Early Artificial
Intelligence
Projects
A
Student Perspective
Part
of NSF's Recovering MIT's AI Film History Project
Created at
CSAIL by Heather Knight,
August 2006
Edited by
Thomas Greene, December 2006
(Please
send email with additions/modifications
to this essay to
J.
Costanza (jackc@csail.mit.edu)
http://projects.csail.mit.edu/films
Contents
Main Paper
I.
What is Artificial Intelligence?
II.
Foundations
III.
1950's: Establishing a Field
IV.
1960's: Pursuing Machine Genius
V.
1970's: Leaping Forward
VI.
1980's: Boom and Crash
VII.
Catching up to the Present
Appendix
I.
The NSF Project Background
II.
AI in Popular Culture
III.
Related Organizations
IV.
MIT Research Centers Timeline
V.
Academic Research Centers Timeline
VI.
Major AI Companies
VII.
AI Projects Timeline
VIII.
AI Papers Timeline
IX.
Computation Landmarks Timeline
Bibliography
Image Credits
Early
Artificial Intelligence
Projects
I. What is Artificial Intelligence?
According
to John McCarthy, the man that coined the term, “[Artificial
Intelligence] is the science and engineering of making intelligent
machines,
especially intelligent computer programs” where
“intelligence is the
computational part of the ability to achieve goals in the
world.”
An
intelligent machine can be a machine that mimics the way humans
think, feel, move and make decisions.
It
could also act in conjunction with a human to compliment and improve
their
ability to do those things. There
are
many possible approaches to the challenge and the definition has never
had a
static solution.
Even the
name 'Artificial Intelligence' has been subject to argument, as
some researchers feel it it sounds unscientific.
They argue the word 'artificial' suggests
lesser or fake intelligence, more like science fiction than academic
research. They
prefer to use terms like computational
neuroscience or emphasize the particular subset of the field they like semantic logic or
machine learning. Nevertheless,
the term 'Artificial
Intelligence' has gained popular acceptance and graces the names of
various
international conferences and university course offerings.
This
paper does not attempt to come up with a precise characterization
of the field. Instead,
it examines what
Artificial Intelligence has been so far by leading the reader through
an
admittedly non-comprehensive collection of projects and paradigms,
especially
at MIT and in the United States.
Unlike
many fields, Artificial Intelligence has not had a linear
progression and its research and breakthroughs have not grown toward an
easily
identified Sun. Computing,
in contrast,
has been noted for its exponential growth and improvement characterized
by
Moore's law, “the empirical observation that the complexity
of integrated
circuits, with respect to minimum component cost, doubles every 24
months”
(wikipedia). The
path of AI, however,
more resembles the intertwining world wide web, spiraling out and
looping back
in many directions.
Here you
will find a rough chronology of some of AI's most influential
projects. It is
intended for both
non-scientists and those ready to continue experimentation and research
tomorrow. Included
is a taste of who the
main players have been, concepts they and their projects have explored
and how
the goals of AI have evolved and changed over time.
Many will be surprised that some of what we
now consider obvious tools like search engines, spell check and spam
filters
are all outcroppings of AI research.
II. Foundations
Though
the term 'Artificial Intelligence' did not exist until 1956, the
advances and ideas from the preceding decades evoked many of the future
themes. At a time
when digital computers
had only just been invented, using programming to emulate human
intelligence
was barely even imaginable.
Understanding
the context into which Artificial Intelligence was born
helps illustrate the technological obstacles that researchers had to
overcome
in the search for machine intelligence as well as elucidating many of
the
original paths.
Beyond
Number-Crunchers: Programmable Machines
The idea
of machines that could not just process, but also figure out how
to solve equations was seen as the first step in creating a digital
system that
could emulate brain processes and living behavior.
What would it mean to have a machine that
could figure out how to solve equations?
Let's go through an example using basic algebra.
In order
to create a machine that can solve more complicated equations
than 2+2=4, a machine needs to have a strategy for deciding on the
multiple
steps necessary to come up with a solution.
For example, if you told the machine, X+Y=7 and X=3, you
would like the
machine to deduce
that 3 + Y = 7, then
that Y = 7 – 3, then that 7 – 3 = 4, and finally
that Y = 4. Assuming
someone has already told the machine
what '+', '-', and '=' mean, you would traditionally tell the machine
how to
solve those simple problems by defining a step-by-step procedure called
a
program.
As early
as 1930, Vannevar Bush of MIT published a paper about a
Differential Analyzer, doing just that for another class of
mathematical
problems. Computers
had not been
invented at that point, but his paper nonetheless described a set of
rules that
would automatically solve differential equations if followed precisely.
The next major idea came in Alan Turing's 1937 paper about any automatic programmable system, known as the Turing Machine. This concept establishes the redundant nature of making a variety of types of programmable-devices out of different materials, because any one could be set up such that it mimics the input-output characteristics of any other.
As computers developed, a fundamental event occurred in understanding communication. In 1948 Shannon published his landmark A Mathematical Theory of Communication. He begins this pioneering paper on information theory by observing that "the fundamental problem of communication is that of reproducing at one point either exactly or approximately a message selected at another point." He then proceeds to so thoroughly establish the foundations of information theory that his framework and terminology remain standard. Of course, today computers and communication are in many ways intertwined.
Early
'Computers' were Room-Sized Calculators
Technology
has improved by leaps and bounds since the start of World War
II when computers were first coming into use.
The first electronic computer, ABC, came in 1940, while
the first
programmable American computer, Mark I, followed in 1944.
Constructed
from wires, magnetic cores and vacuum tubes, they were huge
devices that literally filled rooms.
They had about the functionality of a modern-day
scientific calculator,
but no monitor or keyboard. Instead,
if
you wanted the computer to compute the value of a calculation, you
would punch
buttons in sequence or feed in stacks of punch cards, and it would
eventually
print you back the results
A description of computing pioneer Grace Hopper's
experience with a computer was representative of the kinds of problem
computers
were used for at the time:
[Hopper] was commissioned a
lieutenant in
July 1944 and reported to the Bureau of Ordnance Computation Project at
Harvard
University, where she was the third person to join the research team of
professor (and Naval Reserve lieutenant) Howard H. Aiken. She recalled
that he
greeted her with the words, "Where the hell have you been?" and
pointed to his electromechanical Mark I computing machine, saying
"Here,
compute the coefficients of the arc tangent series by next Thursday."
Hopper plunged in and learned to
program
the machine, putting together a 500-page Manual of Operations for the
Automatic
Sequence-Controlled Calculator in which she outlined the fundamental
operating
principles of computing machines. By the end of World War II in 1945,
Hopper
was working on the Mark II version of the machine.
(Maisel)
Grace
Hopper will also be remembered for discovering and naming the
first computer “bug” in 1945 as well as inventing
the idea of a computer
compiler, a device that can translate higher level programming
languages into
machine language that the computer knows how to execute.
The other
revolutionary electronic creation of the decade was the
transistor, created by Bell Labs in 1947, soon replacing vacuum tubes. A tribute to its
importance according to
wikipedia, an open encyclopedia that all can edit (see bibliography),
follows:
The transistor is
considered by many to be one of the greatest inventions in modern
history,
ranking in importance with the printing press, automobile and
telephone. It is
the key active component in practically all modern electronics.
Its importance in
today's society rests on its ability to be mass produced using a highly
automated process (fabrication) that achieves vanishingly low
per-transistor
costs... The transistor's low cost, flexibility and reliability have
made it an
almost universal device for non-mechanical tasks, such as digital
computing.
Analog
Intelligence: Emulating Brain Function
Before
the new digital technology caught on, many were asking themselves
a question that has recently been having a resurgence in Artificial
Intelligence; If we know how the brain works, why not make machines
based off
the same principles? While
nowadays most
people try to create a programmed representation with the same
resulting behavior,
early researchers thought they might create non-digital devices that
had also
the same electronic characteristics on the way to that end. In other words, while new
approaches try to
represent the mind, analog approaches tried to imitate the brain itself.
Modern systems also look to the brain for inspiration though ultimately do the actual programming using a computer, but early researchers believed we could create analog circuits that mimic the electrical behavior of the brain identically and therefore fundamentally replicate actions and intelligence. Their methodology rested on the feedback and control heralded in Norbert Wiener's 1948 book Cybernetics; or, Control and Communication in the Animal and the Machine.
Examples
of these analog brains included Shannon's mechanical 'mice'
that could remember which path to take through a maze to get to the
'cheese' to
the better known Grey Walter Turtles with wandering, home-seeking and
curiosity
drives that depended on its energy levels.
These machines relied on cleverly arranged circuits using
resistors,
capacitors and basic subcomponents , that automatically behave in a
certain way
based on sensor input or charge levels.
III. 1950's: Establishing the Field
The
fifties saw the growth of an AI community, experimentation with the
first digital AI machines, the inaugural Dartmouth Artificial
Intelligence
Conference, and the creation of one of its strongest initial
proponents, DARPA.
The
Turing Test: An AI Legend
How can
one know if a machine is intelligent?
While the larger issue of defining the field
is subject to debate, the most famous attempt to the answer to the
intelligence
question is in the Turing Test.
With AI's
history of straddling a
huge scope of approaches and fields,
everything
from abstract theory and blue-sky research to day-to-day applications,
the
question of how to judge progress and 'intelligence'
becomes very difficult.
Rather than get caught up in a philosophical
debate, Turner suggested we look at a behavioral example of how one
might judge
machine intelligence.
The
actual test involves examining a transcript of an on screen
conversation between a person and a computer, much like instant
messenger. If a
third party could not tell which one was
the human, the machine would then be classified as intelligent. The
test was
intended merely to illustrate a point, but has since ascended to the
level of
legend in the AI community.
Even
today, The Loebner Prize uses the Turing Test to evaluate
artificial conversationalists and awards a bronze metal annually to the
“most
human” computer. Many
former winners are
available to talk to online. The
organization also offers a $100,000 prize of to the program that can
pass the
test that has yet to be won.
Though
its methodology and exclusive focus on human-style communication
is contentious, one can not learn about AI without knowing what the
Turing Test
is. It is a common
feature in any AI
journal, class or conference and still serves to motivate the AI
community
though its literal goal is still far from being achieved.
Thinking
Machine: The Logical Theorist
Early in
1956, two young CMU researchers, Al Newell and Herbert Simon
implemented a working AI machine.
Their
'Logical Theorist' had a built-in system that could deduce geometric
proofs.
In honor
of its 50-year anniversary, the story was reported in this
year's Pittsburg Post-Gazette:
“Over the Christmas
holiday,” Dr. Simon famously blurted to one of his classes at
Carnegie
Institute of technology, “Al Newell and I invented a thinking
machine...” Dr.
Simon concentrated on developing
“heuristics,” or rules of thumb, that humans use to
solve geometry problems and
that could be programmed into a computer, while Dr. Newell and Mr. Shaw
in
California, developed a programming language that could mimic human
memory
processes...
Their
machine used symbolic reasoning to solve systems of equations,
pioneering an AI methodology that involved programming knowledge and
information directly into a computer.
The
Dartmouth Artificial Intelligence Conference and
General Problem Solver
The 1956
Dartmouth Artificial Intelligence Conference originated with a
proposal submitted to the Rockefeller Foundation by McCarthy, Minsky,
Fochester
and Shannon requested funding for a summer retreat dedicated to
exploring the
potentials in the field whose name it coined.
It is
interesting to note how relevant the seven research pillars they
outlined still are:
1)
Automatic Computers
2)
How Can a Computer be Programmed to Use a Language
3)
Neuron Nets
4)
Theory of the Size of a Calculation
5)
Self-Improvement
6)
Abstractions
7)
Randomness and Creativity.
Though
they made little concrete progress that summer, it marked the
start of an new age and McCarthy's use of the controversial name
'Artificial
Intelligence' stuck.
Given that it was the first working implementation of digital AI, it might seem curious that the Logical Theorist project did not seem to significantly impress the other people at the Dartmouth Conference. One explanation is that Newell and Simon had been invited to the conference almost as an afterthought, less well known than many of the other attendees. But by 1957, the same duo created a new machine called the General Problem Solver (GPS) that they heralded as an epoch landmark in intelligent machines, believing that it could solve any problem given a suitable description.
While its
ability to solve complex problems was disappointing, the
reasons for which will be discussed below, the GPS did explore and
formalize
the problem-solving process and helped researchers better understand
the issues
at stake in achieving an effective program.
It was also the first program that aimed at a general
problem-solving
framework. This
inspired much further
research.
Optimism
about the rate of AI Progress: GPS and
NP-hard Problems
In
retrospect, other established researchers admit that following the
Dartmouth conference, they mostly pursued other routes that did not end
up
working as well as the Newell-Simon GPS paradigm.
Later they acknowledged Newell and Simon's
original insights and many joined the symbolic reasoning fold
(McCorduck).
This
reaction fits into a reputation that this field has of unrealistic
predictions of the future.
Unfortunately, many see AI as a big disappointment,
despite the many
ways its advances have now become a fundamental part of modern life. If you look at the rash
claims of its
original proponents, however, such a conclusion may not seem far
fetched.
A
particularly exuberant example of this disconnection was Newell's
claim after the creation of General Problem Solver that
“there are now in the
world machines that think, that learn and create.
Moreover, ...in a visible future – the range
of problems they can handle will be coextensive with the range to which
the
human mind has been applied.” (Norvig)
One
limitation he overlooked was the curse of 'NP-hard' problems. In these cases, it is not
that one can not
write an appropriate program to find a solution, but rather that it
will, in
effect, never return an answer because the computation will take so
long. A fundamental
property of these problems'
formulation is that execution time grows exponentially with the size of
the
input, and it turns out there are many many problems like with these
characteristics. In
other worlds, given
two inputs, the output might take 2^2 = 4 seconds to compute, three
inputs
might take 2^3=16 seconds, eight might take 2^8=256
seconds and so forth.
Modern
researchers seem to have a more cautious approach to speculations
about the future, having learned from history.
Some see AI research as a way to appreciate and understand
the
complexity of the human mind. It
has
certainly been much harder than most realized to achieve even a small
part of
what organic brains can do. When
I asked
them what advice they would give a novice AI researcher, one AAAI
Fellow
recommended, “Choose a easy problem.
Then
make it simpler. It will always turn out to be much harder than you'd
expect.”
ARPA:
Early AI's Fairy God Mother
If the
Turing Test was the spirit-leader of early AI research, ARPA was
the day-job that paid the bills, although one of its original heads, J.
C. R.
Licklider, did also encouraged many new conceptualizations of the
purpose and
potential of technology. Licklider's
paper, Man Machine Symbiosis, outlined a way of
envisioning the
human-technology relationship, in which a machine assists and works
with a
human to accomplish tasks. The
extensive
resources that the organization provided were indispensable to the
start of the
field.
Short for
the Advanced Research Program Association, and a subset of the
Defense Department, ARPA (now known as DARPA) was created in 1958 after
Sputnik
I went into orbit with the explicit purpose of catching up with the
Russian
space capabilities. When
Eisenhower
decided that space should be civilian-controlled and founded NASA,
however,
ARPA found computing to be its new niche.
It began
operations by contributing large research block grants starting
in 1963 and supported a range of AI and computer science efforts over
the
years, with MIT, Stanford and Carnegie Mellon among the first
recipients.
LISP:
The language that made AI possible
John
McCarthy introduced LISP in 1958, heralded as the language that
made AI programming possible.
LISP is
special because it was the first language that allowed information to
be stored
as list of objects rather than just lists of numbers.
An object is essentially a placeholder or
symbol that is defined somewhere else.
This structuring makes it possible to program recursive
functions and
abstract ideas directly into the machine.
As part
of the shift of batch-processing to interactive computers,
McCarthy designed LISP to have an interactive environment, in which one
could
see errors in the code real time.
The
capability of evaluating and seeing on screen feedback one function at
time,
rather than having to run the entire file can
greatly
facilitate finding bugs in one's code.
While many other early languages have died out, LISP remains the most common programming language for Artificial Intelligence in the United States and is used on par with Prolog in Europe and Japan. According to Peter Norvig, founder of Google and author of a popular textbook on the subject, one reason for the continuing popularity of Lisp is the flexibility of its simple list data structure. In his words, “The list is a very versatile data structure, and while lists can be implemented in any language, Lisp makes it easy to use them. Many AI applications involve lists of constantly changing size, making fixed-length data structures like vectors harder to use.” (Norvig 25)
It is
also easily extensible because there are no limitations on how one
defines and manipulates both programs and data, so one can easily
rename or add
functions to better fit the problem at hand.
Its simple elegance has survived the test of time while
capturing all
the necessary functionality; functions, data structures and a way to
put them
together.
Research
at MIT: The Artificial Intelligence Project
The first coordinated AI research at MIT began in 1959 when John McCarthy and Marvin Minsky founded the Artificial Intelligence Project as part of both the Research Laboratory for Electronics (RLE) in Building 26 and the Computation Center. They were junior faculty at the time and had known each other as from graduate school at Princeton, where Minsky had studied artificial neural networks (cybernetics). A theoretician, he immediately begin work on theories of computations relevant to creating intelligent machines in Computation: Finite and Infinite Machines.
AI and
computation have long had mutually inspiring relationship. Much AI research could not
be implemented
until we had different or better machines, and their theories
influenced the
way those strides forward would be achieved.
The early gurus of the field, like the hackers described
below, were
often pioneers in both, creators and consumers of the new technologies. The tools they created
become part of the
expected package for the next generation of computers, and they
explored and
and improved upon the features that any new machine might have.
MIT
Hackers: Starting a Computer Culture
On the other end of the spectrum from governmental initiatives and administration, computers also captured the imagination of the idealistic relays-and-wiring-obsessed sect of the Tech Model Railroad at MIT. They created a breed of 'hackers' that believed in the power, beauty and freedom of computing. The 'Hacker Ethic' that still exists at MIT today found its roots in the fifties and, as taken from Steven Levy's book about the subject, consisted of the following precepts:
1)
Access to computers – and
anything which might teach you something about
the way the world
works – should be
unlimited and total. Always
yield to the
Hands-On Imperative.
2)
All information should be free.
3)
Mistrust Authority –
Promote Decentralization.
4)
Hackers should be judged by their
hacking, not bogus criteria such as
degrees, age, race, or position.
5)
You can create art and beauty on a
computer.
6)
Computers can change your life for
the better.
A scant few years before, computers had only existed as a heavily regulated industry or military luxury that took up whole rooms guarded by designated personnel who were the only ones actually allowed to touch the machine. Programmers were far removed from the machine and would pass their punch card programs on to the appropriate personnel, who would add them to the queue waiting to be processed. The results would get back to the programmers eventually as a binary printout, which was then deciphered to find the result.
Thus, the Hacker's desire to play with the machine itself was revolutionary for the time. With the reverence surrounding the expensive machines, the concept of spending one's day in front of a computer at the modern office would have sounded ludicrous. In contrast and immune to the social mores of the time, the hackers felt challenged and inspired by the worlds of possibility they saw in these new machines that allowed them to create virtual universes.
Hacker
Innovations
In
the late fifties and even after, computers
were put to work day and night because they were so expensive (and
slow). So it was
common practice for these young
computer enthusiasts to keep late hours and take advantage of the
less-utilized
middle of the night machine time.
They
even developed a system whereby someone would watch out for when
another sleepy
user did not show up for their slot.
The
information would be immediately relayed to the rest of the group at
the Model
Railroad club and someone would make sure the computer time did not go
to waste.
One of
the most important hacker innovations was hooking up a screen and
teletype machine to the computer, first used for interactive debugging. In doing so, users had an
interactive real
time relationship and drastically changed the way a user would use and
relate
to the machine. Several of these innovations would grow into the life,
gas, and
solar corona video
clips available on
this website.
As a result of using the machine so much, they knew where they wanted optimize machine performance and what tools to create to elicit new kinds of functionality from the machines. Early hackers created better languages and even hardwired new commands into the computer circuitry. The most famous program was Space Wars, the first real computer game. It involved maneuvering spacecrafts and torpedoes that was created on a machine little memory and virtually no features.
Soon Space Wars spread through the entire computing community, even used by the Digital Equipment Corporation to ensure the customer properly working computers. As told on wikipedia, “Spacewar was a fairly good overall diagnostic of the PDP-1 computer and Type 30 Precision CRT Display, so DEC apparently used it for factory testing and shipped PDP-1 computers to customers with the Spacewar program already loaded into the core memory; this enabled field testing as when the PDP was fully set up, the field representative could simultaneously relax and do a final test of the PDP.”
IV. 1960's: Pursuing Machine Genius
In terms
of projects, the sixties saw the creation of the first comprehensive
mathematics programs, an attempt to decoding sentence meaning in word
problems
and the creation of now integral operating system tools like user faces
and
word processors. In
addition, a
conversing parody of a psychoanalyst gained notoriety, the first
industrial
robot made its appearance and the expert system DENDRAL derived
conclusions in
the area of chemistry. If
this section
seems like something of a laundry list, that is because there are so
many different
subareas which saw their beginnings in these seminal projects.
As years
progressed, each new computer morphed from big hulking machine to the
present day interactive personal
computer. The
growing capabilities
opened up new possibilities for AI.
For
example, imagine having a computer without a screen.
CRTs were attached to computers in several palces. At
MIT and DEC the program " Spacewar" used the CRT
display for the game.
On the
other coast at the Stanford Research Institute (SRI), Doug
Englebart invented the mouse and on-screen cursor in his experiments
with
different kinds of user faces, as well as windows and multiple raster
monitors,
all of which he demoed in 1967.
The
computer systems in those days were far from failsafe.
In 1960, one Defense computer mistakenly
identified the moon as an incoming missile which understandably caused
great
consternation. Another
example came
during the Cuban Missile crisis, when communications were blocked for
several
days. These
shortcomings would help
motivate high-level encouragement and support for the computer industry.
At the
same time. computer science was gaining growing acceptance as a
field. First, IBM
declared separate
departments for software and hardware, meaning pure programmers
officially
would have a declared place to develop programs and environments. In the academic sphere,
universities began granting
the first degrees in Computer Science.
The decade also saw the birth of the BASIC programming
language,
designed to be easy to understand, and UNIX, a way of structuring and
communicating with an operating system that now underlays all Macs and
Linux-based
computers.
With the new DARPA funding in 1963, MIT created a
new
research group Project MAC. Mirroring
the wide range of research it would inspire, Project MAC brought
together
disparate researchers from departments across the institute, including
those
from the AI Project. All
moved over to
Tech Square, originally occupying two floors, complete with machine
shop and
research areas, including Minsky's beanbags and project testing haven,
the
Play-Pen.
The lab,
under Bob Fano's initial leadership, focused on mimicking
higher cognitive levels of human intelligence.
They worked on systems that could play chess, do SAT
analogy problems,
higher level math, and infer logical conclusions from a given set of
preconditions. One fun invention was Ivan Sutherland Virtual Reality
head-mounted display, the first of its kind.
Math
Programs at MIT: SAINT, MACSYMA, STUDENT
(ANALOGY)
Slagle,
Moses, Bobrow, Evans MIT
The
initial use of programs to solve complex mathematics was not a
matter of rote application of straightforward computations, but rather
involved
programs that could actively figure out what that solution or a close
approximation might be.
The first
step at MIT, SAINT, was created by PhD student James Slagle
and could solve basic integrations.
It
also had the dual fame of being
the
first LISP program ever written. CSAIL
has a reading room that preserves the collection of all these early
thesis
projects, and although not the only institution that could claim this,
early
titles read much like a timeline of developments in AI and Computer
Science at
that time.
Expanding
upon the more traditional approach of using computers as
high-powered calculators, the mammoth MACSYMA entered the scene in 1967. The predecessor of Matlab
and still widely
used by mathematicians and scientists, this program used symbolic
reasoning for
integration problems, in other words, a logic based system. It became the go-to
program for mathematical
operations and one of the earliest expert systems.
Its creator was Joel Moses of MIT and he
initially used a collection of mostly unstructured LISP functions to
accomplish
a wide variety of operations.
Another very different approach to doing math on a computer was Danny Bobrow's thesis in 1964 that solved high-school level algebra word problems, using semantic rules to interpreting natural (human) language. The year before, Thomas Evans had created ANALOGY, a program that could solve SAT-level analogy problems. ANALOGY used a way of deciphering relationships between words that was similar to that used in Bobrow's project. Though they may seem at first glance more human that mammoth-calculator MACSYMA, Norvig, Director of Research at Google, Inc., comments that these kinds of programs “derive simplicity because they deal with simplified worlds.”
Building
Tools at MIT: TECO, SKETCHPAD
Greenblatt
and Murphy, Sutherland, MIT
TECO was
a text editor created at MIT by Greenblatt and Murphy in
1962. Predominantly
used for writing
code at the time, the concept would evolve into the word processor
functionality that later helped computers break into the workplace. In one colorful
description, author Steven
Levy declared the young Greenblatt a “single-minded, unkempt,
prolific, and
canonical MIT hacker who went into the night phase so often that he
zorched his
academic career.”
The
next big tool was SKETCHPAD, a drawing program
that invented the graphical user interface.
According to wikipedia:
Ivan Sutherland demonstrated...
that
computer graphics could be utilized for both artistic and technical
purposes in
addition to showing a novel method of human-computer interaction.
Sketchpad was the first program
ever to
utilize a complete graphical user interface. Sketchpad used an x-y
point
plotter display as well as the then recently invented light pen. The
clever way
the program organized its geometric data pioneered the use of
"objects" and "instances" in computing and pointed forward
to object oriented programming.
LOGO,
1967: early AI language.
Papert,
MIT
There
is a large presence of LOGO and LOGO turtle
videos in the TechSquare film clips.
Invented by Seymour Papert of MIT, LOGO is famous for
being an
easier-to-understand programming language.
It pioneered the idea of educational children programming
programs, the
first of which occurred down the street from MIT in Lexington, MA.
Students and researchers could type in the human-friendly commands over teletype, a typewriter-like contraption that was wired into the main computer and could make simple math, word or whatever-else-they-could-imagine programs.
The next major innovation came when they hooked the system up to a 'turtle' robot whose movements were scripted by the LOGO programs. It provided a way for the students and researchers to immediately see their program in action and test out their algorithms by watching its motion.
By strapping a marker or pencil to the turtles and initiating some simple rules for movements, the robots became famous for tracing complex and beautiful patterns on the paper beneath it. Use the same algorithms to create a path in pixels and they created some of the first screensaver-like graphics.
Vision
Project, 1966: They thought they could Solve
Machine Vision in a Summer
By
connecting cameras to the computers, researchers experimented with
ways of using AI to interpret and extract information about vision data. No one really understood
how difficult that
would be and the initial MIT attempt is one of my favorite AI anecdotes.
Rumor has
it that the task of figuring out how to extract objects and
features from video camera data was originally tossed to a part-time
undergraduate student researcher to figure out in a few short months. What is known for certain
is that there was
summer vision project sometime in the sixties, in which researchers
fully
expected to establish many of the main concepts by the start of the
next
semester.
As would
often be the case in AI, they had vastly underestimated the
complexity of human systems, and the field is still working on how too
make
fully functional vision systems today.
UNIMATE,
1961: The First Industrial Robot
Engelberger
and Devol, General Motors
According
to the Computer History Museum, “The first
industrial robot UNIMATE started out in 1961 on the TV picture tube
manufacturing line, then went to work at General Motors. Weighing
4,000-pounds, the
robot arm that obeyed
commands one-by-one to stack and
sequence die-cast metal.”
Robots would become a major area in AI experimentation, with initial applications in factories or human controllers but later expanding into some cooperative and autonomous tasks. The world 'robot' is derived from the Czech word for worker, but nowadays the machines are used from everything from actors in the Entertainment Industry (see the movies Gremlins, Jurassic Park, A.I.) to high precision medical surgeries, toys and autonomous vacuum cleaners.
ELIZA,
1965: A pattern-matching psychologist
Weizenbaum,
MIT
ELIZA is
a simple pattern matching conversational machine, the first of
her kind, created by MIT computer scientist Joseph Weizenbaum in 1966.
It has now become part and parcel of all Emacs programs
and crudely
simulates a psychotherapist using pattern matching.
Weizenbaum
was not a proponent of AI.
In fact, in the paper about his invention, he says,
“Machines are made
to behave in wondrous ways, often sufficient to dazzle even the most
experienced observer. But
once a
particular program is unmasked, once its inner workings are explained
in
language sufficiently plain to induce understanding, its magic crumbles
away;
it stands revealed as a mere collection of procedures, each quite
comprehensible.”
Whether
or not you agree with Weizenbaum's characterization, the best
way to understand the program is to look at an example.
The following comes from the description
Daniel Bobrow gave of an unplanned encounter between the Vice President
of BBN
an abandoned ELIZA program unwittingly left running on the computer
screen:
Here is how Bobrow tells the story:
One Saturday morning about 9 am., a
Vice
President of Bolt, Beranek, and Newman [BBN]... found a note on the
PDP-1
teletype that read: "TYPE ALL MESSAGES INTO THIS TELETYPE AND END THEM
WITH A PERIOD." This note was left there by one of the programming bums
who had just succeeded in making run a LISP version of the DOCTOR
program
(based on ELIZA as developed by Weizenbaum). However, the VP thought
that this
note indicated that this teletype was linked with the one in my home,
since I
often use the computer all night. Therefore, he started typing to "me,"
but in fact was typing to this version of the DOCTOR program. The
following
conversation ensued:
vp: Do you think that I can use the
computer this morning?
eliza: Why do you ask?
vp: I might be able to make some
additional Telcom sales.
eliza: Why aren't you so sure?
vp: My perspective customers have never
seen a
demonstration of the system.
eliza: Does this mean very much to you?
vp: Of course it does.
eliza: You seem very positive about that.
vp: This is ridiculous.
eliza: What do you mean, this is
ridiculous?
vp: Is there any reason why I can't use
the computer now?
eliza: Tell me more about your perspective
customers.
vp: Please dial me up on 491-1850
Note that after the last remark the
VP
did not obey instructions, and left out the period. Therefore, of
course, the
computer didn't answer him. This so infuriated the VP, who thought I
was
playing games with him, that he called me up, woke me from a deep
sleep, and
said:
vp: Why are you being so snotty to me?
bobrow:
What
do you mean I am being snotty to
you?
The VP angrily read me the dialog
that
"we" had been having, and couldn't get any response but laughter from
me. It took a while to convince him it really was a computer.
The basic
algorithm is the following:
(1) Look at user's input, (2) Find a pattern that matches
the input, (3)
Look up the and print out the corresponding response.
Though you can, of course, form your own
opinion, I find it amazing that such a simple setup can result in such
an
amusing and complex situation.
DENDRAL,
1966: Chemistry
Expert System analyzing
organic compounds
Buchanan,
Feigenbaum, Lederberg, Sutherland, Stanford
One of
the clearest examples of applied AI research, DENDRAL analyzed
organic compounds using mass spectrogram and nuclear magnetic resonance
data to
determine their structure.
It limited
the search space using constraint satisfaction, increasing the
probability that
the system would find a solution.
The
heuristics and rules it used to trace the path of which structures
and characteristics respond to what kind of molecules were painstaking
gathered
from interviewing and shadowing experts in the field.
It involved a very different approach to
intelligence from a universal problem solving structure, requiring
extensive specialized
knowledge about a system.
DENDRAL
evolved into the MetaDendral system, which attempted to automate
the knowledge gathering bottleneck of building an expert system. MetaDendral made the first
scientific
discovery by a machine regarding an unknown chemical compound in 1975.
V. 1970's – A Rising
Industry
Directions
of AI advancement accelerated in the seventies with the
introduction of the first personal computers, a medical diagnostic tool
MYCIN,
new conceptualizations of logic, and games like Pong and PacMan.
Expanding
from abstract tools to applications, Project Gutenburg began
compiling electronic versions of books in 1970, an ongoing effort now
available
online. The first
reading machine was
created by Kurzweil in 1976 and was used to assist the blind. Whether robots or
keyboards, the next
evolutionary step in both AI and computer science came with the
control,
interpretation and coordination of peripheral devices.
Computers, inaccessible to individuals outside of
military, academia and large banks, were suddenly available to own
oneself for
a mere few thousand dollars.
At the
start, the machine did not even have a screen, just a set of LEDs and
buttons
one had to punch in sequence to program the machine.
Market forces soon welcomed in a flood of
peripheral devices to improve input and output capabilities. As Microsoft and Apple
Computers began
operations and the first children's computer camp occurred in 1977,
major
social shifts in the status of computer technology were underway.
Back at
MIT, former director Rod Brooks relates that in the seventies,
“Patrick Winston became the director of the Artificial
Intelligence Project,
which had newly splintered off Project MAC.
The lab continued to create new tools and technologies as
Tom Knight,
Richard Greenblatt and others developed bit-mapped displays, fleshed
out how to
actually implement time-sharing and included e-mail capabilities.
“Knowledge
representation, knowledge-based systems, reasoning and
natural language processing continued to motivate innovations in
projects
programming languages as the lab expanded in size, accepting former
students
Gerry Sussman, Carl Hewitt and Ira Goldstein into the faculty
ranks.”
Early
Mobile Robots: Shakey, Freddie
Stanford
and University of Edinburgh
DARPA
funded various initial robot projects across the
country including Stanford's mobile robot Shakey.
In a similar vein, the University of
Edinburgh soon created their own mobile robot, Freddie, in 1973. Both robots used visual
perception and other
inputs to create internal models of the world around them, which they
would
then use to navigate through space.
More
specifically, wikipedia declares:
SRI
International´s Shakey became the first mobile robot
controlled by artificial
intelligence. Equipped with sensing devices and driven by a
problem-solving
program called STRIPS, the robot found its way around the halls of SRI
by
applying information about its environment to a route. Shakey used a TV
camera,
laser range finder, and bump sensors to collect data, which it then
transmitted
to a DEC PDP-10 and PDP-15. The computer radioed back commands to
Shakey — who
then moved at a speed of 2 meters per hour.
Robots and Natural Language Processing
at
MIT: Copy Demo, Silver Arm, SHRDLU
MIT
Early robotics included the 1961 MH1 robot-hand
project and 1970 copy-demo in which a robotic arm equipped and camera
could
visually determine the structure of a stack of cubes and then construct
an
imitation. Both of
the projects are well
documented on the AI Films website.
The first
first computer controlled robot intended for small parts
assembly came in 1974 in the form of David Silver's arm, created to do
small
parts assembly. Its
fine movements and
high precision required great mechanical engineering skill and used
feedback
from touch and pressure sensors. Patrick
Winston soon expanded the idea of cube manipulation with his program
ARCH, that
learned concepts from examples in the world of children's blocks.
Another
major program that was applied to the blocks world was SHRDLU, a
natural language processing system created by Terry Winograd in 1970.
According
to wikipedia:
SHRDLU carried on a simple dialog
(via
teletype) with a user, about a small world of objects (the BLOCKS
world) shown
on an early display screen (DEC-340 attached to a PDP-6
computer).
The name SHRDLU was derived from
ETAOIN
SHRDLU, the arrangement of the alpha keys on a Linotype machine,
arranged in
descending order of usage frequency in English.
A side effect of this memory, and
the
original rules SHRDLU was supplied with, is that the program could
answer
questions about what was possible in the world and what was not. For
instance,
SHRDLU would deduce that blocks could be stacked by looking for
examples, but
would realize that triangles couldn't be stacked, after having tried
it. The
"world" contained basic physics to make blocks fall over, independent
of the language parser.
Finally, SHRDLU could also remember
names given to objects, or arrangements of them. For instance one could
say
"a steeple is a small triangle on top of a tall rectangle"; SHRDLU
could then answer questions about steeples in the blocks world, and
build new
ones.
To clarify, an example SHRDLU dialog might consist of the following:
Person: PICK UP A
BIG RED BLOCK.
Computer: OK.
Person: GRASP THE PYRAMID.
Computer: I DON'T UNDERSTAND WHICH PYRAMID YOU MEAN.
Person (changing their mind): FIND A BLOCK WHICH IS TALLER THAN THE ONE
YOU ARE
HOLDING AND PUT IT INTO THE BOX.
Computer: BY "IT", I ASSUME YOU MEAN THE BLOCK WHICH IS TALLER THAN
THE ONE I AM HOLDING.
Computer: OK.
Person: WHAT DOES THE BOX CONTAIN?
Computer: THE BLUE PYRAMID AND THE BLUE BLOCK.
Person: WHAT IS THE PYRAMID SUPPORTED BY?
Computer: THE BOX.
Person: HOW MANY BLOCKS ARE NOT IN THE BOX?
Computer: FOUR OF THEM.
Person: IS AT LEAST ONE OF THEM NARROWER THAN THE ONE WHICH I TOLD YOU
TO PICK
UP?
Computer: YES, THE RED CUBE. (wikipedia)
The
SHRDLU research was exciting because it allowed the user, albeit in
highly constrained circumstances, to communicate directly with the
computer in
English, rather than having to learn a machine programming language.
MYCIN:
Medical Diagnostic Expert System
Edward
Shortliffe, Stanford
MYCIN was
the first fully developed expert system, diagnosing and
recommending treatment for infectious blood diseases.
The name 'Mycin' was used because it is a
common antibiotic suffix. Created
in the
beginning of the decade, by 1979 MYCIN was declared as good as medical
experts
by the Journal of American Medical Association.
The system even adjusted recommended dosages to the
patient's body
weight. Its
functionality can be
described as follows:
Mycin operated
using a fairly simple inference engine, and a knowledge base of ~500
rules. It
would query the physician running the program via a long series of
simple
yes/no or textual questions. At the end, it provided a list of possible
culprit
bacteria ranked from high to low based on the probability of each
diagnosis,
its confidence in each diagnosis' probability, the reasoning behind
each
diagnosis (that is, Mycin would also list the questions and rules which
led it
to rank a diagnosis a particular way), and its recommended course of
drug
treatment.
Mycin was never actually used in
practice. This wasn't because of any weakness in its performance
— in tests it
outperformed members of the Stanford medical school. It was as much
because of
ethical and legal issues related to the use of computers in medicine
— if it
gives the wrong diagnosis, who can be held responsible? Issues with
whether
human experts would find it acceptable to use arose as well. (wikipedia)
The
creators of MYCIN found that doctors were unwilling to accept its
advice if the system could not convince them of why it made its
conclusions. Therefore,
they included
the ability to answer questions about how it was making its decisions. As described in one AI
textbook, “[MYCIN]
uses rules that tell it such things as, If the organism has
the following
set of characteristics as determined by the lab results, then it is
likely that
it is organism X. By
reasoning backward
using such rules, the program can answer questions like “Why
should I perform
that test you just asked for?” with such answers as
“Because it would help to
determine whether organism X is present.” (Rich 59) It is important that
programs provide justification
of their reasoning process in order to be accepted for the performance
of
important tasks.
VI. 1980's: Boom and Crash
The start
of the eighties was the golden age for Artificial Intelligence
in the US, as the field caught the imagination of the larger population. Institutions across the
board were suddenly
springing up departments of Artificial Intelligence from video game
companies
to Campbell's Soup. The
most common
utilities came in the form of MYCIN-style expert systems, wizards that
could
give advice or information about how to do something in its area of
expertise.
These
expert systems were specialized, serving the knowledge base of
gurus in a field. For
example, in the
case of Campbell's soup, a factory manager might be curious about the
tub-cleaning requirements between making different batches of soup. As related in the
interview with on AAAI
Fellow, if you were going from Chicken Broth to Chicken Noodle, you
could
proceed right way, but if the ordering was Clam Chowder to Vegetarian
Minestrone, the tanks better be spic and span in between.
Family
and work computers started to become commonplace in the 1980's
with six million computers sold in 1983.
Most of the tool builders at MIT left the lab in the
eighties to work in
new companies and bring their work to the consumer.
IBM introduced its 'PC' and Xerox, LMI and
Symbolics had a variety of Lisp machines.
In addition, Apple's LISA and then Macintosh hit the
market and ARPANET
opened up to civilians, a precursor to the Internet. Despite these
advances, by
the end of the decade, the 'AI Winter' left the field, especially
companies,
struggling to defend their funding and reputation with a downturn in
public
interest.
In 1985,
Professor Nicholas Negroponte and former MIT President Jerome
Wiesner started the MIT Media Laboratory.
According to the Media Lab website:
[The Media Lab
grew] out of the work of MIT's Architecture Machine Group, and building
on the
seminal work of faculty members in a range of other disciplines from
cognition
and learning to electronic music and holography...
In its first decade, much of the
Laboratory's activity centered around abstracting electronic content
from its
traditional physical representations, helping to create now-familiar
areas such
as digital video and multimedia. The success of this agenda is now
leading to a
growing focus on how electronic information overlaps with the everyday
physical
world. The Laboratory pioneered collaboration between academia and
industry,
and provides a unique environment to explore basic research and
applications,
without regard to traditional divisions among disciplines.
The MIT
AI lab was also in full swing, directing its talents at
replicating the visual and mobility capabilities of
a young child, including face recognition,
object manipulation and the ability to walk and navigate through a room. Tomas Lozano-Perez
pioneered path search
methods used for planning the movement of a robotic vehicle or arm. There was work done on
legged robots by Marc
Raibert and John Hollerback and Ken Salisbury created dexterous robot
hands. This decade
was also when
famed roboticist
and current director of
CSAIL Rodney Brooks built his first robots.
Wabot-2, 1980: Robot that reads Sheet
Music and plays Organ
Waseda University, Japan
The name WABOT
is from 'WAseda roBOT', honoring the
University in Japan at which it was designed.
In this case, the story is best told by its originators. The description of the
project on the Waseda
University website follows:
It has been forecast that robots
will penetrate society in 21st
century... In that case, robots will be required to have
anthropomorphic
appearance sand faculties... Developing the anthropomorphic intelligent
robot
WABOT (WAseda roBOT) [aimed] to finally develop a "personal robot"
which resembled a person as much as possible.
In 1980, our laboratories...
commenced the WABOT-2 project. Playing a
keyboard instrument was set up as an intelligent task that the WABOT-2
aimed to
accomplish, since an artistic activity such as playing a keyboard
instrument
would require human-like intelligence and dexterity.
...The robot musician WABOT-2 can
converse with a person, read a normal
musical score with is eye and play tunes of average difficulty on an
electronic
organ. The WABOT-2 is also able of accompanying a person while he
listens to
the person singing. The WABOT-2 was the first milestone in developing a
"personal robot."
It
is interesting to note that the
research group sees WABOT-2 as the first generation of an oncoming
class of
personal robots. It
may seem far-fetched
at the moment, but look how far personal computers have come since they
were
first conceived of fifty years ago.
HEARSAY, 1982: Speech Understanding
Program
Erman, Hayes-Roth, Lesser, Reddy at CMU
HEARSAY
was a speech understanding
program developed at CMU in 1982 that pioneered a useful model for
solving
perceptual problems, that is, problems in which a machine is trying to
derive
meaning out of complex input signals.
That process might involve decoding words from someone's
voice,
recognizing someone's face from a set of vision data or tactilely
distinguishing different kinds of textures.
Because
it is a widely applicable
problem, below you will find a textbook summary of the steps one must
consider
in figuring out how a machine can glean information from sensory data. As HEARSAY was a CMU
project, it seems
appropriate to include a summary from the an Artificial Intelligence
textbook
by Elaine Rich of CMU:
TECHNIQUES USED IN SOLVING
PERCEPTUAL PROBLEMS...
It is important to divide the
overall understanding process into
manageable pieces. We
can do this by
dividing the process of analyzing either a speech sample or a picture
into the
following five stages:
Digitization: Divide the continuous input into
discrete chunks. For
speech recognition,
this can be done by measuring the amplitude of the signal at fixed
intervals,
such as 20,000 times per second...
Smoothing: Eliminate sporadic large
variations in
the input. Because
the real world is
mostly continuous, these spikes in the input are usually the result of
random
noise.
Segmentation: Group the small chunks produced by
digitization into larger chunks corresponding to logical components of
the
signal. For speech
understanding, these
segments correspond to logical components of the signal... such as s
or a.
These segments are often called phones...
Labeling: Attach to each of the segments a
label
that indicates which, of a set of building blocks, that segment
represents... So
the labeling procedure
can do one of two things. It
can assign
multiple labels to a segment and leave it up to the later analysis
procedure or
choose the one that makes sense in the context of the entire input. Or it can apply its own
analysis procedure in
which many segments are examined to constrain the choice of label for
each
segment.
Analysis: Put all the labeled segments
together
to form a coherent object... when surrounding pieces are considered,
the number
of interpretations that lead to a consistent overall interpretation
[also known
as constraint satisfaction] is considerably reduced.. In speech, this
results
from such things as intonation patterns that cover whole sentences.
(Rich 349)
The
actual HEARSAY program parsed audio
information using a 'blackboard model' that follows the above
techniques in a
way that traces up and down the complexity levels of sound, syllable,
word, as
well as right to left, in sentences where there are ambiguous signals. Like constructing a jig
saw puzzle, the
fastest method is invariably putting together the easily parsed border
and then
filling in the less obvious pieces.
This
method becomes particularly useful when words are not enunciated
clearly.
AARON, 1985: An
Autonomous
Artist
Harold Cohen, UCSD
Harold
Cohen is an English artist who
almost accidentally encountered programming at Stanford and then became
father
to the first robot artist, AARON.
Who
knows what the rising technological art community will come
up
with next. According
to Cohen's homepage:
The AARON program, an ongoing
research effort in autonomous machine (art
making) intelligence... began when [Cohen] was a visiting scholar at
Stanford
University's Artificial Intelligence Lab in 1973.
Together, Cohen and AARON have exhibited at
London's Tate Gallery, the Brooklyn Museum, the San Francisco Museum of
Modern
Art, Amsterdam's Stedelijk Museum and many more of the world's major
art
spaces...
One of the few artists ever to have
become deeply involved in artificial
intelligence, Cohen has given invited papers on his work at major
international
conferences on AI, computer graphics and art technologies...
AARON has produced many thousands
of drawings, to a few dozen of which
Cohen has added color... The painting machine with which AARON colored
real
drawings in the real world was premiered at an exhibit at the Computer
Museum
in Boston in the spring of 1999.”
A
picture being created by the latest version of AARON side by side with
its
creator appears above.
Allen, 1985: Starting a New Generation of Reactive
Robots
Rodney Brooks, MIT AI Lab
One
of the original MIT AI Lab groups
was named the Mobot Lab and dedicated to making mobile robots. 'Allen' was the group's
first creation and
shares Brook's middle name.
According
to author Kevin Kelly:
"Allen" was the first robot Brooks
built. It kept its brains
on a nearby desktop, because that's what all robot makers did at the
time...
The multiple cables leading to the brain box [a.k.a. computer] from
Allen's
bodily senses of video, sonar, and tactile were a never ending source
of
frustration for Brooks and crew... Brooks vowed that on their next
project they
would incorporate the brains inside the robot -- where no significant
wiring
would be needed -- no matter how tiny the brains might have to be.
They were thus forced to use very
primitive logic steps and very short
and primitive connections in "Tom" and "Jerry," the next
two robots they built. But to their amazement they found that the
'dumb' way
their onboard neural circuit was organized worked far better than a
[complex]
brain in getting simple things done.
Since
then, Rodney Brooks has become one
of the most famous proponents of robotics and is the current head of
CSAIL,
MIT's Computer Science and Artificial Intelligence Laboratory.
VII. Catching up to the Present
Since the eighties, several projects stand out as major new shifts and developments in the field. When Deep Blue beat world chess champion Garry Kaspacov in 1996, some say it marked the end of an era in which specialized programs and machines reigned. One new potential direction, the first official RoboCup, kicked off that the very same year posing and requires integrating all kinds of intelligences. Their goal is to be able to beat the winning World Cup soccer team by 2050.
With the results of the DARPA Grand Challenge this year, that potentially rash aspiration seems more plausible. After the first year's race when none of the autonomous vehicles made it even ten miles past the start of the 131.2 mile course, this year saw five of the twenty-three DARPA Grand Challenge competitors reach the finish with time to spare.
Other developments include the
efforts started in 2002 to
recreate a once wonder-of-the-world-status library in Egypt as online
e-book
called Bibliotheca Alexandrina. The transition
to computerized medical records has been sluggish, but in other areas
of
medicine from imagery to high precision surgery, the new facilitates
machines
can give a surgeon has saved lives and made new diagnosis and
operations
possible.
While we
have all heard about NASA space robots, but less known were the
$400,000 'His' and 'Her' robots featured in the 2003 Niemen Marcus
Christmas
catalog. Clearly,
our relationships with
machines in society is in transition.
One of the most important examples of that was Cynthia
Breazeal's
research on machine emotion and social interaction with her MIT
thesis-project
Kismet in 2002.
New
versions of ELIZA-like programs are becoming commonplace with AOL
Instant Messenger's SmarterChild, an agent that can answer questions
and try to
search the web to answer your questions about Movie times or tell you
not to
have a 'potty mouth.'
While we
do not have full realization of Licklider's man-machine
symbiosis, the idea of machines and tools becoming agents that work
hand and
hand with human beings seems more and more natural with each generation. IRobot's vacuum cleaner
Roomba is
kickstarting a new household robotics industry
with record sales.
John
McCarthy believes that fundamental new ideas are required before AI
can reach human-level intelligence, rather than just needing large
databases
and faster computers. He
declares on his
website, “My own opinion is that the computers of 30 years
ago were fast enough
if only we knew how to program them.”
Whether
or not human-level intelligence is even the main goal of the
field anymore, it is one of the many that entice our interest and
imagination. It is
clear that AI will
continue to impact and contribute to a range of applications and only
time will
tell which paths it will travel along the way.
Heather
Knight received her B.S. in Electrical
Engineering with a minor in Mechanical Engineering from MIT in 2006 and
has
been accepted into their EECS Masters of Engineering program. She has also worked at the
MIT Media Lab
since 2002 with Professor Cynthia Breazeal of the Robotic Life as well
as
Professor Deb Roy of Cognitive
Machines.
Appendix
I. Project Background
The Recovering
MIT's AI Film History project was born in
2001, when a collection of old film reels showed up on some dusty
shelves
during the move from Tech Square to Frank Ghery's architectural
creation, the
Ray and Maria Stata Center. The
Stata
Center is the home of the now joined AI Lab and Computer Science
departments
known as CSAIL, the Computer Science and Artificial Intelligence
Laboratory.
Thanks to
the support of the National Science Foundation, these films
and more are now available on the project website, http://projects.mit.edu/films. The original NSF proposal
to digitize and
create a website was worded as followed:
This project will collect, organize and
preserve historic materials, particularly film, that are part of the
historical
record of the field of Artificial Intelligence (AI). It will create an
organized digital archive and use highlights selected from the archive
to
illustrate the intellectual history of AI...
Sources for this project included notes, memos and
technical reports
from MIT and elsewhere, and in particular, a uncatalogued, unconserved
and
uncurated collection of films that recently came to light at MIT... The
project
will create a web site or DVD to showcase the selected clips, the
connecting
narrative, and other more technical materials.
The opening of the website
fortuitously coincided with both the
50th
anniversary of Artificial Intelligence (as the term was coined at the
Dartmouth
conference in 1956) and the American Association of Artificial
Intelligence
(AAAI) conference in Boston, MA June 16-22, 2006.
There we had the opportunity to interview on
video more than one quarter of the AAAI Fellows in attendance. The footages is now part
of the site. The
Fellows include the most influential
innovators in the field of Artificial Intelligence and many of the
original
founders of the field were present.
Another
primary source for the site was Rick Greenblatt, who began his
MIT career in the 1960s. He
was
extraordinarily generous with his time, watching each and every of the
site's
film clips and leaving an audio 'podcast' of his reminiscences for each
one.
The
Recovering MIT's AI Film History website itself was created over the
summer of 2006, led by CSAIL's Outreach Officer Tom Greene and produced
by Luis
Gomez (University of Florida undergrad), Heather Knight (MIT MEng
student) and
Matt Peddie (MIT undergrad), who collectively did the research, web
design and
interviews contained within the site.
I would
like to personally thank MIT Electrical Engineering and Computer
Science Professors Fernando Corbato and Bob Fano, as well Harvard
History of
Science PhD candidate Hallam Stevens for reading drafts of this paper. I have not done full
justice to the feedback
they offered, but the content is more complete and less error-ridden
because of
their help.
II. Artificial Intelligence in
Popular Culture
Asimov,
Isaac. I, Robot (1950), Caves of Steel
(1954), Robots
of Dawn(1982). Robot Science Fiction, book.
Conceives fictional Three Laws of Robotics
Orwell,
George. 1984
(1949). Big Brother uses computers to enslave humanity, book.
Shelley,
Mary Frankenstein. book.
Kubrick,
Stanley. “2001: A Space Odyssey” (1968), movie.
(Based on book
by Arthur C. Clark)
“Star
Wars” (1977), movie.
III. AI Organization Timeline*
*many appendix timeline events adapted from Mark Kantrowitz's compilation
(see http://www2.english.uiuc.edu/cybercinema/aihistory.htm)
1951 IEEE
founded.
1956 The
Dartmouth AI Conference, McCarthy coins name.
1958
DARPA created.
1958
Teddington (UK) Conference. McCarthy, Minsky, Selfridge
1969
First IJCAI Conference in Washington DC.
1974
First SIGGRAPH conference.
1980
First AAAI conference.
Stanford.
1982 ICOT
formed. Japan.
IV. MIT Research Centers Timeline
1959
Artificial Intelligence Project starts, led by Minsky and McCarthy
1963
Project MAC begun by Robert Fano
1969 AI
Lab splits off from Project MAC, led by Marvin Minsky
1975 LCS
(Laboratory of Computer Science) replaces Project MAC
1980 The Media Lab founded by Nicholas Negroponte
2003 CSAIL (Computer Science and Artificial Intelligence Laboratory) grows out of an LCS and AI Lab merger with the new director Rod Brooksand the CSAIL co-director Victor Zue.
V. Academic Research Centers
Timeline
1959*
MIT's Artificial Intelligence Project, founded by John McCarthy
and Marvin Minsky.
1963
Stanford AI Lab (SAIL), founded by John McCarthy
1963*
MIT's Project MAC, begun by Robert Fano using a $2
million DARPA grant.
CMU AI
Lab, also founded.
1966
Edinburg AI Lab, founded by Donald Michie.
1979 CMU
Robotics Institute, founded by Raj Reddy.
1980* MIT Media Laboratory founded by Nicholas Negroponte
VI. Major Early AI Companies:
(rashly
incomplete- please report missing ones)
DEC,
Artificial Intelligence Corp., Apple, Microsoft, Symbolics, Xerox,
Intel, LMI, Teknowledge, Thinking Machines, Google
VII. AI Projects Timeline
1947 Grey
Walter builds electro-mechanical “turtle”
1949
Turing and colleagues try to create a chess program on Mach 1.
1950
Chess Program proposed as search problem.
Shannon.
1956 The
Logic Theorist, solves math problems. Newell, Shaw and Simon.
1957
General Problem Solver, “means-end analysis.” Newell, Shaw and Simon.
1959
Checkers Program beats best human players. Samuel.
1961*
SAINT, first Lisp program. PhD work. J. Slagle.
1962*
TECO, text editor for PDP-1.
Murphy and Greenblatt. MIT.
1962
First Commercial Industrial Robots
1963*
ANALOGY, solves SAT-level analogy problems.
PhD work. Thomas Evans.
1963*
SKETCHPAD, drawing tool.
Sutherland.
1963
Parser, tested on “Time flies like an arrow.”
Susumo.
1964*
STUDENT, solves high-school level algebra word problems. PhD.
Danny Bobrow.
1964* SIR. PhD work. Bert
Raphael.
1965*
ELIZA, conversational psychotherapist. Joseph Weizenbaum.
1965*
First Virtual Reality head-mounted display.
Ivan Sutherland.
1966
DENDRAL, chemistry knowledge-based sys.
Buchanan, Feigenbaum, Lederberg, Sutherland.
Stanford.
1967*
LOGO, early AI language.
Papert.
1967*
MACSYMA, symbolic reasoning for integration problems, logic based
system. Joel Moses.
1968*
Tentacle Arm, aka Minsky-Bennett arm.
1970
PROLOG. Alain Colmerauer.
1970
Shakey, first computer controlled mobile robot.
Stanford.
1970
INTERNIST, aid in disease diagnosis. Pople and Myers.
1970*
SHRDLU, natural language processing, blocks world. Terry Winograd.
1970*
ARCH. Winston.
1970
Project Gutenburg, free electronic versions of books. M. Hart.
1971
PARRY, paranoid conversation agent. Colby.
1971
STRIPS, first motion planning system?.
Nils Nilsson and Rich Fikes.
1972
Smalltalk. Xerox Parc.
1972
PONG, early video game. Nolan Bushell.
1973
Scripts developed. Schank and Abelson.
1973
MYCIN, medical diagnostic expert system. PhD
Edward Shortliffe. Stanford.
1974*
Silver Arm, first computer controlled robot, intended for small
parts assembly. David Silver.
1975
MetaDendral, first scientific discovery by a machine.
1976
Adventure, first adventure game.
Crowther and Woods.
1976*
First LISP machine. Greenblatt.
1976
First reading machine. Kurzweil.
1976
Automated Mathematician.
Lenat.
1976*
Primal Sketch for Visual Representation. David Marr et al.
1979 Stanford Cart crosses
chair
filled room without help. Hans
Moravec.
1978
Version Spaces. Tom
Mitchell. Stanford.
1978
MYCIN generalized. PhD.
Bill
VanMelle. Stanford.
1979
PacMan brought to market.
1980
HEARSAY, uses blackboard model. Erman, Hayes-Roth, Lesser,
Reddy. CMU.
1980
Expert systems up to 1000 rules.
1980
Japanese 5th Generation Project.
Kazuhiro Fuchi.
1981
Connection Machine Designed, powerful parallel architecture. Danny
Hillis. Thinking Machines.
1983
SOAR. John Laird & Paul Rosenbloom with Allen Newell. PhDs.
CMU.
1984
Neural Nets with backpropagation widely used. John Hopsfield.
1984
“Wabot-2” reads sheet music and plays organ.
1985
Aaron, autonomous drawing program.
Harold Cohen.
1985*
Allen, autonomous reactive robot.
Rod Brooks.
1990
Human Genome Project begins
1997 Deep
Blue beats world chess champion Garry Kaspacov.
1997
First Official RoboCup, start of a new paradigm
2000*
Kismet, robot that recognizes and displays emotion.
PhD. Cynthia Breazeal.
2000 AIBO
introduced.
2002
Bibliotheca Alexandrina
2003
Niemen Marcus's Christmas catalog features $400,000 his and her
robots.
VIII. AI Papers Timeline
1930*
"Differential Analyzer," Vannevar Bush, MIT
1937
“On Computable Numbers,” Turing Machine. A. Turing.
1943
Neural Networks. McCulloch
and Pitts.
1945*
“As We May Think.” Vannevar Bush, MIT.
1948
“Cybernetics, or Control and
Communication in the Animal and Machine .” Norbert Wiener.
1948 "A Mathematical Theory of Communication" , Claude Shannon.
1950
“Computing Machinery and Intelligence,” -Turing
Test. Turing.
1957*
“Syntactic Structures.” Chomsky.
1958*
Perceptron, Rosenblatt.
1962
“Structure of Scientific Revolutions.” Kuhn.
1962
“Possible Worlds Semantics.” Kripke.
1963
Semantic Networks as a Knowledge Representation.
M. Ross Quillian.
1963*
“Steps Toward Artificial Intelligence.” Marvin
Minsky.
1968*
“Semantic Information Processing.” Marvin Minsky.
1968
*“The Sound Pattern of English.” Chomsky and Halle.
1969*
“Perceptrons,” discusses limits of single layer
neural networks.
Minsky and Papert.
1969*
“Philosophical Problems from the Perspective of Artificial
Intelligence,” situation calculus
McCarthy and Pat Hayes.
1972
“What Computers Can't Do.” Dreyfus.
1974*
“A Framework for Representing Knowledge.” Marvin
Minsky.
1974
“Creative Computing.” Ahl.
1974
“Computer Lib.” Nelson
1976
Metalevel reasoning, PhD. R. Davis. Stanford.
1979
Mycin as good as medical experts.
Journal of American Medical Association.
1979* AI
Lab Flavors OOP memo.
Weinreb and Moon.
1979*
Non-monotonic logics. McDermott and Doyle (MIT), McCarthy
(Stanford).
1980
“The Knowledge Level.” Allen Newell.
1980
“Gödel, Esher,
Bach,” wins Pulitzer. Hofstadter.
1983
“The Fifth Generation.” Feigenbaum and McCorduck.
1984
“Common LISP the language.” Steele.
1985*
“The Society of Mind.” Marvin Minsky.
IX. Landmarks in Computation
1940 The
ABC, first electronic computer. Atanasoff and Berry.
1941 Z3,
first programmable computer. Zuse. Germany.
1944 Mark
I, first programmable computer in US.
Aiken.
1945
First computer “bug.” Grace Hopper.
1947
Transistor. Schockley, Brittain and Ardeen.
Bell Labs.
1950
UNIVAC, first commercial computer.
Eckert and Mauchley.
1952
Compiler. Grace Hopper.
1956
FORTRAN, programming language.
IBM.
1958
Integrated Circuit. Jack St. Clair Kilby.
1959
PDP-1 sells for $159,000.
DEC.
1960
Defense computer mistakes moon for incoming missile.
1960
LINC, first computer with integrated CRT.
Lincoln Labs.
1961 All
Dartmouth students required to be computer literate. Kemeny's
timesharing system.
1964
PDP-8, first mass-produced microcomputer. DEC.
1964 IBM
360 series.
1964 BASIC, programming language. Kemeny and Kurtz.
1965 * Multics papers published at Fall Joint
Computer Conference (see http://www.multicians.org/papers.html ) First
time-sharing.
1967 IBM
distinguishes hardware and software.
1968 Mouse, windows and multiple raster monitors demoed. Englebart. SRI.,
Fall Joint Computer Conference held at the Convention Center in San Francisco
1969
UNIX, Thomson and Ritchie. AT&T.
1970
Floppy Disks.
1971
Intel 8008, first microprocessor in US.
1975
BASIC for a microcomputer, Gates and Allen.
1975
Altair 8800, first personal computer with 256 bytes memory.
1975
BYTE, magazine.
1977
Apple Computer. Wozniak
and
Jobs.
1977
Apple II, Radio Shack TRS80, Commodore PET.
1977
First children's computer camp.
1977
Microsoft founded.
1980 Lisp
machines widely marketed. Xerox, LMI, Symbolics.
1981 IBM
Introduces Personal Computer (PC)
1983 Six
million computers sold.
1984
Apple LISA
1984
Compact Disk (CD) technology. Sony.
1984
Apple introduces Macintosh.
1987
ARPANET opens to civilians
*at MIT
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*A
note on wikipedia:
The use
of wikipedia as a source is sometimes viewed with skepticism, as
its articles are created voluntarily rather than by paid encyclopedia
writers. I contend
that not only is the
concept of wikipedia an
outcropping of
the field this paper is about, but it probably has more complete and up
to date
information than many other sources about this particular topic. The kind of people that do
or are interested
in AI research are also the kind of people that are most likely to
write
articles in a hackeresque virtual encyclopedia to begin with. Thus, though multiple
sources were consulted
for each project featured in this paper, the extensive use of wikipedia
is in
keeping with championing clever technological tools that distribute and
share
human knowledge.
Photo Credits
1. Grace
Hopper (San Diego Supercomputer Center)
<http://www.sdsc.edu/ScienceWomen/hopper.html>
2. John
McCarthy (Computer History Museum)
<http://www.computerhistory.org/about/press_relations/chess/>
3. PDP-1
(Computer History Museum)
<http://www.computerhistory.org/timeline/timeline.php?timeline_category=rai>
4. Chess
(Project Website)
<http://projects.csail.mit.edu/films/pictures/Screenshots/13-cubechess2.jpg>
5. LOGO
Turtle (Project Website)
<http://projects.csail.mit.edu/films/pictures/Screenshots/22-turtle.jpg>
6. Shakey
(Computer History Museum)
<http://www.computerhistory.org/timeline/timeline.php?timeline_category=rai>
7. Cube
Stacking (Project Website)
<http://projects.csail.mit.edu/films/pictures/Screenshots/13-cubechess.jpg>
8. WABOT
(Waseda University)
<http://www.humanoid.waseda.ac.jp/booklet/kato02.html>
9. AARON
(SciNetPhotos) <http://www.scinetphotos.com/aaron.html>
10.
RoboCup (RoboCup Website)
<http://www.robocup2006.org/sixcms/detail.php?id=117&lang=en>
11.
Kismet (MIT Media Lab)
<http://web.media.mit.edu/~cynthiab/NewFiles/research.html>
12. Lucy on Minsky-Bennett Arm (Project Website) <http://projects.csail.mit.edu/films/pictures/Screenshots/61-arm.jpg>