CYBERNETICS — A Definition
Artificial Intelligence and cybernetics: Aren't they the same
thing? Or, isn't one about computers and the other about robots?
The answer to these questions is emphatically, No.
Researchers in Artificial Intelligence (AI) use computer
technology to build intelligent machines; they consider implementation
(that is, working examples)
as the most important result. Practitioners of cybernetics use
models of organizations, feedback, goals, and conversation to understand
the capacity and limits
of any system (technological, biological, or social); they
consider powerful descriptions as the most important result.
The field of AI first flourished in the 1960s as the
concept of universal
computation [Minsky 1967], the cultural view of the brain as
a computer, and the availability of digital computing machines
came together to paint a future where computers were at least as
smart as humans. The field of cybernetics came into being in the late
1940s when
concepts of information, feedback, and regulation [Wiener 1948]
were generalized from specific applications in engineering
to systems in general, including systems of living organisms,
abstract intelligent processes, and language.
Origins of "cybernetics"
The term itself began its rise to popularity in 1947 when Norbert Wiener used
it to name a discipline apart from, but touching upon, such established
disciplines as electrical engineering, mathematics, biology,
neurophysiology, anthropology, and psychology. Wiener, Arturo
Rosenblueth, and Julian Bigelow needed a name for
their new discipline, and they adapted a Greek word meaning "the art of steering"
to evoke the rich interaction of goals, predictions, actions,
feedback, and response in systems of all kinds (the term "governor"
derives from the same root) [Wiener 1948]. Early applications
in the control of physical systems (aiming artillery, designing
electrical circuits, and maneuvering simple robots) clarified
the fundamental roles of these concepts in engineering; but the
relevance to social systems and the softer sciences was also
clear from the start. Many researchers from the 1940s through
1960 worked solidly within the tradition of cybernetics without
necessarily using the term, some likely (R. Buckminster Fuller)
but many less obviously (Gregory Bateson, Margaret Mead).
Limits to knowing
In working to derive functional models common to all systems,
early
cybernetic researchers quickly realized that their "science
of observed systems" cannot be divorced from "a science
of observing systems" — because it
is we who observe [von Foerster 1974]. The cybernetic approach is
centrally concerned
with this unavoidable limitation of what we can know: our own
subjectivity.
In this way cybernetics is aptly called "applied epistemology".
At minimum, its utility is the production of useful descriptions,
and, specifically, descriptions that include the observer in
the description. The shift of interest in cybernetics from
"observed systems" — physical systems such as thermostats or complex
auto-pilots — to "observing systems" — language-oriented systems such as
science or social systems — explicitly incorporates the observer into
the description, while maintaining a foundation in feedback, goals, and
information. It applies the cybernetic frame to the process of
cybernetics itself. This shift is often characterized as a transition
from 'first-order cybernetics' to 'second-order cybernetics. Cybernetic
descriptions of psychology, language,
arts, performance, or intelligence (to name a few) may be quite
different from more conventional, hard "scientific"
views — although cybernetics can be rigorous too. Implementation
may then follow in software and/or hardware, or in the design
of social, managerial, and other classes of interpersonal
systems.
Origins of AI in cybernetics
Ironically but logically, AI and cybernetics have each gone in
and out of fashion and influence in the search for machine intelligence.
Cybernetics started in advance of AI, but AI dominated between 1960 and
1985, when repeated failures to achieve its claim of building
"intelligent machines" finally caught up with it. These difficulties in
AI led to renewed search for solutions that mirror prior approaches of
cybernetics. Warren McCulloch and Walter Pitts were the first to propose
a synthesis of neurophysiology and logic that tied the capabilities of
brains to the limits of Turing computability [McCulloch & Pitts
1965]. The euphoria that followed spawned the field of AI [Lettvin 1989]
along with early work on computation in neural nets, or, as then
called, perceptrons. However the fashion of symbolic computing rose to
squelch perceptron research in the 1960s, followed by its resurgence in
the late 1980s. However this is not to say that current fashion in
neural nets is a return to where cybernetics has been. Much of the
modern work in neural nets rests in the philosophical tradition of AI
and not that of cybernetics.
Philosophy of cybernetics
AI is predicated on the presumption that knowledge is a
commodity
that can be stored inside of a machine, and that the
application
of such stored knowledge to the real world constitutes
intelligence
[Minsky 1968]. Only within such a "realist" view of
the world can, for example, semantic networks and rule-based
expert systems appear to be a route to intelligent machines.
Cybernetics in contrast has evolved from a "constructivist"
view of the world [von Glasersfeld 1987] where objectivity
derives
from shared agreement about meaning, and where information (or
intelligence for that matter) is an attribute of an interaction
rather than a commodity stored in a computer [Winograd &
Flores 1986]. These differences are not merely semantic in character,
but rather determine fundamentally the source and direction of
research performed from a cybernetic, versus an AI, stance.
|
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| Underlying philosophical differences between AI and
cybernetics are displayed by showing how they each construe the terms in
the central column. For example, the concept of "representation" is
understood quite differently in the two fields. Relations on the left
are causal arrows and reflect the reductionist reasoning inherent in
AI's "realist" perspective that via our nervous systems we discover
the-world-as-it-is. Relations on the right are non-hierarchical and
circular to reflect a "constructivist" perspective, where the world is
invented (in contrast to being discovered) by an intelligence acting in a
social tradition and creating shared meaning via hermeneutic (circular,
self-defining) processes. The implications of these differences are
very great and touch on recent efforts to reproduce the brain [Hawkins
2004, IBM/EPFL 2004] which maintain roots in the paradigm of "brain as
computer". These approaches hold the same limitations of digital
symbolic computing and are neither likely to explain, nor to reproduce,
the functioning of the nervous system. |
Influences
Winograd and Flores credit the influence of Humberto Maturana,
a biologist who recasts the concepts of "language"
and "living system" with a cybernetic eye [Maturana
& Varela 1988], in shifting their opinions away from the
AI perspective. They quote Maturana: "Learning is not a
process of accumulation of representations of the environment;
it is a continuous process of transformation of behavior through
continuous change in the capacity of the nervous system to
synthesize
it. Recall does not depend on the indefinite retention of a
structural
invariant that represents an entity (an idea, image or symbol),
but on the functional ability of the system to create, when
certain
recurrent demands are given, a behavior that satisfies the
recurrent
demands or that the observer would class as a reenacting of a
previous one." [Maturana 1980] Cybernetics has directly
affected software for intelligent training, knowledge
representation, cognitive modeling, computer-supported coöperative
work, and neural modeling. Useful results have been demonstrated
in all these areas. Like AI, however, cybernetics has not produced
recognizable solutions to the machine intelligence problem, not at
least for domains considered complex in the metrics
of symbolic processing. Many beguiling artifacts have been
produced
with an appeal more familiar in an entertainment medium or to
organic life than a piece of software [Pask 1971]. Meantime,
in a repetition of history in the 1950s, the influence of
cybernetics
is felt throughout the hard and soft sciences, as well
as in AI. This time however it is cybernetics' epistemological
stance — that all human knowing is constrained by our perceptions and
our beliefs, and hence is subjective —
that is its contribution to these fields. We must continue to wait
to see if cybernetics leads to breakthroughs in the construction of
intelligent artifacts of the complexity of a nervous system, or a brain.
Cybernetics Today
The term "cybernetics" has been widely misunderstood, perhaps
for two broad reasons. First, its identity and boundary are difficult to
grasp. The nature of its concepts and the breadth of its applications,
as described above, make it difficult for non-practitioners to form a
clear concept of cybernetics. This holds even for professionals of all
sorts, as cybernetics never became a popular discipline in its own
right; rather, its concepts and viewpoints seeped into many other
disciplines, from sociology and psychology to design methods and
post-modern thought. Second, the advent of the prefix "cyb" or "cyber"
as a referent to either robots ("cyborgs") or the Internet
("cyberspace") further diluted its meaning, to the point of serious
confusion to everyone except the small number of cybernetic experts.
However, the concepts and origins of cybernetics have become of
greater interest recently, especially since around the year 2000. Lack
of success by AI to create intelligent machines has increased curiosity
toward alternative views of what a brain does [Ashby 1960] and
alternative views of the biology of cognition [Maturana 1970]. There is
growing recognition of the value of a "science of subjectivity" that
encompasses both objective and subjective interactions, including
conversation [Pask 1976]. Designers are rediscovering the influence of
cybernetics on the tradition of 20th-century design methods, and the
need for rigorous models of goals, interaction, and system limitations
for the successful development of complex products and services, such as
those delivered via today's software networks. And, as in any social
cycle, students of history reach back with minds more open than was
possible at the inception of cybernetics, to reinterpret the meaning and
contribution of a previous era.
