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:20240328T145741Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nAs humans\, our understand
ing of language is grounded in a rich mental model about “how the world wo
rks” – that we learn through perception and interaction. We use this under
standing to reason beyond what we literally observe or read\, imagining ho
w situations might unfold in the world. Machines today struggle at this ki
nd of reasoning\, which limits how they can communicate with humans.
In my talk\, I will discuss th
ree lines of work to bridge this gap between machines and humans. I will f
irst discuss how we might measure grounded understanding. I will introduce
a suite of approaches for constructing benchmarks\, using machines in the
loop to filter out spurious biases. Next\, I will introduce PIGLeT: a mod
el that learns physical commonsense understanding by interacting with the
world through simulation\, using this knowledge to ground language. From a
n English-language description of an event\, PIGLeT can anticipate how the
world state might change – outperforming text-only models that are orders
of magnitude larger. Finally\, I will introduce MERLOT\, which learns abo
ut situations in the world by watching millions of YouTube videos with tra
nscribed speech. Through training objectives inspired by the developmental
psychology idea of multimodal reentry\, MERLOT learns to fuse language\,
vision\, and sound together into powerful representations. Together\, these directions suggest a pa
th forward for building machines that learn language rooted in the world.<
/p>\n
Biography
\nRowan Zellers is a final year P hD candidate at the University of Washington in Computer Science & Enginee ring\, advised by Yejin Choi and Ali Farhadi. His research focuses on enab ling machines to understand language\, vision\, sound\, and the world beyo nd these modalities. He has been recognized through an NSF Graduate Fellow ship and a NeurIPS 2021 outstanding paper award. His work has appeared in several media outlets\, including Wired\, the Washington Post\, and the Ne w York Times. In the past\, he graduated from Harvey Mudd College with a B .S. in Computer Science & Mathematics\, and has interned at the Allen Inst itute 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-TAGS;LANGUAGE=en-US:2022\,February\,Zellers END:VEVENT END:VCALENDAR