BEGIN:VCALENDAR VERSION:2.0 PRODID:-//ARNI - ECPv6.15.18//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-ORIGINAL-URL:https://arni-institute.org X-WR-CALDESC:Events for ARNI REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20250309T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20251102T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20260308T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20261101T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20270314T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20271107T060000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=America/New_York:20260320T113000 DTEND;TZID=America/New_York:20260320T130000 DTSTAMP:20260404T140450 CREATED:20260217T161957Z LAST-MODIFIED:20260217T161957Z UID:2376-1774006200-1774011600@arni-institute.org SUMMARY:CTN: Farzaneh Najafi DESCRIPTION: URL:https://arni-institute.org/event/ctn-farzaneh-najafi/ LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20260323T150000 DTEND;TZID=America/New_York:20260323T160000 DTSTAMP:20260404T140450 CREATED:20260303T195824Z LAST-MODIFIED:20260303T195824Z UID:2404-1774278000-1774281600@arni-institute.org SUMMARY:Speaker: Katherine Xu - Language and Vision Working Group DESCRIPTION:Title: Are Vision-Language Models Checking or Looking?\n\nAbstract:\nToday’s AI vision systems are trained on vast amounts of data\, yet it remains unclear whether they simply retrieve memorized answers or actively reason. We conjecture that hallucinations and limited creativity in these models stem from an over-reliance on superficial “checking” rather than active “looking.” Checking retrieves the most probable memorized association\, which often fails when novel inputs mismatch stored patterns. In contrast\, looking involves reasoning on the fly by iteratively sampling information\, revising interpretations\, and integrating evidence across modalities. First\, I will share our recent work on Vibe Spaces for creatively connecting visual concepts. Second\, I will propose visual humor as a lens to probe these cross-modal reasoning deficits. I will conclude with early findings from my ongoing research to open a discussion on potential collaborative directions for our working group.\n\nZoom: upon request@ arni@columbia.edu URL:https://arni-institute.org/event/speaker-katherine-xu-language-and-vision-working-group/ LOCATION:Virtual END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20260324T150000 DTEND;TZID=America/New_York:20260324T170000 DTSTAMP:20260404T140450 CREATED:20260224T152401Z LAST-MODIFIED:20260224T152401Z UID:2397-1774364400-1774371600@arni-institute.org SUMMARY:Speaker: Vijay Balasubramanian ARNI WG Multi-resource-cost optimization of neural network models DESCRIPTION:Title and Abstract: TBD URL:https://arni-institute.org/event/speaker-vijay-balasubramanian-arni-wg-multi-resource-cost-optimization-of-neural-network-models/ LOCATION:Zuckerman Institute – L3-079\, 3227 Broadway\, New York\, NY\, 10027\, United States END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20260327T110000 DTEND;TZID=America/New_York:20260327T120000 DTSTAMP:20260404T140450 CREATED:20260217T161534Z LAST-MODIFIED:20260324T150421Z UID:2372-1774609200-1774612800@arni-institute.org SUMMARY:Lecture Series in AI: Richard Zemel DESCRIPTION:General website \nTitle: Integrating Past and Present in Continual Learning \nAbstract: Continual learning aims to bridge the gap between typical human and machine-learning environments. The continual setting does not have separate training and testing phases\, and instead models are evaluated online while learning novel concepts and tasks. The most capable current AI systems struggle to learn new knowledge sequentially without forgetting old ones. Challenging research questions include how to rapidly assess a learner system’s abilities and how to most efficiently train it to improve on a sequence of tasks. I will describe recent progress on these questions\, across various research groups in ARNI\, our NSF AI Institute for Artificial and Natural Intelligence. Finally we will consider open issues and challenges in continual learning. \nBio: Richard Zemel is the Trianthe Dakolias Professor of Engineering and Applied Science in the Computer Science Department at Columbia University. \nHe is the Director of the NSF AI Institute for Artificial and Natural Intelligence (ARNI)\, and was the co-founder and inaugural Research Director of the Vector Institute for Artificial Intelligence. His awards include an AI Lifetime Achievement Award (CAIA) and a Pioneer of AI Award (NVIDIA). His research contributions include foundational work on systems that learn useful representations of data with little or no supervision; graph-based machine learning; and algorithms for fair and robust machine learning. URL:https://arni-institute.org/event/lecture-series-in-ai-richard-zemel/ LOCATION:Davis Auditorium\, 530 W 120th St\, New York\, NY 10027\, New York\, NY\, 10027 END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20260327T160000 DTEND;TZID=America/New_York:20260327T170000 DTSTAMP:20260404T140450 CREATED:20260324T150404Z LAST-MODIFIED:20260324T150404Z UID:2411-1774627200-1774630800@arni-institute.org SUMMARY:ARNI Biological Learning Working Group DESCRIPTION:Continuation of prior meetings. \nZoom: Upon request @arni@columbia.edu \n  \n  URL:https://arni-institute.org/event/arni-biological-learning-working-group-8/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20260330T150000 DTEND;TZID=America/New_York:20260330T160000 DTSTAMP:20260404T140450 CREATED:20260331T161001Z LAST-MODIFIED:20260331T161001Z UID:2422-1774882800-1774886400@arni-institute.org SUMMARY:Speaker Josue Ortega Caro: ARNI Frontier Models for Neuroscience and Behavior Working Group DESCRIPTION:Time: 30th March. 3pm EST\n\nTitle: Large scale models for spatiotemporal data.\nSpeaker: Josue Ortega Caro https://josueortc.github.io/\nAbstract:  Spatiotemporal and multimodal datasets contain structured variability distributed across space\, time\, and measurement modality\, motivating modeling approaches that can learn representations directly from large-scale data. Inspired by video foundational models\, we study how the masked autoencoder training objective can learn shared structure across heterogeneous observations while preserving modality-specific information\, and how training these models requires multiple engineering methods for scaling. Furthermore\, we show that self-attention supports the emergence of interpretable structure by decomposing them based on the variability across samples. These results suggest that large-scale self-supervised learning provides a unified approach for modeling high-dimensional dynamical systems while enabling interpretation of the learned representations. URL:https://arni-institute.org/event/speaker-josue-ortega-caro-arni-frontier-models-for-neuroscience-and-behavior-working-group/ LOCATION:Virtual END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20260409T150000 DTEND;TZID=America/New_York:20260409T160000 DTSTAMP:20260404T140450 CREATED:20260327T173721Z LAST-MODIFIED:20260327T173721Z UID:2418-1775746800-1775750400@arni-institute.org SUMMARY:Speaker: Mengye Ren - ARNI Continual Learning Working Group Meeting DESCRIPTION:Mengye Ren URL:https://arni-institute.org/event/speaker-mengye-ren-arni-continual-learning-working-group-meeting/ LOCATION:CEPSR 620\, Schapiro 530 W. 120th St END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20260410T160000 DTEND;TZID=America/New_York:20260410T170000 DTSTAMP:20260404T140450 CREATED:20260330T140055Z LAST-MODIFIED:20260330T140055Z UID:2421-1775836800-1775840400@arni-institute.org SUMMARY:ARNI Biological Learning Working Group DESCRIPTION:Continuation of prior meetings. \nZoom: Upon request @arni@columbia.edu URL:https://arni-institute.org/event/arni-biological-learning-working-group-9/ LOCATION:Virtual END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20260414T140000 DTEND;TZID=America/New_York:20260414T150000 DTSTAMP:20260404T140450 CREATED:20260402T193254Z LAST-MODIFIED:20260402T193254Z UID:2428-1776175200-1776178800@arni-institute.org SUMMARY:CTN: Jack Lindsey (Anthropic) DESCRIPTION:Title: The inner lives of language models \nAbstract: In recent years\, LLMs have evolved from bad text completion engines\, to decent chatbots\, to digital genies that work miracles on your computer (while making the occasional catastrophic error). The increasing sophistication of AI models’ behavior has been accompanied by a commensurate enrichment of their internal representations and computations. In this talk\, I’ll give an overview of what’s known about LLM cognition\, and the ways in which it emulates components of human psychology: emotional reactions\, strategic manipulation\, and forms of introspection. I’ll also cover aspects of LLM behavior that are fundamentally un-human-like\, owing to features of their architecture and training process\, and how these give rise to odd failure modes—for instance\, a weakly anchored sense of self. Finally\, I’ll discuss the urgency of addressing pathologies\, both human-like and alien\, of LLM psychology\, and some ideas for doing so. \nThe talk is in-person. If you do not have card access to the Jerome L. Greene Science center building\, you can email Arianna Pepin to be added to the guest list for the seminar. URL:https://arni-institute.org/event/ctn-jack-lindsey-anthropic/ LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20260427T150000 DTEND;TZID=America/New_York:20260427T160000 DTSTAMP:20260404T140450 CREATED:20260402T192942Z LAST-MODIFIED:20260402T192942Z UID:2425-1777302000-1777305600@arni-institute.org SUMMARY:Speaker: TBD – ARNI Frontier Models for Neuroscience and Behavior Working Group DESCRIPTION: URL:https://arni-institute.org/event/speaker-tbd-arni-frontier-models-for-neuroscience-and-behavior-working-group/ LOCATION:Virtual END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20260427T150000 DTEND;TZID=America/New_York:20260427T160000 DTSTAMP:20260404T140450 CREATED:20260402T193102Z LAST-MODIFIED:20260402T193102Z UID:2426-1777302000-1777305600@arni-institute.org SUMMARY:Speaker: Ziwei (Sara) Gong - ARNI Language and Vision Working Group DESCRIPTION:Title: Decoding Human Emotions: From Psychological Theories to Multimodal NLP Models\nAbstract: Understanding and modeling human emotions is essential for natural language processing (NLP) applications\, from conversational AI to mental health assessment. This talk explores the intersection of emotion theory\, dataset development\, and multimodal machine learning\, highlighting key challenges and innovations in emotion recognition. We discuss the alignment of psychological emotion frameworks with computational models\, strategies for improving multimodal emotion recognition\, and advances in self-supervised learning for low-resource languages. Additionally\, we examine how multimodal signals enhance model performance and interpretability. URL:https://arni-institute.org/event/speaker-ziwei-sara-gong-arni-language-and-vision-working-group/ LOCATION:Virtual END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20260512T130000 DTEND;TZID=America/New_York:20260512T190000 DTSTAMP:20260404T140450 CREATED:20260327T171344Z LAST-MODIFIED:20260327T171344Z UID:2415-1778590800-1778612400@arni-institute.org SUMMARY:Memory\, Neuroscience and AI: Zuckerman Institute’s Local Circuits Symposium DESCRIPTION:Register Here! \nHow are memories formed\, organized\, and used to guide behavior? And what can artificial intelligence teach us about how the brain remembers? \nJoin faculty and early-career researchers from across Columbia for the Local Circuits symposium\, exploring the science of memory across biological and artificial systems. Talks will span systems and cognitive neuroscience\, machine learning\, and theoretical modeling\, examining how brain circuits encode and retrieve memories and how AI is helping researchers probe these processes in new ways. \nPart of the Zuckerman Institute’s Local Circuits series\, this symposium brings together researchers from across the university to spark collaboration around mind\, brain\, and behavior. \nAll Columbia ID holders are welcome. Registration is required. \nPresented by the Alan Kanzer Center for Cognition and Reasoning \nOpening Remarks:\nAngela V. Olinto\, Provost of the University; Professor of Astronomy and of Physics\, Columbia University \nSpeakers include:\nChris Baldassano\, PhD\, Associate Professor of Psychology\, Columbia University\nChristine Denny\, PhD\, Associate Professor of Clinical Neurobiology (in Psychiatry)\, Columbia University Irving Medical Center\nStefano Fusi\, PhD\, Professor of Neuroscience\, Principal Investigator in the Zuckerman Institute\, Columbia University\nScott Small\, MD\, Boris and Rose Katz Professor of Neurology\, Director of the Alzheimer’s Disease Research Center\, Columbia University Irving Medical Center\nKim Stachenfeld\, PhD\, Senior Research Scientist at Google DeepMind in NYC and Adjunct Assistant Professor at the Center for Theoretical Neuroscience\, Columbia University\nRichard Zemel\, PhD\, Trianthe Dakolias Professor of Engineering and Applied Science; Professor of Computer Science; Director of the NSF AI Institute for Artificial and Natural Intelligence (ARNI)\, Columbia University \nModerator:\nDaphna Shohamy\, PhD\, Kavli Professor of Brain Science; Director of the Zuckerman Institute; Co-director of the Kavli Institute for Brain Science\, Columbia University URL:https://arni-institute.org/event/memory-neuroscience-and-ai-zuckerman-institutes-local-circuits-symposium/ LOCATION:Zuckerman Institute- Kavli Auditorium 9th Fl\, 3227 Broadway\, NY ORGANIZER;CN="Zuckerman Institute":MAILTO:events@zi.columbia.edu END:VEVENT END:VCALENDAR