Thursday Morning Talk

Abstract:  In groups of agents learning how to solve a common task, interaction and knowledge transfer between agents is important and can vary depending on network topology. Heterogeneity is one of the key principles that influences the type and quality of interaction between learning agents. Different learning strategies and behaviors can be a driving factor

Abstract: Swarm robotics is a promising approach to the coordination of large groups of robots. Traditionally, the design of collective behaviors for robot swarms has been an iterative manual process: a human designer manually refines the control software of the individual robots until the desired collective behavior emerges. In this talk, I discuss automatic design

Abstract: From writing essays to evaluating potential hires, machines are doing a lot these days. In all spheres of life, it seems that machines are being delegated more and more decisions. Some of these machines are being delegated decisions that could have significant impact on human lives.Examples of such machines which have caused such impact

Abstract: I investigate how large groups of simple robots can reach a consensus with decentralized minimalistic algorithms. Simple robots can be useful in nanorobotics and in scenarios with low-cost requirements. I show that through decentralized voting algorithms, swarms of minimalistic robots can make best-of-n decisions. In my research, I show that using a biologically-inspired voting

Abstract: Biological intelligent systems manifest their intelligence in physical interactions with other agents and with their environment. Such interactions require embodiment. Intelligence, both artificial and biological, also requires some kind of learning. But what is the relationship between the two? How should the two interact? Do they even have to? What could be a common

Abstract:  This talk will be targeting junior postdocs and phd at their final stages. It will be a short and brief introduction to the major options for grants (those aiming at the stars or smaller ones). Julten will offer some quick tips on the application process. She will also share her own experience in applying

Abstract: The capabilities of AI systems are improving rapidly, and these systems are being deployed in increasingly complex and high-stakes contexts, from self-driving cars to the detection of medical conditions. As the importance of AI grows, so too does the need for robust evaluation. If we want to determine the extent to which systems are

Abstract: The classic view of nature is one of a deathly struggle for existence. Yet, throughout nature, organisms cooperate with each other. Mutualisms – mutually beneficial interactions between species - are more than fascinating natural history stories: they are central to the diversity and the diversification of life on Earth. Charles Darwin, well aware of

Abstract: In this talk, Dr. Jung will focus on the three key principles of good time management: defining priorities, managing expectations and developing routines that work. Following the lecture, the participants have the opportunity to discuss their time management challenges in an individual coaching session. Defining Priorities: Dealing with high performance expectations in wide array

Abstract: The recent success in Computer Vision has been mostly attributed to improved results using deep learning models trained on large labeled datasets. Many of these datasets have been labeled by humans. The labeling process, however, can be time-consuming, and in many applications, it may require expertise that could be costly to acquire. In order

Abstract: Programming robots to manipulate articulated objects such as drawers, doors, or locks is a challenging task. One of the major reasons for this difficulty is that robots must physically interact with objects, and even minor errors during manipulation can result in significant internal forces that may cause damage. While robots struggle with these manipulation

Abstract: It has recently been proposed that a minimal condition of intelligence is the ability to form accurate predictions (Tjøstheim & Stephens 2021). In this talk, I evaluate the promise of this view for integrating intelligence research across subdisciplines within the cognitive and life sciences. I argue that this view combines two desirable features: (1)