CYBERNETICS — A Definition
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. 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.
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 cooperative 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, or at least not 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.
Hawkins, Jeff and Blakeslee, Sandra, On Intelligence. Times Books, 2004.
IBM/Ecole Polytechnique Fédérale de Lausanne (EPFL), http://bluebrainproject.epfl.ch/, 2004.
Lettvin, Jerome Y., "Introduction to Volume 1" in W S McCulloch., Volume 1, ed., Rook McCulloch, Salinas,
California: Intersystems Publications, 1989, 7-20.
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