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DTSTART;TZID=Europe/Berlin:20250911T100000
DTEND;TZID=Europe/Berlin:20250911T110000
DTSTAMP:20260408T073805
CREATED:20250526T094651Z
LAST-MODIFIED:20250902T080023Z
UID:25080-1757584800-1757588400@www.scienceofintelligence.de
SUMMARY:Asieh Daneshi (Science of Intelligence)\, “Is risky behavior contagious?”
DESCRIPTION:More details to follow. \nImage created with DALL-E by Maria Ott.
URL:https://www.scienceofintelligence.de/event/asieh-daneshi-behavioral-contagion-in-human-and-artificial-multi-agent-systems/
CATEGORIES:Thursday Morning Talk
ATTACH;FMTTYPE=image/jpeg:https://www.scienceofintelligence.de/wp-content/uploads/2025/02/chatgtp2.jpg
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DTSTART;TZID=Europe/Berlin:20250925T100000
DTEND;TZID=Europe/Berlin:20250925T230000
DTSTAMP:20260408T073805
CREATED:20250912T140413Z
LAST-MODIFIED:20250922T204359Z
UID:26786-1758794400-1758841200@www.scienceofintelligence.de
SUMMARY:Simon Vock (Charité Universitätsmedizin)\, "Critical dynamics governs deep learning"
DESCRIPTION:Artificial 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 and neuroscience\, we demonstrate that criticality provides a unifying principle linking structure\, dynamics\, and function in DNNs. Analyzing more than 80 state-of-the-art models\, we first report that improvements in accuracy over the past decade coincided with an implicit evolution toward more critical dynamics. Architectural and training innovations unknowingly guided networks toward this optimal regime. Second\, building on these insights\, we develop a training method that explicitly drives networks to criticality\, improving robustness and performance. Third\, we show that fundamental problems in AI\, including loss of performance in deep continual learning\, are caused by loss of criticality and that maintaining criticality rescues performance. This work introduces criticality as a fundamental framework for AI development by emphasizing dynamic optimization alongside scale. It bridges artificial intelligence with physics and biological cortical network function inspiring novel self-tuning strategies in DNNs. The findings offer a theoretically grounded path forward in designing efficient\, adaptable\, and high-performing artificial intelligence systems drawing inspiration from principles observed in biological neural systems. \nImage generated with DALLE by Maria Ott.
URL:https://www.scienceofintelligence.de/event/simon-vock-charite-universitatsmedizin/
LOCATION:Marchstraße 23\, 10587 Berlin\, Room 2.057
CATEGORIES:Thursday Morning Talk
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