During the sixties, a most curious symbiosis took hold between Heinz von Foerster then the Director of a top-notch and lavishly funded US laboratory [Biological Computer Laboratory, 1958–1975] and the Chilean neuroscientist Humberto R. Maturana professor at the Universidad de Chile. The chance encounter between them triggered a long-lasting friendship and a fundamental change in our understanding of Systems Science. In particular the contributions of Biology of Cognition and Autopoiesis are important to understand this change and the years 1968–1973 are (...) particularly relevant. In this period, Maturana published, as a Technical Report of BCL, the foundational document The Neurophysiology of Cognition. Later, in 1973, the book De Maquinas y Seres Vivos put forward the notion of Autopoietic Systems. Also, between 1971 and 1973, the Allende government tried to cybernitize the Chilean economy with the SYNCO project. In that project, Maturana and von Foerster played a role by teaching Cybernetics to the engineers and administrators of this technological dream. During this same time, Cybernetics was important in Chile and Professors Maturana and von Foerster although not directly involved in the day-to-day running of SYNCO were important in that unique effort. When a military coup deposed Allende and sent Chile into a tailspin of right-wing retribution, von Foerster, a foreigner who loved Chile and its people, did his outmost to assure the safety of many people involved in this unique Latin American Cybernetics dream. (shrink)

The problem of epistemic opacity in Artificial Intelligence is often characterised as a problem of intransparent algorithms that give rise to intransparent models. However, the degrees of transparency of an AI model should not be taken as an absolute measure of the properties of its algorithms but of the model’s degree of intelligibility to human users. Its epistemically relevant elements are to be specified on various levels above and beyond the computational one. In order to elucidate this claim, I first (...) contrast computer models and their claims to algorithm-based universality with cybernetics-style analogue models and their claims to structural isomorphism between elements of model and target system. While analogue models aim at perceptually or conceptually accessible model-target relations, computer models give rise to a specific kind of underdetermination in these relations that needs to be addressed in specific ways. I then undertake a comparison between two contemporary AI approaches that, although related, distinctly align with the above modelling paradigms and represent distinct strategies towards model intelligibility: Deep Neural Networks and Predictive Processing. I conclude that their respective degrees of epistemic transparency primarily depend on the underlying purposes of modelling, not on their computational properties. (shrink)