At our cluster science slam, scientists try everything to entertain their audience, regardless of whether the subject is e.g. mathematics, neuroscience or active material. The sky is the limit when it comes to what’s possible. Costumes, props, movies, power-point presentations or other experimental setups – it is all allowed. Only time sets the limits –
Abstract: As many of us know by now, Deep Learning has enabled tackling very challenging problems and applications that were previously almost impossible to solve with machine learning. However, for most of the tasks we want to solve with Deep Learning, we need large, if not huge, amounts of data and computing power. This is
Abstract: The BUA-funded experimental knowledge transfer project CollActive Materials, a collaboration between the Clusters of Excellence Science of Intelligence and Matters of Activity, encourages speculation on what the future has in store. Which intelligent materials will pave our tomorrows? How can substances and materials change our world in an intelligent way? What will the world
Abstract: The contents of this presentation will be twofold. In the first part the Centre for Robotics of the Queensland University of Technology (QUT) and its research directions and facilities will be introduced. The research on semantics for the benefit of UAVs, specifically quadcopters, will be highlighted. The second part will contain the personal experiences
Abstract: Learning is a central concept in many scientific disciplines. Communication about research on learning is, however, hampered by the fact that different researchers define learning in different ways. In this talk, we introduce the extended functional definition of learning that can be used across scientific disciplines. We provide examples of how the definition can
Abstract: The aim of SCIoI’s P10 is to investigate how anticipation and prediction shapes social interactions among live and artificial agents using for example the Robofish system. We will outline our research showing the sophisticated anticipation abilities of live fish, as well as how we integrated prediction and anticipation into Robofish’s social interaction behaviors. We
Abstract: The brain is a complex dynamical system with processes operating on different spatial scales. At the macroscopic end one observes global dynamical phenomena, which are called „brain states“ and which are often acompanied by oscillations in different frequency bands or by specific functional connectivity patterns between populations of neuron. A common hypothesis states, that
Abstract: Lack of data and annotations has been the showstopper for machine learning projects when I started my PhD and 15 years later it still is. In my talk, I will give a brief overview of recent models we developed for weakly- and semi supervised as well as for active learning. In addition, we will
Abstract: A central challenge in understanding the concept of swarm intelligence is the relation between the behavior of a swarm of agents and its ecological niche. In order to interpret such collective concept, we have been using analytical and synthetic approaches to get more insights using mainly one particular biological system of Sulphur mollies as
Many claims have been made that machine intelligence could make humans superfluous in the near future. Today this claim is largely seen as overstated, but it is still important to assess the relative strengths of human versus machine cognition. This talk will take place in person at SCIoI.
Abstract: We can view cortex from two fundamentally different perspectives: a powerful device for performing optimal inference, or an assembly of biological components not built for achieving statistical optimality. The former approach is attractive thanks to its elegance and potentially wide applicability, however the basic facts of human pattern vision do not support it. Instead,
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
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