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Key research themes
1. How can distributed and networked frameworks redefine and extend the concept of intelligence?
This research area investigates intelligence not as a property of isolated agents but as a fundamentally distributed and networked phenomenon. It emphasizes collective intelligence emerging from interactions among humans, machines, and hybrid socio-technical systems, exploring architectures and theories that model intelligence as a scalable, self-organizing network process. This matters because it challenges traditional, individual-based notions of intelligence and has implications for designing cognitive systems, socio-technical infrastructures, and artificial intelligence capable of adaptive, emergent behaviors.
Key finding: Proposes Extended Intelligence (EI) as a distributed phenomenon arising from the integration of humans, machines, and networks, exemplified by human-computer collaboration in activities like Advanced Chess. EI research shows... Read more
Key finding: Defines distributed intelligence in global-scale networks where coordination and self-organization emerge from local interactions among a multitude of autonomous agents, both human and technological. Shows that the Global... Read more
Key finding: Demonstrates that the Internet-Human symbiosis creates self-organizing social structures capable of solving complex problems beyond individual capacities. Emphasizes that problem-solving arises through distributed dynamics,... Read more
Key finding: Advocates for viewing human intelligence as network-based, driven by collaborations and relationships embedded in socio-technical contexts. Highlights that human cognition and decision-making are inseparable from dynamic... Read more
Key finding: Presents a formal psychometric network model where intelligence arises from reciprocal, mutualistic interactions among cognitive abilities rather than a single latent factor. Demonstrates how network centrality and dynamic... Read more
2. What are the neural and cognitive functional units underlying intelligence and how can they inform artificial intelligence systems?
This research area focuses on identifying the fundamental neural modules and cognitive mechanisms that support intelligence in biological systems, especially the human brain, and translating these insights into architectural models for AI. It includes explorations of neuronal circuit units, perception-imagination substrates, backpropagation loops, and how these may give rise to self-awareness or sentience. Understanding these units is critical for advancing AI systems toward more human-like cognition and potential consciousness.
Key finding: Develops a theory of 'equimerec units'—functional neuronal circuits that integrate threshold logic and feedback loops responsible for object recognition, memory retrieval, and imagination. Highlights that backpropagation... Read more
Key finding: Expands on the theory of neuron circuit functional units, emphasizing their hierarchical organization and shared neural substrates for perception and mental imagery. Demonstrates how recurrent cortical networks with... Read more
Key finding: Introduces abstract intelligence (αI) based on mathematical engineering models that formalize brain function across logical, cognitive, physiological, and neurological levels. Elaborates on how such formal models facilitate... Read more
Key finding: Synthesizes methods linking brain connectivity metrics to diverse cognitive states including memory, attention, and emotions. Underlines that understanding neural connectivity patterns offers insights into dynamic brain... Read more
Key finding: Examines cognition from philosophical, logical, computational, and educational perspectives, emphasizing symbolic and recursive processes foundational to cognitive architectures. Connects insights on human logical reasoning... Read more
3. How do cognitive dynamic systems and cyber-physical interactions contribute to connected intelligence in IoT and autonomous environments?
This theme covers the integration of natural intelligence principles and autonomous decision-making within Internet of Things (IoT) frameworks and cyber-physical systems (CPS). It investigates architectures like cognitive dynamic systems (CDS) and autonomic decision-making systems (ADMS) that process environmental sensing data to effect intelligent reasoning and action. This research is vital for designing smart environments, healthcare applications, and self-governing networks that embody connected intelligence principles.
Key finding: Proposes the design of intelligent systems based on cognitive dynamic systems (CDS) coupled with autonomic decision-making systems (ADMS) within cyber-physical system (CPS) architectures. Demonstrates how ADMS uses sensor... Read more
Key finding: Introduces the ACP-based parallel intelligence paradigm (Artificial societies, Computational experiments, Parallel execution) as a cyber-social-physical system approach that models complex system behaviors. This paradigm... Read more
Key finding: Conceptualizes intelligence as an open-ended, formative process of agent individuation emphasizing self-organization and sense-making. Applies this to general and distributed cognitive agents, highlighting the role of... Read more
Key finding: Differentiates AI agency conceptions into point (individual agent-centered) and network (relational and distributed) notions. Argues that understanding AI agency as networked interactions is essential in socio-technical... Read more
Key finding: Explores how open and distance learning environments facilitate connections among multiple intelligences, fostering collective forms of cognition. Provides a pedagogical perspective on harnessing diverse cognitive capacities... Read more