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X-WR-CALDESC:Events for scienceofintelligence.de
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DTSTART;TZID=Europe/Berlin:20220106T100000
DTEND;TZID=Europe/Berlin:20220106T110000
DTSTAMP:20260430T123803
CREATED:20211222T105550Z
LAST-MODIFIED:20250604T092730Z
UID:11457-1641463200-1641466800@www.scienceofintelligence.de
SUMMARY:Ruben Arslan (MPI Berlin): “Bad Science vs. Open Science. The Replication Crisis and Possible Ways Out.”
DESCRIPTION: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 papers that are riddled with errors invalidating their conclusions. Our textbooks are full of findings that do not replicate or are otherwise in serious doubt.\nAcademia as a system has issues\, but can we use the scientific method to understand and remedy them? A vibrant reform movement is seeking to do so\, but it is hard to keep track of all the suggestions to do better and tell fads from truly beneficial reforms. I outline concrete plans and paths that could lead to lasting improvements\, such as PCI Registered Reports\, the Peer Reviewer’s Openness Initiative\, post publication peer review\, and guideline and incentive setting at the journal\, hiring and funding level.\n \n  \nThe Zoom Link will be sent the day before the lecture.
URL:https://www.scienceofintelligence.de/event/thursday-morning-talk-ruben-arslan-mpi-berlin-personal-and-social-information-search-and-integration-for-intelligent-decisions-on-climate-action/
LOCATION:On Zoom
CATEGORIES:Thursday Morning Talk
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BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20220106T160000
DTEND;TZID=Europe/Berlin:20220106T173000
DTSTAMP:20260430T123803
CREATED:20211213T101206Z
LAST-MODIFIED:20250604T092718Z
UID:11359-1641484800-1641490200@www.scienceofintelligence.de
SUMMARY:Lars Lewejohann (Science of Intelligence)\, “What’s on a Mouse’s Mind? Behavioral Measures To Understand Experiences and Needs of an Animal”
DESCRIPTION:What’s on a mouse’s mind? Behavioral measures to understand experiences and needs of an animal\nLars Lewejohann\, Freie Universität Berlin\, German Federal Institute for Risk Assessment (BfR)\, German Centre for the Protection of Laboratory Animals (Bf3R) \nMice\, as all other living creatures\, have adapted to specific living conditions in the course of evolution. From a human point of view\, the behavior of animals is therefore not always easy to understand. This applies not only to the question of whether mice are actually capable of behaving intelligently\, but also to the question of what is necessary for optimizing animal welfare of laboratory animals. In our work\, we are interested in both questions and follow an animal-centered approach and are giving mice their say. Of course mice cannot fill out questionnaires\, but we have developed a series of behavioral tests that allow to query the animals. In this lecture I will outline our approach with regard to improving housing and living conditions as well as the implications of using mice as a model species for the science of intelligence. \nThe Zoom Link will be sent the day before the lecture.
URL:https://www.scienceofintelligence.de/event/pi-lecture-with-lars-lewejohann-3/
CATEGORIES:PI Lecture
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20220120T100000
DTEND;TZID=Europe/Berlin:20220120T110000
DTSTAMP:20260430T123803
CREATED:20211221T061620Z
LAST-MODIFIED:20250604T092659Z
UID:11447-1642672800-1642676400@www.scienceofintelligence.de
SUMMARY:Mengmi Zhang (Harvard Medical School)\, “A Peek Into How Brain Computations Inspire New Paths in AI and How AI Elucidate Brain Computations”
DESCRIPTION:Abstract: \nThe 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 brain computations that underlie biological intelligence. In my talk\, I will present several efforts of deciphering brain computations by building computational models and quantifying model behaviors with human benchmarks in visual search and object recognition. Specifically\, I divide my presentation into two parts. First\, I will present works on predicting eye movement behaviors during visual search tasks.  An intriguing property of some classical search tasks is asymmetry such that finding a target A among distractors B can be easier than finding B among A.  We elucidate the mechanisms responsible for asymmetry in visual search. Second\, I will introduce two works on contextual reasoning in object recognition. We systematically investigated critical properties of where\, when\, and how context modulates recognition in humans and machines. \nPlease refer to the following list of papers for details. \nCVPR 2020: https://arxiv.org/pdf/1911.07349.pdf \nICCV 2021: https://arxiv.org/pdf/2104.02215.pdf \nNeurips 2021: https://arxiv.org/pdf/2106.02953.pdf \nNature Communications 2018: https://www.nature.com/articles/s41467-018-06217-x.pdf \nBio:\nMengmi Zhang is a research scientist and principal investigator in  Agency for Science\, Technology and Research (A*STAR)\, Singapore. Prior to this\, Dr. Zhang is a postdoc with Gabriel Kreiman at the Harvard Medical School from 2019-2021. She obtained her PhD at the National University of Singapore (2015-2019) and was a visiting graduate student in KreimanLab at the Harvard Medical School (2017-2018). Her research background is multi-disciplinary at the intersection of artificial intelligence and computational neuroscience. She has published multiple papers in top-tier conferences (such as CVPR\, ICCV\, IROS and NeurIPS) and international science journals (TPAMI\, Nature Communications\, Nature Human Behaviors). Her papers often come with humorous titles like “Finding any Waldo with zero-shot invariant and efficient visual search” or “When Pigs Fly”. She will become an adjunct assistant professor in the Department of Electrical and Computer Engineering\, National University of Singapore from August 2022. \n  \nThe Zoom Link will be sent the day before the lecture.
URL:https://www.scienceofintelligence.de/event/thursday-morning-talk-with-mengmi-zhang/
LOCATION:On Zoom
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BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20220120T160000
DTEND;TZID=Europe/Berlin:20220120T173000
DTSTAMP:20260430T123803
CREATED:20211213T101428Z
LAST-MODIFIED:20250604T092649Z
UID:11363-1642694400-1642699800@www.scienceofintelligence.de
SUMMARY:Henning Sprekeler (Science of Intelligence)\, “Harnessing Machine Learning To Model Biological Systems”
DESCRIPTION:“Harnessing machine learning to model biological systems” \nAbstract:\nClassically\, 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. Top-down approaches\, on the other hand\, start with a presumed function and ask how this question could be implemented in a biologically inspired architecture. Both approaches have been very successful\, but both suffer from their own kind of problems. Bottom-up approaches often suffer from (potentially many) parameters that cannot be sufficiently constrained from data. Top-down approaches were in the past hard to combine with the complexities of the biological system in question. Recent advances in machine learning (ML) software now offer a promising hybrid approach\, because they allow to optimize not only neural nets\, but basically any dynamical system by gradient descent. I will offer a few examples how we have used ML to study biological systems\, ranging from behavioral level (nature vs. nurture) down to the level of neural circuits (role of feedback for invariant sensory processing\, and\, time permitting\, the function of different cell classes). \nThe Zoom Link will be sent the day before the lecture.
URL:https://www.scienceofintelligence.de/event/pi-lecture-with-henning-sprekeler/
CATEGORIES:PI Lecture
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20220127T100000
DTEND;TZID=Europe/Berlin:20220127T110000
DTSTAMP:20260430T123803
CREATED:20211221T062119Z
LAST-MODIFIED:20240813T100506Z
UID:11451-1643277600-1643281200@www.scienceofintelligence.de
SUMMARY:Dimitri Coelho Mollo (Science of Intelligence)\, "The Concept of Intelligence - A progress report"
DESCRIPTION:In this presentation\, I will report on the results of my work so far on the concept of intelligence\, summarising some of the main points and proposals made\, and opening the floor for open discussion about the topic. \n  \nThe Zoom Link will be sent the day before the lecture.
URL:https://www.scienceofintelligence.de/event/thursday-morning-talk-with-dimitri-coelho-mollo-scioi-the-concept-of-intelligence-a-progress-report/
CATEGORIES:Thursday Morning Talk
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