The article considers the complexities of thinking about the computer as a model of the mind. It examines the computer as being a model of the brain in several very different senses of “model‘. On the one hand the basic architecture of the first modern stored-program computers was „modeled on“ the brain by John von Neumann. Von Neumann also sought to build a mathematical model of the biological brain as a complex system. A similar but different approach to modeling the brain was taken by Alan Turing, who on the one hand believed that the mind simply was a universal computer, and who sought to show how brain-like networks could self-organize into Universal Turing Machines. And on the other hand, Turing saw the computer as the universal machine that could simulate any other machine, and thus any particular human skill and thereby could simulate human intelligence. This leads to a discussion of the nature of “simulation” and its relation to models and modeling. The article applies this analysis to a written correspondence between Ashby and Turing in which Turing urges Ashby to simulate his cybernetic Homeostat device on the ACE computer, rather than build a special machine.

This chapter sketches an intellectual portrait of W. Ross Ashby’s thought from his earliest work on the mechanisms of intelligence in 1940 through the birth of what is now called artificial intelligence (AI), around 1956, and to the end of his career in 1972. It begins by examining his earliest published works on adaptation and equilibrium, and the conceptual structure of his notions of the mechanisms of control in biological systems. In particular, it assesses his conceptions of mechanism, equilibrium, stability, and the role of breakdown in achieving equilibrium. It then proceeds to his work on refining the concept of “intelligence,” on the possibility of the mechanical augmentation and amplification of human intelligence, and on how machines might be built that surpass human understanding in their capabilities. Finally, the chapter considers the significance of his philosophy and its role in cybernetic thought.

Key words: artificial intelligence, machine intelligence, equilibrium, human intelligence, cybernetic thought

Niklas Luhmann is not exactly known for his thinking about a possible change of the society due to the introduction of the computer. His society is the modern society, based on the overall importance of the communication medium of the printing press. Yet, his double volume book on Die Gesellschaft der Gesellschaft is so rich in remarks about the possible influence of the introduction of the computer on the society, equal only to the introduction of, first, writing and, then, the printing press, that one might be tempted to consider this book his way to bid farewell to the modern culture of the society based on the printing press. Let us look at what modern society has achieved relying on a notion of order stemming, with only slight exaggeration, from the library, and then let us try to watch how this very same society has to find equally wide-ranging solutions to a society relying, for a dominant part of its communication, on an order adapted to the computing machine, or so he seems to tell us. This paper looks at Die Gesellschaft der Gesellschaft in terms of a theory of the emerging computer culture of a society we cannot any more call the modern one. And it proposes to call for a competition to complete one of the most speculative chapters of this book in which Luhmann attributes the central cultural notion, or theory form, of the literal society, telos, to Aristotle, of the printing press society, self-referential restlessness, to Descartes, and leaves the slot open for the one possibly defining the culture of the computer society, which is the theory form of the form.

When confronted with issues dealing with first and second order cybernetics, it seems that the manner of defining the former has been somewhat caricatured. The second appears to sometimes give rise to conclusions which are almost opposite to those of Wiener by questioning the possibility of a control for a system. We find in Wiener’s research a prefiguration of the autonomy concept, which, in our opinion, could bring an explanation – and a solution – in cases where control elicits some perverse effect; an acceptance of positive feedback if it serves a desired purpose; the central importance held for him by ergodic theory that we use in an addendum on imbalanced strange attractors control; the idea of a knowledge which may be the fruit of the control; an interest for logical paradoxes he put in relation to communication in nervous system; and already the notion of dialogue in the core of the relation man/man or man/machine. Of course, Wiener did not accord an equal development to all his insights, but we have not yet finished scrutinizing his writings. First and second order cybernetics perhaps form an agonistic/antagonistic couple of which neither element could overshadow the other.

Key words: communications, control, cybernetics, systems theory

Purpose: Yerkish is an artificial language created in 1971 for the specific purpose of exploring the linguistic potential of nonhuman primates. The aim of this paper is to remind the research community of some important issues and concepts related to Yerkish that seem to have been forgotten or appear to be distorted. These are, particularly, its success, its promising aspects for future research and last but not least that it was Ernst von Glasersfeld who invented Yerkish: he coined the term “lexigrams,” created the first 120 of them and designed the grammar that regulated their combination. Design: The first part of this paper begins with a short outline of the context in which the Yerkish language originated: the original LANA project. It continues by presenting the language itself in more detail: first, its design, focusing on its “lexigrams” and its “correlational” grammar (the connective functions or “correlators” and the combinations of lexigrams, or “correlations”), and then its use by the chimpanzee Lana in formulating sentences. The second part gives a brief introduction to the foundation of Yerkish in Silvio Ceccato’s Operational Methodology, particularly his idea of the correlational structure of thought and concludes with the main insights that can be derived from the Yerkish experiment seen in the light of Operational Methodology. Findings: Lana’s success in language learning and the success of Yerkish during the past decades are probably due to the characteristics of Yerkish, particularly its foundation in operational methodology. The operation of correlation could be what constitutes thinking in a chimpanzee and an attentional system could be what delivers the mental content that correlation assembles into triads and networks. Research implications: Since no other assessment or explanation of Lana’s performances has considered these foundational issues (findings), a new research project or program should validate the above-mentioned hypotheses, particularly the correlational structure of chimpanzee thinking.

Key words: Yerkish, artificial language, correlational grammar, operational methodology, Silvio Ceccato, machine translation, animal communication

Open peer commentary on the article “Constructivism and Computation: Can Computer-Based Modeling Add to the Case for Constructivism?” by Manfred Füllsack. Upshot: Füllsack’s article offers many interesting ideas but falls short of elucidating the relationship between constructivism and computation. It could profit by taking into consideration stronger constructivist foundations such as the distinction between machine and organism, the relationship between reality and the observer, and Ceccato’s theory of attention.

Open peer commentary on the article “Applying Radical Constructivism to Machine Learning: A Pilot Study in Assistive Robotics” by Markus Nowak, Claudio Castellini & Carlo Massironi. Upshot: Applying radical constructivism to machine learning is a challenge that requires us to dive very deeply into its theory of knowing and learning. We need to clarify its fundamental concepts, if possible, in operational terms. This commentary aims at outlining how this kind of clarification could look in the case of 3 such concepts: (a) the construction of experiential reality; (b) learning as a constructive activity; (c) the viability of conceptual structures.

Open peer commentary on the article “A Computational Constructivist Model as an Anticipatory Learning Mechanism for Coupled Agent–Environment Systems” by Filipo Studzinski Perotto. Upshot: Interaction-based models of cognition force anticipatory and constructivist models. The CALM model offers significant development of such models within a machine learning framework. It is suggested that moving to an entirely interactive-based model offers still further advantages.

“Strong computationalism” holds that any suitably programmed computer instantiates genuine conscious mental states purely in virtue of carrying out a specific series of computations. The argument presented herein is a simple development of that originally presented in Putnam’s “Representation & Reality”, which if correct, has important implications for Turing machine functionalism and the prospect of “conscious” machines. In the paper, instead of seeking to develop Putnam’s claim that, “everything implements every finite state automata”, I will try to establish the weaker result that “everything implements the specific machine Q on a particular input set (x)”. Then, equating Q (x) to any putative AI program, I will show that conceding the “strong AI” thesis for Q (crediting it with mental states and consciousness) opens the door to a vicious form of panpsychism whereby all open systems, (e.g., grass, rocks, etc.), must instantiate conscious experience and hence that disembodied minds lurk everywhere. Relevance: This paper critiques the computational accounts of mind and cognition using a construction borrowed from Putnam.

For many years three key aspects of creative processes have been glossed over by theorists eager to avoid the mystery of consciousness and instead embrace an implicitly more formal, computational vision: autonomy, phenomenality and the temporally embedded and bounded nature of creative processes. In this paper we will discuss autopoiesis and creativity; an alternative metaphor which we suggest offers new insight into these long overlooked aspects of the creative processes in humans and the machine, and examine the metaphor in the context of dance choreography.

Key words: autonomy, autopoiesis, creativity, dance, improvisation.