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DTSTART;TZID=Europe/Berlin:20200402T100000
DTEND;TZID=Europe/Berlin:20200402T110000
DTSTAMP:20260501T210044
CREATED:20200330T102046Z
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SUMMARY:Zoom Talk: Dimitri Coelho Mollo (SCIoI): Introducing the Seminar “Philosophy of Artificial Intelligence”
DESCRIPTION:Thursday Morning Talk \nSCIoI postdoctoral researcher Dimitri Coelho Mollo will be giving us a sneak peak of his upcoming seminar for the SS2020 “Philosophy of Artificial Intelligence” and present his current work on the project “Concept of Intelligence” (PIs: Michael Pauen\, Miriam Kyselo\, John-Dylan Haynes).
URL:https://www.scienceofintelligence.de/event/thursday-morning-talk-on-zoom-dimitri-coelho-mollo-scioi-introducing-the-seminar-philosophy-of-artificial-intelligence/
LOCATION:On ZOOM (Contact us for Link)
ATTACH;FMTTYPE=image/jpeg:https://www.scienceofintelligence.de/wp-content/uploads/2020/03/Dimitri1-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20200416T100000
DTEND;TZID=Europe/Berlin:20200416T110000
DTSTAMP:20260501T210044
CREATED:20200406T064706Z
LAST-MODIFIED:20250604T100715Z
UID:7658-1587031200-1587034800@www.scienceofintelligence.de
SUMMARY:Zoom Talk: Ralf Kurvers (MPI): How To Sway Voters
DESCRIPTION:Thursday morning talk\n \nHow to sway voters \nAcross the world\, politicians challenging the established elite are rising. Presenting contrarian views with high confidence\, these politicians challenge the status quo\, calling for a radically different course of action\, often going against scientific evidence. Systematic investigations of the success of such ‘contrarian’ strategies are\, however\, absent. Though the process of advice taking is well-studied\, the equally-important process of advice-giving has received much less attention. We present a systematic investigation of the success of the ‘contrarian’ in attracting the attention of decision-makers. Using game-theoretical modeling\, we find that a contrarian strategy is a Nash equilibrium. Next\, we tested whether this contrarian strategy is successful in drawing the attention of decision-makers across seven experiments testing over 800 participants. We found that\, across all experiments\, participants are more likely to follow the ‘contrarian’ than an advisor who honestly communicates the available evidence. This was found for participants who made decisions by themselves\, in anonymous groups in which decisions were pooled using a majority vote\, as well as in communicating groups. The success of the contrarian increased the more unpredictable the environment was. Our results show that actively going against the evidence is a powerful strategy to attract followers\, and this explains the recent (and historical) success of such contrarian leaders. Especially in highly uncertain situations\, as for example the recent Corona outbreak\, such strategies are successful.
URL:https://www.scienceofintelligence.de/event/ralf-kuvers-how-to-sway-voters/
LOCATION:On ZOOM (Contact us for Link)
ATTACH;FMTTYPE=image/jpeg:https://www.scienceofintelligence.de/wp-content/uploads/2019/10/kurvers_800.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20200423T100000
DTEND;TZID=Europe/Berlin:20200423T110000
DTSTAMP:20260501T210044
CREATED:20200330T100916Z
LAST-MODIFIED:20200330T100916Z
UID:7583-1587636000-1587639600@www.scienceofintelligence.de
SUMMARY:Zoom Lecture: Fatma Deniz (UC Berkeley & TU Berlin): Natural Language Representations in the Human Brain
DESCRIPTION:Thursday Morning Lectures (on Zoom)  \nNatural Language Representations in the Human Brain \n  \nAbstract \nAn integral part of human language is the capacity to extract the meaning of words through different sensory modalities. For example\, humans can easily comprehend the meaning of language presented through auditory speech or written text. However\, how the human brain represents language in different modalities is still unclear. I suggest to observe the human brain performing tasks in its most natural setting\, then build predictive models of brain responses\, and create generalizable and reproducible results that aim to help in unifying our understanding of how the human brain processes linguistic information. In my talk\, I will present predictive models of brain responses collected using functional magnetic resonance imaging while human participants listened to or read natural narrative stories. Using natural text and vector representations derived from natural language processing methods I will first present a modeling framework to study language processing in the human brain across modalities. I will then discuss how contextual effects modulate the representation of word meaning in the human brain. I will end my talk with research directions on how different languages are represented in the brains of bilinguals and how we could use the knowledge learned from the brain to augment the design of natural language processing algorithms. \nBio:\nFatma Deniz is the team leader and co-PI of a US-German collaborative research project and holds joint affiliations between Technical University Berlin and University of California\, Berkeley. Prior to that\, she was a postdoctoral fellow in Dr. Jack Gallant’s laboratory at UC Berkeley\, a Moore-Sloan Data Science Fellow at Berkeley Institute for Data Science and a fellow at the International Computer Science Institute in Berkeley. In her work\, Dr. Deniz uses machine-learning approaches and large-scale brain data to understand how language is represented in the brain. Dr. Deniz received her Ph.D. from the Computer Science Department at Technical University Berlin and was a member of Haynes Neuroimaging Lab led by Dr. John-Dylan Haynes at BCCN Berlin. She received her bachelor’s and master’s degrees in Computer Science from the Technical University Munich and worked with Dr. Christof Koch at Caltech. Dr. Deniz is an advocate of reproducible research practices and is an editor of the book “The Practice of Reproducible Research”. \n\nPhoto by Robina Weermeijer on Unsplash
URL:https://www.scienceofintelligence.de/event/zoom-lecture-fatma-deniz-uc-berkeley-tu-berlin-natural-language-representations-in-the-human-brain/
LOCATION:On ZOOM (Contact us for Link)
ATTACH;FMTTYPE=image/jpeg:https://www.scienceofintelligence.de/wp-content/uploads/2020/03/robina-weermeijer-IHfOpAzzjHM-unsplash-scaled-1.jpg
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DTSTART;TZID=Europe/Berlin:20200423T160000
DTEND;TZID=Europe/Berlin:20200423T173000
DTSTAMP:20260501T210044
CREATED:20200421T173905Z
LAST-MODIFIED:20250604T100605Z
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SUMMARY:Martin Rolfs (SCIoI): The Impact of Visual Actions on Human Vision
DESCRIPTION:PI Lecture on Zoom \nThe impact of visual actions on human vision\nMore than 10\,000 times every waking hour\, we use rapid movements of our eyes\, head and body to reorient our gaze. These visual actions allow us to see every aspect of the visual world at the highest resolution. It seems likely — in particular within SCIoI — that we can only begin to understand perception and cognition if we study their fundamental mechanisms in active observers. Yet psychology and neuroscience have long studied vision and motor control largely independently\, presenting two success stories: Vision has been the work horse of perception research for more than a century and the brain circuits controlling gaze movements are now among the best understood in systems neuroscience.\nIt is at the intersection of these two systems\, however\, that we encounter the most intriguing questions. How do we not experience the brisk motion of the entire scene on the retina every time the eyes move? How does the visual system keep track of objects’ changing retinal locations across consecutive glances. And how do we routinely attribute retinal motion to our own movements rather than to motion in the world. To explain these phenomena\, research and theories across disciplines have focused on how the brain uses its knowledge about ongoing movement plans to predict and compensate for undesirable side effects of visual actions. I will present a number of findings from psychophysical studies that\, more often than not\, give more surprising answers and that raise new questions about the tight weaving of perception and action.\n\n 
URL:https://www.scienceofintelligence.de/event/pi-lecture-on-zoom-martin-rolfs-scioi-the-impact-of-visual-actions-on-human-vision/
LOCATION:On ZOOM (Contact us for Link)
CATEGORIES:PI Lecture
ATTACH;FMTTYPE=image/jpeg:https://www.scienceofintelligence.de/wp-content/uploads/2018/11/rolfs_800.jpg
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BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20200430T100000
DTEND;TZID=Europe/Berlin:20200430T110000
DTSTAMP:20260501T210044
CREATED:20200420T102050Z
LAST-MODIFIED:20240813T105855Z
UID:7808-1588240800-1588244400@www.scienceofintelligence.de
SUMMARY:Zoom Lecture: Oliver Brock\, Verena Hafner\, Pawel Romanczuk (SCIoI): The Role of Example Behaviors in Science of Intelligence and for Your Project
DESCRIPTION:Thursday Morning Talk (on Zoom) \nAbstract: \nOne of the assumptions made in the original SCIoI proposal is that intelligence is non-decomposable. This means that intelligent behavior results to a large degree from the interactions between component and not only from the components themselves.  Components have different names\, depending on what discipline you are from.  If you look at the study of cognition\, you might call these components “attention\, the formation of knowledge\, memory and working memory\, judgment and evaluation\, reasoning and “computation”\, problem solving and decision making\, comprehension and production of language” [Wikipedia].  To be able to study the interactions among such components\, SCIoI has proposed three Example Behaviors\, each associated with a different “type” of intelligence: \n1) Escaping from an escape room – individual intelligence\n2) Learning in social interaction – social intelligence\n3) Cooperative shepherding – collective intelligence \nThese example behaviors integrate the synthetic output of SCIoI projects\, mostly from research unit 1\, but indirectly also from RU2 and RU3.  The idea is the following: If you develop an intelligence mechanism for visual attention\, this mechanism should demonstrate its validity in the context of an integrated “intelligent” system.  Only then can we study and understand the interactions among your and other components.  We will therefore integrate your mechanism into one of the three example platforms\, one platform for each example behavior.  Within this system\, the interactions with other components are revealed and we can observe your component’s functionality within their context. \nThis presentation will trigger a SCIoI-wide\, gradual process of building the three example behaviors.  The goals of the presentation are: \n\nto briefly talk about non-decomposability\nto discuss how example behaviors fundamentally contribute to SCIoI\nto introduce the three example platforms (mobile manipulator for behavior 1\, humanoid robots for behavior 2\, and swarm robots for behavior 3)\, including sensors\, actuators\, possible interfaces and uses\nto provide you with initial information that should enable you to start thinking about the integration of your contribution into the platform(s)\nto answer your questions to plan next steps(Photo by Brett Jordan on Unsplash)
URL:https://www.scienceofintelligence.de/event/zoom-lecture-oliver-brock-verena-hafner-pawel-romanczuk-the-role-of-example-behaviors-in-science-of-intelligence-and-for-your-project/
LOCATION:On ZOOM (Contact us for Link)
ATTACH;FMTTYPE=image/jpeg:https://www.scienceofintelligence.de/wp-content/uploads/2020/04/brett-jordan-GQCYOS_MH0w-unsplash-scaled-1.jpg
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