PI Lecture Series Models for Feedback -- Cost and Benefits of Simplicity Joerg Raisch, Control Systems Group, TU Berlin Abstract: We'll start with a short introduction to feedback control. I'll
PI Lecture Series Marianne Maertens: "How we perceive surfaces"
PI Lecture Series - "Spatial AI and Event-based Vision" Prof. Dr. Guillermo Gallego Robotic Interactive Perception Group, TU Berlin
On SCIoI faculty member Professor Jens Krause will open this year's lecture series with a talk on swarm intelligence: "Ob Mensch oder Tier: Warum der Schwarm intelligenter ist als der
PI Lecture Series Abstract: The biological perspective on intelligence is well represented by the following quotes: “Is it not reasonable to anticipate that our understanding of the human mind would
The concept of intelligence in cognitive science has been highly elusive. One pragmatic approach to understanding intelligence is to use classical intelligence tests, such as the Wechsler Adult Intelligence Scale
Henry Shevlin is a research associate at the Leverhulme Centre for the Future of Intelligence (Cambridge). He did his PhD at CUNY Graduate Center in New York with a thesis
Winter School Keynote Lecture: Oliver Brock (Cluster Science of Intelligence, TU Berlin) Cluster Speaker Prof. Dr. Oliver Brock will deliver the keynote lecture at this year's Winter School Ethics and
Thursday Morning Lectures Abstract: Generalization and adaptation of learned skills to novel situations is a core requirement for intelligent autonomous robots. Although contextual reinforcement learning provides a principled framework for
PI Lecture on Zoom The impact of visual actions on human vision More than 10,000 times every waking hour, we use rapid movements of our eyes, head and body to
Abstract: Learning - here defined as knowledge acquisition and behavioral changes caused by experiences - is a central prerequisite for the development of humans, animals, and some artificial agents. Against
Reward-based Learning and Decision Making under Risk Reinforcement learning provides a framework for making agents learn policies through feedback signals (“rewards”), which provide information about whether their actions or action
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