PI Lecture

Klaus Obermayer (SCIoI): Reward-based Learning and Decision Making under Risk

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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 sequences were successful or not. Reinforcement learning also provides a framework for understanding how humans learn and decide given reward information only. Standard reinforcement learning

Thursday Morning Talk

Manuel Lopes (hosted by Marc Toussaint): Optimal Behavior Without Optimal Rewards : Artificial Vs Natural

On ZOOM (Contact communication@scioi.de for link)

Abstract: Research in robotics and A.I. aims at optimizing very specific task rewards. Intelligent animals have a high degree of curiosity, and recent results have shown that instrumental reward optimization is a poor explanation for their behavior. We can show that to explain empirical results from animals, we need to have the drive to optimize

PI Lecture

Oliver Brock (SCIoI): Genesis, Goals, and Gossip of SCIoI

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Abstract: I would like to give a personal perspective of the scientific motivation and framing of SCIoI and relate them to the research of my lab, the Robotics and Biology Laboratory. But at the same time, I would like to critically question and discuss all of these things, in an attempt to move towards a

Thursday Morning Lectures: Dr Utku Culha (Max Planck Inst.): Physical Intelligence on Soft Robots: order, functionality, and adaptation from the bottom-up

On ZOOM (Contact communication@scioi.de for link)

Abstract: We typically have a clear idea about the final design and functionality of a robot before we start building it. We apply this top-down design approach to a wide range of robotic systems and it allows our robots to be more optimized, autonomous, and programmable. However, if we want to design and actuate multiple

Distinguished Speaker Series

Jacqueline Gottlieb (hosted by Martin Rolfs): Curiosity and information demand: how we can study them and why we should care

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Curiosity and information demand: how we can study them and why we should care A rapidly growing literature has recently emphasized the importance of our sense-making instincts, including complex investigative behaviors such as curiosity, for behavior and brain function. While much of this literature has focused on simple forms of decision making, we explored its

PI Lecture: Thorsten Pachur (SCIoI): Ecologically Rational Decision Making

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Ecologically rational decision making How do we make inferences about a world full of uncertainty and given the mind’s natural bounds in computational abilities? I present a perspective according to which the decision maker is equipped with a repertoire of strategies, containing both simple heuristics and more complex strategies that are adaptive under different ecological

Thursday Morning Talk: Leon Sixt (Biorobotics Lab, FU Berlin): Opportunities and Challenges in Interpetable ML

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Abstract: Deep neural networks underlie many state of the art solutions to hard problems in computer vision, natural language processing or playing Go. Yet, their power comes with a price. Deep networks transform inputs gradually into outputs, using many parameters and intermediary activations. Understanding what a network has learned, how inputs are mapped to outputs,

Work-in-progress Talk: Dimitri Coelho Mollo (SCIoI) & Alfredo Vernazzani (Ruhr Universität Bochum): Saving Representational Formats: A computational theory

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Abstract Most cognitive sciences (including AI) appeal to representations in explaining (or trying to create) systems capable of cognition, and especially of complex, intelligent behaviour. In philosophy, considerable attention has been dedicated to the problem of explaining in scientifically-acceptable ways how representations come to represent what they do. In contrast, there has been much less

SCIoI Scientific Networking Days (internal event)

Internal Event

The SCIoI Scientific Networking Days event is an internal retreat meant to establish scientific connections among the people and the projects in SCIoI. Through this event, we want to emphasize the importance of establishing scientific connections among SCIoI researchers and promote in-depth scientific discussions and exchanges. During the retreat, the SCIoI executive board will evaluate

Thursday Morning Talk with Rico Jonschkowski (Google Brain): Perception in Motion

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Title: Perception in Motion Abstract: This is a talk on perception in two parts. Part one exemplifies the "movement" of the field of learning-based robot perception. Here, I will give one example for increasing structural assumptions and one for decreasing them based on our work on differentiable mapping and differentiable resampling. Part two takes the

Thursday morning talk with Benjamin Wild: Social networks through time – Individuality in a colony of honey bees

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ABSTRACT: In many social systems, an individual’s role is reflected by its interactions with other members of the group. In many model organisms, and particularly in social insects, the patterns of actions and interactions among individuals are not static but constantly evolving over time. This can be due to the emergence or demise of certain