4,788 research outputs found
From human to artificial cognition and back: New perspectives on cognitively inspired AI systems
We overview the main historical and technological elements characterising the
rise, the fall and the recent renaissance of the cognitive approaches to Artificial
Intelligence and provide some insights and suggestions about the future directions
and challenges that, in our opinion, this discipline needs to face in the
next years
Designing as Construction of Representations: A Dynamic Viewpoint in Cognitive Design Research
This article presents a cognitively oriented viewpoint on design. It focuses
on cognitive, dynamic aspects of real design, i.e., the actual cognitive
activity implemented by designers during their work on professional design
projects. Rather than conceiving de-signing as problem solving - Simon's
symbolic information processing (SIP) approach - or as a reflective practice or
some other form of situated activity - the situativity (SIT) approach - we
consider that, from a cognitive viewpoint, designing is most appropriately
characterised as a construction of representations. After a critical discussion
of the SIP and SIT approaches to design, we present our view-point. This
presentation concerns the evolving nature of representations regarding levels
of abstraction and degrees of precision, the function of external
representations, and specific qualities of representation in collective design.
Designing is described at three levels: the organisation of the activity, its
strategies, and its design-representation construction activities (different
ways to generate, trans-form, and evaluate representations). Even if we adopt a
"generic design" stance, we claim that design can take different forms
depending on the nature of the artefact, and we propose some candidates for
dimensions that allow a distinction to be made between these forms of design.
We discuss the potential specificity of HCI design, and the lack of cognitive
design research occupied with the quality of design. We close our discussion of
representational structures and activities by an outline of some directions
regarding their functional linkages
Autopoiesis of the artificial: from systems to cognition
In the seminal work on autopoiesis by Varela, Maturana, and Uribe, they start by addressing the confusion between processes that are history dependent and processes that are history independent in the biological world. The former is particularly linked to evolution and ontogenesis, while the latter pertains to the organizational features of biological individuals. Varela, Maturana, and Uribe reject this framework and propose their original theory of autopoietic organization, which emphasizes the strong complementarity of temporal and non-temporal phenomena. They argue that the dichotomy between structure and organization lies at the core of the unity of living systems. By opposing history-dependent and history-independent processes, methodological challenges arise in explaining phenomena related to living systems and cognition. Consequently, Maturana and Varela reject this approach in defining autopoietic organization. I argue, however, that this relationship presents an issue that can be found in recent developments of the science of artificial intelligence (AI) in different ways, giving rise to related concerns. While highly capable AI systems exist that can perform cognitive tasks, their internal workings and the specific contributions of their components to the overall system behavior, understood as a unified whole, remain largely uninterpretable. This article explores the connection between biological systems, cognition, and recent developments in AI systems that could potentially be linked to autopoiesis and related concepts such as autonomy and organization. The aim is to assess the advantages and disadvantages of employing autopoiesis in the synthetic (artificial) explanation for biological cognitive systems and to determine if and how the notion of autopoiesis can still be fruitful in this perspective
Recommended from our members
Towards integrated neural-symbolic systems for human-level AI: Two research programs helping to bridge the gaps
After a human-level AI-oriented overview of the status quo in neural-symbolic integration, two research programs aiming at overcoming long-standing challenges in the field are suggested to the community: The first program targets a better understanding of foundational differences and relationships on the level of computational complexity between symbolic and subsymbolic computation and representation, potentially providing explanations for the empirical differences between the paradigms in application scenarios and a foothold for subsequent attempts at overcoming these. The second program suggests a new approach and computational architecture for the cognitively-inspired anchoring of an agent's learning, knowledge formation, and higher reasoning abilities in real-world interactions through a closed neural-symbolic acting/sensing-processing-reasoning cycle, potentially providing new foundations for future agent architectures, multi-agent systems, robotics, and cognitive systems and facilitating a deeper understanding of the development and interaction in human-technological settings
- …