Decision making strategies in the face of conflicting or uncertain sensory input have been successfully described in many different species. Here we analyze large behavioral datasets of larval zebrafish engaged in a ‘coherent dot’ optomotor assay. We find that animal performance is bimodal and can be separated into two ‘states’, an engaged state where performance
Scalability is often lauded as one of the advantages of decentralized multi-robot systems and robot swarms. Theory and many experimental works predict that with increasing swarm density, we will observe a gradual decay of performance. In our work, we have taken a closer look at the scalability of robot swarms in different settings and we
What can AI systems do? Answering this question requires us to model their capabilities, but this first demands a clear conception of what capabilities are and which tools we can use to measure them. I advance a dispositional account of capabilities, understanding them as a system’s propensity to behave in certain ways under certain conditions.
Far-reaching perspectives in swarm robotics consider robots that are minimalist in their sensing, communication and computation, but are deployed in thousands to collaborate towards the accomplishment of tasks distributed in space and time. Generally speaking, future robot swarms might face harsh operating conditions where little communication is possible and no external infrastructure is available. These
This event brings together a wide range of speakers to discuss the challenges and opportunities in Embodied Intelligence research. The conference is structured with a morning session and afternoon session each day to accommodate different time zones. Each session includes plenary talks, panel discussions (including flash talks by leading researchers), and breakout sessions. The conference
What is intelligence, and how can research contribute to improving current technologies with insights from this work? At the Girls' Day with the Excellence Cluster Science of Intelligence, you'll find out how scientists from various disciplines research the topic of intelligence. Through interactive exchanges, you can learn how researchers work to understand intelligence from different
Dive into the wonderful world of research of the seven Berlin Clusters of Excellence: from literature to chemistry, from politics to AI, you and your team can find answers for exciting and surprising questions from the clusters’ research areas. So seek fellows and think of your team name! Every quiz evening focuses on the research
Science often deals with issues pertaining to possibilities, contingencies and necessities, by engaging in thought experiments and modeling. This talk discusses how much deep learning can be helpful for navigating the possibility space for intelligence, adding to our scientific understanding of possibilities. One epistemically useful feature of neural networks is their runnability - they can
More details to follow. Photo created with DALL-E by Maria Ott.
Foraging complexity and competitive social challenges are considered key drivers of human cognition. Yet, we still have a poor understanding of the decision-making mechanisms underlying foraging behavior, especially in social contexts. In this talk, I will combine immersive lab experiments, field work using high-resolution tracking, and computational and agent-based models to uncover the mechanisms guiding
The human brain extracts complex information from visual inputs, including objects, their spatial and semantic interrelations, and their interactions with the environment. However, a quantitative approach to capture this information remains elusive. I will present work where we show that LLM embeddings of scene captions successfully characterise brain activity evoked by viewing the natural scenes.
From the daily movement of primate troops to the mesmerizing murmurations of starling flocks in the sky, the dynamics of animal groups on the move fascinate us with the mystery of their underlying social interactions. In this talk, I will first showcase how we combine empirical data and computational models based on self-organization to understand
Marchstr. 23 – MAR 5-2
10587 Berlin
Email: info@scioi.de
Tel: +49 30 314 77344
