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DTSTART;TZID=America/New_York:20241106T160000
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DTSTAMP:20260416T021501
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SUMMARY:Art by Humans and Machines
DESCRIPTION:Join us for this Campus Point Connection\, where Larry O’Gorman of Nokia Bell Labs will consider how art and technology fuse to create interactive experiences. \n\n\n\nO’Gorman will trace the evolution of tech-enhanced artworks by examining the histories of video\, audio\, biometrics and machine learning. Expect to hear about Bell Labs’ earliest collaborations with visionaries like Robert Rauschenberg and Merce Cunningham to today’s cutting-edge creations. \n\n\n\nGateway South: Room 216 \n\n\n\nABSTRACT \n\n\n\nIn the last decade\, the art and theater worlds have increasingly endeavored to create immersive experiences for their audiences. In many cases\, this entails the use of cameras and other sensors\, combined with recognition techniques so audience input can modify the artwork. Recent machine learning advancements have also increased artists’ enthusiasm for creating technology-enhanced artworks. \n\n\n\nIn this talk\, Larry O’Gorman of Nokia Bell Labs will discuss interactive methodologies using patterns from video\, audio\, biometrics\, and machine learning. O’Gorman will also show a sampling of the interactive artworks starting with the Experiments in Art and Technology that involved New York artists such as Robert Rauschenberg and Merce Cunningham working with Bell Labs engineers in the 1960s\, and up to present day.
URL:https://njbda.org/event/art-by-humans-and-machines/
LOCATION:Gateway South\, Stevens Institute of Technology\, 607 River Terrace\, Hoboken\, New Jersey\, United States
CATEGORIES:lectures/talks
ATTACH;FMTTYPE=image/jpeg:https://njbda.org/wp-content/uploads/2024/10/Stevens-Arts-.jpeg
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DTSTART;TZID=America/New_York:20241108T103000
DTEND;TZID=America/New_York:20241108T120000
DTSTAMP:20260416T021501
CREATED:20241031T150426Z
LAST-MODIFIED:20241031T150430Z
UID:2833-1731061800-1731067200@njbda.org
SUMMARY:Belief transport: The mathematical theory of learning agents
DESCRIPTION:Abstract:  Learning agents\, which include humans and (ideally) AI agents\, take action in the world and learn from the outcomes. I will present our recent efforts toward an integrated theory of learning agents that span learning\, planning\, and social reasoning. The talk will focus on cooperative communication as an extended case study\, and suggest directions\, implications\, and limitations of the proposed approach.  \n\n\n\nBio: Dr. Patrick Shafto is Professor of Mathematics and Computer Science at Rutgers University – Newark and Program Manager at DARPA’s I2O office. He spent the previous two years as Member of the School of Mathematics at the Institute for Advanced Study. Research in his lab focuses on mathematical foundations of learning in humans and machines. He has received honors and awards including an NSF CAREER award\, chair in Data Science\, and outstanding reviewer awards at NeurIPS and ICML. His research has been supported by NSF (EHR\, CISE\, SBE)\, DARPA\, DoD\, NIH\, and the intelligence community and is a fellow of the Cognitive Science Society.  \n\n\n\nLight breakfast will be served.
URL:https://njbda.org/event/belief-transport-the-mathematical-theory-of-learning-agents/
LOCATION:Princeton University Press\, 41 William St\, Princeton\, New Jersey\, United States
CATEGORIES:lectures/talks
ATTACH;FMTTYPE=image/jpeg:https://njbda.org/wp-content/uploads/2024/10/PatrickShafto.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241112T120000
DTEND;TZID=America/New_York:20241112T133000
DTSTAMP:20260416T021501
CREATED:20241031T151835Z
LAST-MODIFIED:20241031T151839Z
UID:2845-1731412800-1731418200@njbda.org
SUMMARY:Language model-guided anticipation and discovery of unknown metabolites
DESCRIPTION:Speaker: Michael Skinnider\, Princeton University \n\n\n\nLunch is available beginning at 12 PM \n\n\n\nSpeaker to begin promptly at 12:30 PM \n\n\n\nAbstract: Despite decades of study\, large parts of the human metabolome remain unexplored. Mass spectrometry-based metabolomics routinely detects thousands of unidentified small molecules within human tissues and biofluids\, but structure elucidation of novel metabolites remains a low-throughput endeavour. Here\, we present an approach that leverages chemical language models to discover previously uncharacterized metabolites. We introduce DeepMet\, a language model that learns the latent biosynthetic logic embedded within the chemical structures of known metabolites and exploits this understanding to anticipate the existence of as-of-yet undiscovered metabolites. Prospective synthesis of metabolites predicted to exist by DeepMet directs their targeted discovery. Integrating DeepMet with tandem mass spectrometry (MS/MS) data enables automated metabolite discovery within complex tissues. We demonstrate the potential for language models to accelerate the mapping of the metabolome by harnessinging DeepMet to discover several dozen mammalian metabolites. 
URL:https://njbda.org/event/language-model-guided-anticipation-and-discovery-of-unknown-metabolites/
LOCATION:Bendheim House\, Princeton University\, 26 Prospect Avenue\, Princeton\, New Jersey\, United States
CATEGORIES:lectures/talks
ATTACH;FMTTYPE=image/jpeg:https://njbda.org/wp-content/uploads/2024/10/mike_skinnider.jpg
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