From the Ascendancy of ARPA to the Advent of Commercial Expert Systems Forward Part I.

341 Pages·2013·2.23 MB·English

by  Rebecca Elizabeth Skinner

UC Berkeley
UC Berkeley Previously Published Works
Title
Building the Second Mind, 1961-1980: From the Ascendancy of ARPA to the Advent of
Commercial Expert Systems
Permalink
https://escholarship.org/uc/item/82h464gg
Author
Skinner, Rebecca Elizabeth
Publication Date
2012-09-03
eScholarship.org Powered by the California Digital Library
University of California
Building the Second Mind, 1961-1980: From the
Ascendancy of ARPA to the Advent of Commercial Expert
Systems
Rebecca E. Skinner
Copyright 2013 by Rebecca E. Skinner
ISBN 978-0-9894543-2-2
Forward
Par t I . Introductio n
Preface
Chapter 1. Introduction: The Status Quo of AI in 1961
Par t II . Twi n Bolt s o f Lightnin g Alte r th e Landscap e
Chapter 2. The Revolutionary Impact of the Integrated
Circuit
Chapter 3. The Revitalization of the Advanced Research
Projects Agency and the Foundation of the IPTO
Chapter 4. Hardware, Systems and Applications in the
1960s
Par t III . Th e Bell e Epoqu e o f th e 1960 s
Chapter 5. MIT: Work in AI in the Early and Mid-1960s
Chapter 6. CMU: From the General Problem Solver to the
Physical Symbol System and Production Systems
Chapter 7. Stanford University and SRI
Par t IV . Th e Challenge s o f 197 0
Chapter 8. The Mansfield Amendment, “The Heilmeier Era”,
and the Crisis in Research Funding
Chapter 9. The AI Culture Wars: the War Inside AI and
Academia
Chapter 10. The AI Culture Wars: Popular Culture
Par t V . Bi g Idea s an d Hardwar e Improvement s i n th e 1970 s
Chapter 11. AI at MIT in the 1970s: The Semantic Fallout
of NLR and Vision
Chapter 12. Hardware, Software, and Applications in the
1970s
Chapter 13. Big Ideas in the 1970s
Chapter 14. Conclusion: the Status Quo in 1980
Chapter 15. Acknowledgements
Chapter 16. List of Abbreviations
Chapter 17. Bibliography
Chapter 18. Endnotes
Forward to the Beta Edition
This book continues the story initiated in Building the Second Mind:
1956 and the Origins of Artificia l Intelligence Computing . Building
the Second Mind , 1961-1980: From the Establishment of ARPA-IPTO
to the Advent of Commercia l Expert Systems follows the history,
through to the fortunate phase of the second decade of AI
computing. How quickly we forget the time during which the
concept of AI itself was considered a strange and impossible effort !
The successes and rapid progress of AI software programs, with
related work in adjacent fields such as robotics and complementary
fields such as hardware, thus are our theme and meat of our
narrative.
The Building the Second Mind venture traces back to the author's
Ph.D. dissertation, which studied the commercialization of AI during
the 1980s. Curiously, portions from the author’s original Ph.D.
dissertation itself, concerning the abortive business enterprises,
working in fits and starts, devoted to early commercialization of
expert systems, now make up several chapters of the third volume.
Most of AI is found in ongoing laboratory research, published in
dense academic journal articles and in conference proceedings.
Synoptic and simplifying works, such as this book, are apt to appear
superficial to the researchers who write such papers, yet overly
complex to the man on the street. The author tries to avoid both of
these ills. Popularizers are not well-liked in academia or the world of
research. However, with the exception of those who write the
journal articles, everyone is someone's popularizer. They are a
necessary part of the intellectual ecosystem, since sci-fi novels and
movies alone can't do the work of representing research. This work
is neither highly technical nor comprehensive: my interests are
skewed in favor of knowledge representation. Building the Second
Mind draws heavily upon both academic and journalistic nonfiction
sources regarding AI. Much work in AI is necessarily specialist, but
the value of synoptic works should not be underestimated.
New technological advances allow alterations of the traditional
publishing conventions, leaving social and professional convention
to adjust. Paper publishing schedules and the pace of review have
historically slowed publication and precluded revisions. This author
instead asks for editorial input from the Cloud for this Beta edition.
This will result in far more reviews from a larger pool of
knowledgeable people. This will make it far easier for this text to
include hyperlinks, refs to oral histories and museum and scholarly
websites, as well as corrections and editorial improvements. I will
take comments for several months and then release a revised ePub
edition later in 2012.
Preface
Building the Second Mind , 1961-1980: From the Establishment of
ARPA-IPTO to the Advent of Commercia l Expert Systems tells the
story of the development of AI, the field of research that sought to
get computers to do things that would be considered intelligent if a
person did them, during the 1960s and 1970s. (The definition is
commonly attributed to Marvin Minsky). The book takes its leave at
the conclusion of the ‘70s, shortly before both expert systems and
personal computing were commercially introduced in their awkward
early forms. In the late 1950s, the field had been founded and
began to undertake extremely rudimentary logic and problem-
solving programs. In the 1960s, the immense growth in funding
given to the field of computing, the development of integrated
circuits for mainframe computing, and the increasing numbers of
people in AI and in computer science in general, all tremendously
advanced the field. This is evidenced in more complex problem-
solving, development of an understanding of the field of knowledge
representation; appearance of fields such as graphic representation
and problem-solving for more knowledge-intensive fields. Finally,
the early integrated circuits of the 1960s gave way to
microprocessors and microcomputers in the early 1970s. This
heralded a near future in which computers would be increasingly
fast and ubiquitous. In a clear virtuous cycle, the enhanced
cheapness of processing power and storage would encourage the
proliferation of applications.
This work takes up where Building the Second Mind: 1956 and the
Origins of Artificia l Intelligence Computing ended. That book studied
this field from the distant prehistory of abstract thinking, through its
early formation as a field of study in the mid-1950s, and the several
years following that turning point. Watching the advances of the
1960s and 1970s offers the satisfying vindication of the early efforts
of AI’s founders- John McCarthy, Marvin Minsky, Allen Newell, and
Herbert Simon.
Enthralled with Technology
“ O Brave New World that has such people in it !” Aldous Huxley, Brave New World . 1932.
The early 1960s was an expansive, ebullient era. The NASA mission
to land a man on the Moon, initiated in 1961 and achieved in 1969,
was one of the most memorable events of both the Cold War and
scientific and technical history. During the Cuban Missile Crisis of
1962-1963, the United States confronted Cuba, and by proxy the
USSR, demanding that the Soviets refrained from stationing missiles
in the Caribbean. The peaceful resolution of this showdown proved
that the USA at least held its own, and could keep the Soviets from
maintaining a military presence in the Western Hemisphere. The
conquest of diseases through vaccines continued. The cultural
fascination with the products of technological modernity continued:
new materials in the form of novel fabrics, building materials, and
jarring and atonal music and art, ensured that cultural
enlightenment included an element of discomfort and shock. High
Modernist architecture provided exposed metal and glass, with
enough sharp edges and right angles to fill a geometry textbook.
Freeways plowed through cities: an elevated highway wrecked the
San Francisco waterfront until an earthquake took it down, and a
highway nearly was run through the dense streets of Greenwich
Village in New York City. The sleek and metallic and manufactured
and aerodynamic were lauded; the quaint and candlelit and rustic
had no place.
Modernism in the form of the wholesale glorification of the new,
including faith in AI and the progress offered by computation, would
see profound challenges. The central cultural shocks which began to
break Modernism apart had not taken place yet. But they were
beginning to appear: Jane Jacobs’ influential masterwork The Death
and life of great American cities appeared in 1961 (1), and Silent
Spring by Rachel Carson was published in 1963 (2). Jacobs helped
to bring about the new urban movement of densely populated,
village-like urban centers, and Silent Spring decried environmental
pollution. Both helped to bring caveats to technological growth, but
the early 1960s saw only the very beginning of doubts as to its
munificence. Likewise, the ardent participation of both parties in the
Cold War had not yet begun to show fatigue.
Building the Second Mind will be an advocate of computer
technology as an augmentation technology for human abilities and
as a research technology for cognitive science. General belief in- if
not blind adhesion to- scientific progress has been an occupational
requirement for writing this work. However, the caveats to
technological progress issued by such challenges to Modernism are
significant, and will be considered later in this book.
The Context of AI’s Progress
Artificial Intelligence was one of the great triumphs of Postwar
technological optimism, and this time period was one of its sweet
spots. During the 1960s and 1970s, the field developed
tremendously. It started to devise ways to represent visual data by
computation, as in the 1966 MIT Summer Vision project; began to
examine the mysteries of creativity (in the work of Herbert Simon
and his students); tore apart sentences for their meaning (Schank
conceptual primitives, Bobrow calculus problem parser); and
developed means to represent high-level scientific problem-solving
and classification knowledge (Buchanan and Feigenbaum’s Dendral
program), among other things.
AI’s earlier history, in the years of the postwar research and
development frenzy, had been more precarious. After the Second
World War, the idea of making machines think had seemed strange
and disturbed. Beyond this, it had often been perceived as morally
bad, and the whole concept sinister. The appearance of arguments
in its favor, or at least those urging measured equivocation, was a
welcome salve. But by the late 1960s, the situation was immensely
different. The shoe was on the other foot, and the field had
achieved success with chess-playing programs, with the Logic
Theorist, with calculus-solving programs, with early robotic arms,
and most recently with the Dendral program, which solved
diagnoses in internal medicine. With the appearance of the
Advanced Research Projects Agency (ARPA, known as Darpa
starting in 1972), AI had attained research centers, tenure-track
professors, and influence in large-scale, capital-intensive
timesharing projects. Throughout the 1970s the tasks which the
field was capable of doing proceeded to be come far more erudite,
as better forms of abstraction and embodiment of knowledge were
created. As Edward Feigenbaum, a Stanford researcher who will be
important to this book said, the field had a great deal of low fruit,
easily attained, to pick.
This book tells this story.
2. Themes
The thematic concerns of this work should not overshadow its
historical nature. We recognize that there is a voluminous and
enthusiastic body of knowledge regarding the history and sociology
of technology and science. Our concern with contribution to this
body of knowledge is first and foremost expository rather than
theoretical. We will revisit the themes of the last book. In other
cases they are revised because of the change in the lessons of
history.
First, the successive successes of AI are salient in their most
unalloyed form in this time period. During the 1950s all advances
were painstaking and even the most simple thing took astonishing
effort. During the 1960s, and into the 1970s, AI successively
knocked down the list of things that had seemed that it would not
be able to do. Herbert Simon’s 1963 scholarship began to analyze
creativity in a systematic fashion. Programs involving schematized
worlds, or “toy” domains, were devised. More difficult domains,
such as the systematic but complex field of mass spectrometry,
were and AI programs analyzed spectrometric data. This was
accomplished by a program known technically as a production
systems, which would later be developed as “expert systems” in the
1980s. Vision, robotics, computer chess, and knowledge
representation all underwent new and fundamental changes.
Second, the theme of the successful implementation of technological
innovation by governmental programs- war both hot and cold, is
repeated (3). The author said it in the beginning of Building the
Second Mind: 1956 and the Origins of AI Computing, and shall say it
again here. The role of the military in the initial birth and later
development of the computer and its ancillary technologies cannot
be erased, eroded, or diminished. As we saw in the first book, this
was true of the earliest development of the digital computer during
the Second World War, and of its progress along a broad variety of
fronts during the postwar decades. The advances which may be
directly attributed to the war effort- the Cold War effort, that is-
include relay memory followed by magnetic core memory; storage
in the form of punched cards and magnetic tape; input-output in the
form of followed by teletype operations, and finally the CRT screen
with a keyboard. The crucial role of government policy in expressly
and successfully encouraging computer development is a well-
articulated scholarly theme (4). Our study is simply a further
corroboration.
Third, a well-tuned organizational culture was also essential in the
development of the computer in every aspect, along the course of
the entire chronology. ARPA allowed and encouraged creative
people to work on their own terms, at a multiplicity of institutions-
the Rand Corporation, SRI, and the laboratories at each of the
universities, among others. The same results would not have taken
place without the ARPA bureaucracy and the institutional freedom
which it encouraged.
A fourth theme is that of the paradigm transition in scientific and
popular conception of computers and machinery entailed by AI. The
essay of AI itself envisions an active intelligence apart from
humans. Getting this new gestalt in place has required the
incipience of acceptance of the computer, and a rethinking of the
capabilities of human tools. Belief in the computational metaphor
for the human mind, and belief in the possibility of computing as
qualitative as well as only numerical and quantitative information
processing, were established among a very small cohort by the time
discussed in the beginning of this book. The corner had already
been turned in these erudite circles, but the decades of the 1960s
and 1970s saw the vast widening of the software to prove that
programs could emulate cogitation, and the hardware which could
carry this out. The proof was in the software.
Fifth and finally, our study is concerned with the profound
contributions of AI beyond the area of its designated efforts- to build
tools that do ‘intelligent’ things and to emulate human intelligence.
Much of the achievement of the field during this time consisted of AI
researchers’ development of computer languages and operating
systems more generic to research and consumer computing (the
Multics program, which helped lead to C++ and Unix); more
interactive input-output in the form of input through a keyboard,
with a monitor one looks at; and useful computing tools such as
newswire programs, editing conventions, and the concept of
software agents for a wide range of tasks. Put in even simpler
terms: AI and its wider circles was absolutely central to the
development of the personal computer and the popular Internet.
Outline of the Chapters
The history of AI through 1956, and in the several years afterwards,
naturally structures itself with the culmination of field’s foundation.
The history of AI during the 1960s and 1970s naturally structures
itself as well. The onslaught of monies given to AI and other
computing disciplines, following the 1961 selection of ARPA to be
the pre-eminent research agency for funding computer science, was
a game-changing windfall. It could be compared to a television or
movie sight gag in which the door is opened and a flood ensues.
Thus the narrative begins with a bang, imposed by history, as the
increased effort in the United States’ space program meant
increased funding for AI as well.
We commence by presenting the framing facts of the integrated
circuit (IC), which has allowed computers that offer bigger, better,
faster, and more computing, and ARPA, which footed the tab for its
development. These were invented and founded in 1959 and 1958
respectively, but did not become prominent until their country
needed them, so to speak, because of the NASA Apollo mission. The
1962 foundation of ARPA’s Information Processing Techniques
Office, and its immeasurable contributions in funding much AI-
related research during these two decades, are discussed as it
specifically relates to AI’s agenda (Chapters Two and Three). The wide
dispersal of improvements to input-output, memory, and storage
are discussed in Chapter Four.
Part III . The Belle Epoque of the 1960s
The sine qua non of ARPA’s contribution to AI was its free rein to
researchers. AI flourished in response to opportunity unfettered. We
refer to this decade as the Belle Epoque, in reference to the
salubrious climate for art and literature prior to the First World War.
In Chapter Five we discuss the more general task of institution-
building that nurtured AI.
At the Carnegie Institute of Technology (now Carnegie Mellon
University), Newell and Simon expanded upon the schematic and
simple problems they had initially taken on with the General
Problem Solver program, and began to work on the production
system protocol for solving toy problems. These programs, which
were seminal for cognitive science, were generally not concerned
with domain knowledge, but with the nature of accretion of
knowledge in small, closely defined ‘toy’ domains such as games
and SAT-type problems (Chapter Six). Today computer programs can
solve hard problems; at that time solving tiny problems was an
enormous achievement. Closely influenced by Newell and Simon
(and thus included in the CMU chapter), Bruce Buchanan and
Simon’s protégée Edward Feigenbaum at Stanford, initiated the
Dendral project, AI’s first significant success in solving a problem in
a difficult scientific domain.
Chapters Five and Seven address AI at MIT and Stanford respectively.
Embodiment of intelligence through vision and robotics proceeded
with work at the MIT AI Lab, and at the Stanford AI Lab (‘SAIL’),
established by John McCarthy in the early 1960s. Concepts such as
the representation of graphic imagery by pixels were first