BEGIN:VCALENDAR VERSION:2.0 PRODID:-//128.220.36.25//NONSGML kigkonsult.se iCalcreator 2.26.9// CALSCALE:GREGORIAN METHOD:PUBLISH X-FROM-URL:https://www.clsp.jhu.edu X-WR-TIMEZONE:America/New_York BEGIN:VTIMEZONE TZID:America/New_York X-LIC-LOCATION:America/New_York BEGIN:STANDARD DTSTART:20231105T020000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RDATE:20241103T020000 TZNAME:EST END:STANDARD BEGIN:DAYLIGHT DTSTART:20240310T020000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RDATE:20250309T020000 TZNAME:EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:ai1ec-21277@www.clsp.jhu.edu DTSTAMP:20240329T023523Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nAs humans\, our understanding of language is grounded in a rich mental model about “how the world works” – that we learn throug h perception and interaction. We use this understanding to reason beyond w hat we literally observe or read\, imagining how situations might unfold i n the world. Machines today struggle at this kind of reasoning\, which lim its how they can communicate with humans.In my talk\, I will discuss three lines of work to bridge this gap between machines and humans. I will firs t discuss how we might measure grounded understanding. I will introduce a suite of approaches for constructing benchmarks\, using machines in the lo op to filter out spurious biases. Next\, I will introduce PIGLeT: a model that learns physical commonsense understanding by interacting with the wor ld through simulation\, using this knowledge to ground language. From an E nglish-language description of an event\, PIGLeT can anticipate how the wo rld state might change – outperforming text-only models that are orders of magnitude larger. Finally\, I will introduce MERLOT\, which learns about situations in the world by watching millions of YouTube videos with transc ribed speech. Through training objectives inspired by the developmental ps ychology idea of multimodal reentry\, MERLOT learns to fuse language\, vis ion\, and sound together into powerful representations.Together\, these di rections suggest a path forward for building machines that learn language rooted in the world.\nBiography\nRowan Zellers is a final year PhD candida te at the University of Washington in Computer Science & Engineering\, adv ised by Yejin Choi and Ali Farhadi. His research focuses on enabling machi nes to understand language\, vision\, sound\, and the world beyond these m odalities. He has been recognized through an NSF Graduate Fellowship and a NeurIPS 2021 outstanding paper award. His work has appeared in several me dia outlets\, including Wired\, the Washington Post\, and the New York Tim es. In the past\, he graduated from Harvey Mudd College with a B.S. in Com puter Science & Mathematics\, and has interned at the Allen Institute for AI. DTSTART;TZID=America/New_York:20220214T120000 DTEND;TZID=America/New_York:20220214T131500 LOCATION:Ames Hall 234 - Presented Virtually Via Zoom https://wse.zoom.us/j /96735183473 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Rowan Zellers (University of Washington) ” Grounding Language by Se eing\, Hearing\, and Interacting” URL:https://www.clsp.jhu.edu/events/rowan-zellers-university-of-washington- grounding-language-by-seeing-hearing-and-interacting/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nAs humans\, our understanding of language is grounded
in a rich mental model about “how the world works” – that we learn throug
h perception and interaction. We use this understanding to reason beyond w
hat we literally observe or read\, imagining how situations might unfold i
n the world. Machines today struggle at this kind of reasoning\, which lim
its how they can communicate with humans.
In my talk\, I will discuss three lines of work to bridge
this gap between machines and humans. I will first discuss how we might m
easure grounded understanding. I will introduce a suite of approaches for
constructing benchmarks\, using machines in the loop to filter out spuriou
s biases. Next\, I will introduce PIGLeT: a model that learns physical com
monsense understanding by interacting with the world through simulation\,
using this knowledge to ground language. From an English-language descript
ion of an event\, PIGLeT can anticipate how the world state might change –
outperforming text-only models that are orders of magnitude larger. Final
ly\, I will introduce MERLOT\, which learns about situations in the world
by watching millions of YouTube videos with transcribed speech. Through tr
aining objectives inspired by the developmental psychology idea of multimo
dal reentry\, MERLOT learns to fuse language\, vision\, and sound together
into powerful representations.
Together\, these directions suggest a path forward for building mac
hines that learn language rooted in the world.
Biography strong>
\nRowan Zellers is a final year PhD candidate at the Univers ity of Washington in Computer Science & Engineering\, advised by Yejin Cho i and Ali Farhadi. His research focuses on enabling machines to understand language\, vision\, sound\, and the world beyond these modalities. He has been recognized through an NSF Graduate Fellowship and a NeurIPS 2021 out standing paper award. His work has appeared in several media outlets\, inc luding Wired\, the Washington Post\, and the New York Times. In the past\, he graduated from Harvey Mudd College with a B.S. in Computer Science & M athematics\, and has interned at the Allen Institute for AI.
\n< /HTML> X-TAGS;LANGUAGE=en-US:2022\,February\,Zellers END:VEVENT BEGIN:VEVENT UID:ai1ec-23302@www.clsp.jhu.edu DTSTAMP:20240329T023523Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20230130T120000 DTEND;TZID=America/New_York:20230130T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Daniel Fried (CMU) URL:https://www.clsp.jhu.edu/events/daniel-fried-cmu/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,Fried\,January END:VEVENT END:VCALENDAR