Building the Second Mind, 1961-1980: From the Ascendancy of ARPA-IPTO to the Advent of

394 Pages·2014·1.33 MB·English

by  Rebeccae Skinner

UC Berkeley
UC Berkeley Previously Published Works
Title
Building the Second Mind, 1961-1980: From the Ascendancy of ARPA-IPTO to the Advent of
Commercial Expert Systems
Permalink
https://escholarship.org/uc/item/7ck3q4f0
ISBN
978-0-989453-4-6
Author
Skinner, Rebecca Elizabeth
Publication Date
2013-12-31
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
copyright 2013 Rebecca E. Skinner
ISBN 978 09894543-4-6
Forward
Part I. Introduction
Preface
Chapter 1. Introduction: The Status Quo of AI in 1961
Part II. Twin Bolts of Lightning
Chapter 2. The Integrated Circuit
Chapter 3. The Advanced Research Projects Agency
and the Foundation of the IPTO
Chapter 4. Hardware, Systems and Applications in the
1960s
Part II. The Belle Epoque of the 1960s
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
Part III. The Challenges of 1970
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
Part IV. Big Ideas and Hardware Improvements in the 1970s
invert these and put the hardware chapter first
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
Bibliography
Endnotes
Forward to the Beta Edition
This book continues the story initiated in Building
the Second Mind: 1956 and the Origins of Artificial
Intelligence Computing. Building the Second Mind,
1961-1980: From the Establishment of ARPA to the Advent
of Commercial Expert Systems continues this story,
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.
In provenance, the entire Building the Second Mind
effort 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 now make up several chapters of
the third volume. This work is neither highly technical
nor entirely comprehensive: my interests are skewed in
favor of knowledge representation. It 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. Everyone is someone’s popularizer.
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 three months and then release a revised ePub
edition later in 2012.
Preface
Building the Second Mind, 1961-1980: From the
Establishment of ARPA to the Advent of Commercial Expert
Systems tells the story of the development, during the
1960s and 1970s, of AI, the field that sought to get
computers to do things that would be considered
intelligent if a person did them. In the late 1950s, the
field was 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, 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 time at 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 is the sequel to Building the Second Mind: 1956
and the Origins of Artificial Intelligence Computing,
which studied this field from the distant prehistory of
abstract thinking through its early formation as a field
of study in the mid-1950s. Watching the advances of the
1960s and 1970s offers a 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.
The early 1960s was an expansive, ebullient era. The
confrontation and showdown of the United States with
Cuba, and by proxy with the USSR, during the Cuban
Missile Crisis of 1962-1963, proved that the USA at least
held its own and could keep the Soviets from maintaining
a military presence in the Western Hemisphere. 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.
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 aesthetics of the waterfront of San
Francisco 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- including faith in AI and the progress offered
by computation- would see profound challenges. Some of
the central cultural shocks which began to break
Modernism apart had not taken place yet. Jane Jacobs’
influential masterwork The Death and life of great
American cities appeared in 1961 (1)1, and Silent Spring
by Rachel Carson was published in 1963 (2)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 beginning of questions to its munificence.
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, if not blind adhesion, to
scientific progress, is 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 without a doubt one of the
great triumphs of postwar technological optimism. During
the 1960s and 1970s, the field of AI 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 (Simon article); 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, not only had the
idea of making machines think seemed strange and
disturbed. 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 mid-1960s
and late 1960s, the situation was immensely different.
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 money-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- and in several cases repeat- 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 phase of the work.
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. The technical means
through which this was accomplished is in 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. The low fruit phase, as referred to
earlier, was intrinsically attractive and- it’s too good
to resist- fruitful.
Second, the theme of the successful implementation of
technological innovation by governmental programs- war
both hot and cold, is repeated (3)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 can
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)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
entailed by AI. AI envisioning an active intelligence
apart from humans: getting this new gestalt in place
required a kind of thinking and rethinking that was
difficult in more than an algorithmic and logistical
fashion. 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. The corner had already been turned in these
circles, but the two decades we study in this book 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.
Outline of the Chapters
The history of AI through 1956, and in the several years
afterwards, naturally structured itself with the
culmination of field’s foundation. The history of AI
during the 1960s and 1970s naturally structures itself as
well. 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.
Inside the field of AI itself, there was a will but no
easily visible way to move from very simple problems to
bigger domains.
An immense and consequential event took place early on in
this history. The onslaught of monies shoveled at AI (and
other computing disciplines) following the 1961 selection
of ARPA to be the pre-eminent research agency for funding
computer science was a great windfall. It could be
compared to a television or movie sight gag in which the
door is opened and a flood ensues, or Willy Wonka’s
Golden Ticket.
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. The 1958
foundation of ARPA, 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 II. The Belle Epoque
The sine qua non of ARPA’s contribution to AI was its
free rein to researchers: in response to opportunity
unfettered, AI flourished. 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, and began to work on the