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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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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).

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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.

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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 aprevious 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 toorganic life than a piece of software (Pask 1971). Meantime,in a repetition of history in the 1950s, the influence of cyberneticsis felt throughout the hard and soft sciences, as wellas 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.

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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.

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Here's a couple  videos that may help you get it easier...

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Here's a couple  videos that may help you get it easier...

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https://youtu.be/jjzUEhFvtsg https://youtu.be/EPGtPWRz1Ng