Operative communication: project Cybersyn and the intersection of information design, interface design, and interaction design

This article examines the connecting lines between the Chilean Project Cybersyn’s interface design, the German Hochschule für Gestaltung Ulm and its cybernetically inspired approaches towards information design, and later developments in interaction design and the emerging field of Human–Computer Interaction in the USA. In particular, it first examines how early works of designers Tomàs Maldonado and Gui Bonsiepe on operative communication, that is, language-independent (and thus internationalizable) pictogram systems and visual grammars for computational systems, were intertwined with attempts to ground industrial design in a scientific methodology, to address an era of computing machines, and to develop the concept of the interface as a heuristic for a renovated design thinking. It thereby also reconstructs further historical vanishing lines—e.g. the pictorial grammar of Otto Neurath’s ISOTYPE—of the development of the ‘ulm model’ of design. Second, the article explores how an apprehension of first-order cybernetics in West Germany—e.g. represented by hfg ulm staff like Max Bense or Abraham Moles, merged with Cybersyn’s second-order cybernetics ideas, as represented by Stafford Beer’s Viable System Model. And third, it asks about a further conceptual turn regarding an understanding of design which resulted in a focus on communicative interaction, e.g. in the later works of Fernando Flores and Terry Winograd on HCI, or in Beer’s Team Syntegrity approach. As an effect, the text will explore a specific and international network of cybernetic thinking between Latin America, Europe, and North America which emerged around Project Cybersyn, and which was occupied with questions of HCI, a democratization of design, and intelligence amplification.

A New Class of Autopoietic and Cognitive Machines

Making computing machines mimic living organisms has captured the imagination of many since the dawn of digital computers. However, today’s artificial intelligence technologies fall short of replicating even the basic autopoietic and cognitive behaviors found in primitive biological systems. According to Charles Darwin, the difference in mind between humans and higher animals, great as it is, certainly is one of degree and not of kind. Autopoiesis refers to the behavior of a system that replicates itself and maintains identity and stability while facing fluctuations caused by external influences. Cognitive behaviors model the system’s state, sense internal and external changes, analyze, predict and take action to mitigate any risk to its functional fulfillment. How did intelligence evolve? what is the relationship between the mind and body? Answers to these questions should guide us to infuse autopoietic and cognitive behaviors into digital machines. In this paper, we show how to use the structural machine to build a cognitive reasoning system that integrates the knowledge from various digital symbolic and sub-symbolic computations. This approach is analogous to how the neocortex repurposed the reptilian brain and paves the path for digital machines to mimic living organisms using an integrated knowledge representation from different sources.

Why Can the Brain (and Not a Computer) Make Sense of the Liar Paradox?

Ordinary computing machines prohibit self-reference because it leads to logical inconsistencies and undecidability. In contrast, the human mind can understand self-referential statements without necessitating physically impossible brain states. Why can the brain make sense of self-reference? Here, we address this question by defining the Strange Loop Model, which features causal feedback between two brain modules, and circumvents the paradoxes of self-reference and negation by unfolding the inconsistency in time. We also argue that the metastable dynamics of the brain inhibit and terminate unhalting inferences. Finally, we show that the representation of logical inconsistencies in the Strange Loop Model leads to causal incongruence between brain subsystems in Integrated Information Theory.

Introduction: Algorithmic Thought

This introduction to a special section on algorithmic thought provides a framework through which the articles in that collection can be contextualised and their individual contributions highlighted. Over the past decade, there has been a growing interest in artificial intelligence (AI). This special section reflects on this AI boom and its implications for studying what thinking is. Focusing on the algorithmic character of computing machines and the thinking that these machines might express, each of the special section’s essays considers different dimensions of algorithmic thought, engaging with a diverse set of epistemological questions and issues.

Why Can the Brain (And Not a Computer) Make Sense of the Liar Paradox?

Ordinary computing machines prohibit self-reference because it leads to logical inconsistencies and undecidability. In contrast, the human mind can understand self-referential statements without necessitating physically impossible brain states. Why can the brain make sense of self-reference? Here, we address this question by defining the Strange Loop Model, which features causal feedback between two brain modules, and circumvents the paradoxes of self-reference and negation by unfolding the inconsistency in time. We also argue that the metastable dynamics of the brain inhibit and terminate unhalting inferences. Finally, we show that the representation of logical inconsistencies in the Strange Loop Model leads to causal incongruence between brain subsystems in Integrated Information Theory.

Infusing Autopoietic and Cognitive Behaviors into Digital Automata to Improve their Sentience, Resilience and Intelligence

The holy grail of Artificial Intelligence (AI) has been to mimic human intelligence using computing machines. Autopoiesis which refers to a system with well-defined identity and is capable of re-producing and maintaining itself and cognition which is the ability to process information, apply knowledge, and change the circumstance are associated with resilience and intelligence. While classical computer science (CCS) with symbolic and sub-symbolic computing has given us tools to decipher the mysteries of physical, chemical and biological systems in nature and allowed us to model, analyze various observations and use information to optimize our interactions with each other and with our environment, it falls short in reproducing even the basic behaviors of living organisms. We present the foundational shortcomings of CCS and discuss the science of infor-mation processing structures (SIPS) that allows us to fill the gaps. SIPS allows us to model su-per-symbolic computations and infuse autopoietic and cognitive behaviors into digital machines. They use common knowledge representation from the information gained using both symbolic and sub-symbolic computations in the form of system-wide knowledge networks consisting of knowledge nodes and information sharing channels with other knowledge nodes. The knowledge nodes wired together fire together to exhibit autopoietic and cognitive behaviors.

User Steering Support in Large-scale Workflows

Large-scale workflows that execute on High-Performance Computing machines need to be dynamically steered by users. This means that users analyze big data files, assess key performance indicators, fine-tune parameters, and evaluate the tuning impacts while the workflows generate multiple files, which is challenging. If one does not keep track of such interactions (called user steering actions), it may be impossible to understand the consequences of steering actions and to reproduce the results. This thesis proposes a generic approach to enable tracking user steering actions by characterizing, capturing, relating, and analyzing them by leveraging provenance data management concepts. Experiments with real users show that the approach enabled the understanding of the impact of steering actions while incurring negligible overhead.

Evaluation of Serpent Capabilities for Hyperfidelity Depletion of Pebble Bed Cores

Accurate burnup calculation in pebble bed reactor cores is today necessary but challenging. The continuous advancement in computing capabilities make the use of Monte Carlo transport codes possible to efficiently study individual pebbles depletion without making strong assumptions. The purpose is to eliminate unnecessary typical assumptions made in existing codes, while being flexible and suitable for commonly available computing machines. Among the available codes, Serpent 2 provides extremely useful tools to make pebble beds modeling and simulation efficient. The explicit stochastic geometry definition handles irregular pebble beds with comparable performances to regular lattices. Optimization modes controlling the use of unionized energy grids, cross-sections pre-calculation and flux calculation through spectrum collapse or direct tally lead to high flexibility and optimal memory usage while limiting calculation time. Automated burnable materials division is a useful tool to lower the memory requirements while quickly generating the geometry and materials. Finally, parallelization and domain decomposition allow for decreasing unreasonable memory constraints for large cores. This work thus explores the possibilities of Serpent 2 when applying depletion in pebble beds, compares the optimization modes and quantifies the simulation time and memory usage depending on the conditions of the calculation. Overall, the results show that Serpent 2 is well adapted to the use of small to large cores calculations with commonly available resources.

A Survey on Contactless Gesture based Interactions

Gesture Based Interaction is the mathematical interpretation of human motion by a computing device. Contactless Gesture Based Interaction with devices aims to offer new possibilities to interact with machines thereby enabling development and design of far more natural and intuitive interactions with computing machines. The system makes use of static and dynamic gestures in order to perform operations on a system. This paper provides a detailed review on contactless systems which facilitates a better means of interaction between humans and machines.

Quantum Foundations of Classical Reversible Computing

The reversible computation paradigm aims to provide a new foundation for general classical digital computing that is capable of circumventing the thermodynamic limits to the energy efficiency of the conventional, non-reversible digital paradigm. However, to date, the essential rationale for, and analysis of, classical reversible computing (RC) has not yet been expressed in terms that leverage the modern formal methods of non-equilibrium quantum thermodynamics (NEQT). In this paper, we begin developing an NEQT-based foundation for the physics of reversible computing. We use the framework of Gorini-Kossakowski-Sudarshan-Lindblad dynamics (a.k.a. Lindbladians) with multiple asymptotic states, incorporating recent results from resource theory, full counting statistics and stochastic thermodynamics. Important conclusions include that, as expected: (1) Landauer’s Principle indeed sets a strict lower bound on entropy generation in traditional non-reversible architectures for deterministic computing machines when we account for the loss of correlations; and (2) implementations of the alternative reversible computation paradigm can potentially avoid such losses, and thereby circumvent the Landauer limit, potentially allowing the efficiency of future digital computing technologies to continue improving indefinitely. We also outline a research plan for identifying the fundamental minimum energy dissipation of reversible computing machines as a function of speed.