David Bierbach & Yunus Sevinchan (Science of Intelligence), “Self-Organised Criticality in Animal Collectives”
SCIoI, Marchstraße 23, 10587 Berlin, Room 2.057More details to follow. Photo by lance Anderson on Unsplash.
12 events found.
Thursday Morning Talk
Anne Jaap, Friedrich Schüßler, and Paul Mieske (Science of Intelligence): “Big mouse data: Characterizing mouse behavior through temporal statistics”
SCIoI, Marchstraße 23, 10587 Berlin, Room 2.057The study of animal behavior is rapidly changing due to recent advances in long-term recording and automated analysis. Here we use these new developments to characterize mouse behavior via their temporal statistics. We analyzed positional data (RFID detections) of groups of mice housed in complex environments over many months. We found that behavior spanning seconds
Palina Bartashevich and David Bierbach (Science of Intelligence), “Collective Air Breathing In the Largest Freshwater Fish on Earth”
SCIoI, Marchstraße 23, 10587 Berlin, Room 2.057More details to follow. Photo by David Clode on Unsplash.
Max Ploner (Science of Intelligence), “Evaluating Sample Efficiency: How Language Models Learn to Recall Facts from Data”
SCIoI, Marchstraße 23, 10587 Berlin, Room 2.057More details to follow. Image created with DALL-E by Maria Ott.
Raina Zakir (Université Libre De Bruxelles), “Robust Decision-Making in Minimalistic Robot Swarms Under Social Noise”
SCIoI, Marchstraße 23, 10587 Berlin, Room 2.057Abstract Minimalistic robot swarms hold great promise for applications in healthcare, disaster response, and environmental monitoring. A key challenge lies in enabling these robots to rapidly and reliably reach consensus using limited communication, computation, and memory. In this talk, we explore how robot swarms can collectively identify the best among multiple discrete options in their
Matthias Nau (Vrije Universiteit Amsterdam), “Revealing General Principles Underlying Active Vision and Memory”
SCIoI, Marchstraße 23, 10587 Berlin, Room 2.057Abstract: Cognitive neuroscience seeks theories that jointly explain behavioral, neural, and mental states. The dominant approach is to use specialized tasks designed to optimally probe a concept of interest (e.g., episodic memory), and to disentangle behavioral, sensory, and mnemonic factors through design (e.g., by constraining gaze during image recognition). I will present an alternative framework
POSTPONED: Alican Mertan (University of Vermont), “Morphological Cognition: Evolving Robots Exhibiting Cognitive Behavior without Abstract Controllers”
SCIoI, Marchstraße 23, 10587 Berlin, Room 2.057With the rise of modern deep learning, neural networks have become an essential part of virtually every artificial intelligence system, making it difficult to imagine different models for intelligent behavior. In contrast, nature provides us with many different mechanisms for intelligent behavior, most of which we have yet to utilize. One such underinvestigated aspect of
Asieh Daneshi (Science of Intelligence), “Is risky behavior contagious?”
More details to follow. Image created with DALL-E by Maria Ott.
Simon Vock (Charité Universitätsmedizin), “Critical dynamics governs deep learning”
Marchstraße 23, 10587 Berlin, Room 2.057Artificial intelligence has advanced rapidly through larger and deeper neural networks, yet fundamental questions remain about how to optimize network dynamics for performance and adaptability. This study shows that deep neural networks (DNNs), like biological brains, perform optimally when operating near a critical phase transition - poised between active and inactive dynamics. Drawing from physics
Anna Lange and Helene Ackermann (Science of Intelligence), “Adaptivity in Learner-Teacher Interaction”
SCIoI, Marchstraße 23, 10587 Berlin, Room 2.057More details to follow.
Félicie Dhellemmes and Valerii Chirkov (Science of Intelligence), “Navigating the Explore-Exploit Trade Off in Collective Search”
SCIoI, Marchstraße 23, 10587 Berlin, Room 2.057More details to follow. Photo by Prince Patel on Unsplash.
Dimitri Coelho Mollo (Umeå University), “Functional Ontologies for AI systems: Tasks, Mechanisms, and Capacities”
SCIoI, Marchstraße 23, 10587 Berlin, Room 2.057Abstract The size and complexity of current Deep Artificial Neural Networks pose remarkable challenges to our attempts of explaining and understanding their workings. In this talk, I put forward a proposal for complementing existing efforts to that aim, inspired by research on cognitive ontology in philosophy of cognitive science. In particular, I suggest that, as
Marchstr. 23 – MAR 5-2
10587 Berlin
Email: info@scioi.de
Tel: +49 30 314 77344
