As part of the events linked to the exhibition Nach der Natur at the Humboldt Forum, on 25 November at 7pm cluster members Jens Krause, Lea Musiolek, and Pawel Romanczuk will take part in an exciting discussion on the meaning, the study, and the challenges of collective intelligence, bringing great examples and fascinating materials. How
Abstract: Models of vision have come far in the past 10 years. Deep neural networks can recognise objects with near-human accuracy, and predict brain activity in high-level visual regions. However, most networks require supervised training using ground-truth labels for millions of images, whereas brains must somehow learn from sensory experience alone. We have been using
Breaking new ground: innovation in birds, primates and human infants Novel behaviors that suddenly appear either as a solution to a new problem or as an alternative way to solve an existing problem allow humans and animals to deal with environmental challenges and to create new opportunities. They are thus key ingredients for developing advanced problem-solving abilities. In order
Abstract: Choice architecture suggests that much of what we decide is influenced by that options are presented. This means that the choice environment can encode intelligence that will help (or can hurt) the decision maker. The talk will start by reviewing some results from choice architecture and describe how the environment can affect choice through
Postponed to a later date (to be assessed) More info coming soon!
Abstract: Like most of our technologies today, AI dramatically increases the world's carbon footprint, thereby strengthening the severity of the coming downfall of life on the planet. In this talk, I propose that recent advances in large dimensional mathematics, and especially random matrices, could help AI engage in the future economic growth. This being said,
Abstract: Bees have a diverse instinctual repertoire that exceeds in complexity that of most vertebrates. This repertoire allows the social organisation of such feats as the construction of precisely hexagonal honeycombs, an exact climate control system inside their home, the provision of the hive with commodities that must be harvested over a large territory (nectar,
Abstract: Some of my past and current research looks at "decisions from experience,” i.e., decisions based on the personally experienced outcomes of past choices, along the lines of reinforcement learning models and how such learning and updating is related to and differs from the way in which people and other intelligent agents use other sources of information,
Estimates from large-scale replication projects in psychology suggest that the majority of studies from top journals do not replicate. Using commonly accepted research methods, several academic fields amassed prolific, seemingly coherent literatures on phenomena that do not exist, such as extrasensory perception and depression candidate genes. Throughout the biomedical and life sciences, data detectives keep finding highly cited
What's on a mouse's mind? Behavioral measures to understand experiences and needs of an animal Lars Lewejohann, Freie Universität Berlin, German Federal Institute for Risk Assessment (BfR), German Centre for the Protection of Laboratory Animals (Bf3R) Mice, as all other living creatures, have adapted to specific living conditions in the course of evolution. From a
Abstract: The fields of neuroscience and AI have a long and intertwined history. From the study of simple and complex cells in visual areas of the brain to the recent success of convolution neural networks in many real-world applications, experimental and theoretical neuroscience has contributed significantly to designing smarter machines. In turn, AI models help us better understand
"Harnessing machine learning to model biological systems" Abstract: Classically, models of biological systems follow two different approaches. In bottom-up approaches, biological data are used to constrain a phenomenological model of the system in question, and the model is the studied to identify potential functions or potential consequences of the observations that flow into the model.
Marchstr. 23 – MAR 5-2
10587 Berlin
Email: info@scioi.de
Tel: +49 30 314 70660
