
create better project schedules faster
Advanced learning software that works
like human tutor in teaching one-on-one with each student
Continuous speech recognition programs
that accurately turn speech into text
Software to manage information for
individuals, finding just the documents immediately needed from
amongst million of documents, and automatically summarizing documents
Face-recognition systems
Washing machines that automatically
adjust to different conditions to wash clothes better
Automatic mortgage underwriting systems
Automatic investment decision makers
Software that improves the prediction
of daily revenues and staffing requirements for a business
Credit fraud detection systems
Help desk support systems that help
find the right answer to any customer's question faster
Shopping bots on the web
Data mining tools
E-mail filters
Automated advice systems that personalize
their responses
And many, many more.
Many commercializers of such products and services aren't identifying
their use of artificial intelligence in their products and services.
Probably they're not doing so because "artificial intelligence" isn't
perceived to sell, while improved intelligent solutions to a customer's
problem does.
Early 1990s
The National Center for Supercomputing Applications (NCSA)
at the University of Illinois at Urbana-Champaign developed and released
released the first widely used web browser, named
Mosaic.
1997
Deep Blue, a highly parallel 32-node
IBM RS/6000 SP supercomputer, beat Gary Kasparov, world champion of
chess. Deep Blue did this by calculating hundreds of million of alternative
plays for a number of moves ahead.
1997
Over 40 teams fielded teams of robotic soccer players in the first
RoboCup competition.
1999
Sony Corporation
introduced the AIBO, a robotic pet dog that understandins 100 voice commands,
sees the world using computer vision, and learns and matures.
AIBO is an acronym for Artificial Intelligence roBOt, and aibo also means "love"
or "attachment" in Japanese. On January 26, 2006, Sony announced that it
would discontinue the AIBO.
May 17, 1999
An artificial intelligence system,
Remote Agent,
was given primary control of a spacecraft for the first
time. For two days Remote Agent ran on the on-board computer of Deep
Space 1, while 60 million miles from earth. The goal of such control
systems is to provide less costly, more capable control, that is more
independent from ground control. Currently the difficult job of spacecraft
control is done by a team of spacecraft engineers. Sharing control
with onboard AI systems will enable these people to control more spacecraft,
and for more ambitious missions than possible before to be undertaken.
2002
iRobot, founded by researchers at the MIT Artificial Intelligence Lab,
introduced Roomba, a vacuum cleaning robot. By 2006, two million had been sold.
March 13, 2004
The
Defense Advanced Research Projects Agency (DARPA),
the central research organization of the United States Department of Defense, sponsored the first
DARPA Grand Challenge, a prize
competition for autonomous (driverless) vehicles. The first Challenge took place on a desert course between
Barstow, California to Primm, Nevada. No vehicles completed the course.
October 8, 2005
Stanley, an autonomous Volkswagen Touareg R5 entered by the Stanford Racing Team, won the
DARPA Grand Challenge 2005
and a $2M prize by completing the 212.4 km course in just under 7 hours.
23 vehicles competed, and five completed the course.
References
Daniel Crevier, AI: The Tumultuous
History of the Search for Artificial Intelligence, Basic Books,
1993. This book provides a readable history of artificial intelligence
for the lay person, but is unfortunately out of print.
Ray Kurzweil, The Age of Spiritual
Machines: When Computers Exceed Human Intelligence, Viking,
1999. This book has a detailed time line, which as well as going
back to the origin of the universe, boldly forecasts the future
of intelligent machines through 2099.
Arturo Sangalli, The Importance
of Being Fuzzy and Other Insights from the Border between Math
and Computers, Princeton University Press, 1998. This book
provides a good introduction to fuzzy logic, neural networks,
and genetic algorithms for the non-expert.
Copyright © 2002
Stottler Henke Associates, Inc. All rights reserved.
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Brief | history | of | artificial | intelligence, | especially | expert | systems |
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http://www.stottlerhenke.com/ai_general/history.htm
Stottler Henke - Artificial Intelligence History 2009 January
dvd rental
dvd
Brief history of artificial intelligence, especially expert systems
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