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UID:ai1ec-20120@www.clsp.jhu.edu
DTSTAMP:20240329T093354Z
CATEGORIES;LANGUAGE=en-US:Seminars
CONTACT:
DESCRIPTION:
Abstract
\nRobotics@Google’s mission
is to make robots useful in the real world through machine learning. We a
re excited about a new model for robotics\, designed for generalization ac
ross diverse environments and instructions. This model is focused on scala
ble data-driven learning\, which is task-agnostic\, leverages simulation\,
learns from past experience\, and can be quickly adapted to work in the r
eal-world through limited interactions. In this talk\, we’ll share some of
our recent work in this direction in both manipulation and locomotion app
lications.
\nBiography
\nCarolina Parada is a Senior Engineering Manager at Goo
gle Robotics. She leads the robot-mobility group\, which focuses on improv
ing robot motion planning\, navigation\, and locomotion\, using reinforcem
ent learning. Prior to that\, she led the camera perception team for self-
driving cars at Nvidia for 2 years. She was also a lead with Speech @ Goog
le for 7 years\, where she drove multiple research and engineering efforts
that enabled Ok Google\, the Google Assistant\, and Voice-Search. Carolina grew up in Venezuela and moved to the US
to pursue a B.S. and M.S. degree in Electrical Engineering at University
of Washington and her Phd at Johns Hopkins University at the Center for La
nguage and Speech Processing (CLSP).
DTSTART;TZID=America/New_York:20210423T120000
DTEND;TZID=America/New_York:20210423T131500
LOCATION:via Zoom
SEQUENCE:0
SUMMARY:Carolina Parada (Google AI) “State of Robotics @ Google”
URL:https://www.clsp.jhu.edu/events/carolina-parada-google-ai/
X-COST-TYPE:free
X-TAGS;LANGUAGE=en-US:2021\,April\,Parada
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-21487@www.clsp.jhu.edu
DTSTAMP:20240329T093354Z
CATEGORIES;LANGUAGE=en-US:Seminars
CONTACT:
DESCRIPTION:Abstract
\nEnormous amounts of ever-changing knowledge are a
vailable online in diverse textual styles and diverse formats. Recent adva
nces in deep learning algorithms and large-scale datasets are spurring pro
gress in many Natural Language Processing (NLP) tasks\, including question
answering. Nevertheless\, these models cannot scale up when task-annotate
d training data are scarce. This talk presents my lab’s work toward buildi
ng general-purpose models in NLP and how to systematically evaluate them.
First\, I present a general model for two known tasks of question answerin
g in English and multiple languages that are robust to small domain shifts
. Then\, I show a meta-training approach that can solve a variety of NLP
tasks with only using a few examples and introduce a benchmark to evaluate
cross-task generalization. Finally\, I discuss neuro-symbolic appr
oaches to address more complex tasks by eliciting knowledge from structure
d data and language models.
\n\nBiography
\n\nHanna Hajishirzi is an Assistant Professor in the Paul G. Allen Schoo
l of Computer Science & Engineering at the University of Washington and a
Senior Research Manager at the Allen Institute for AI. Her research spans
different areas in NLP and AI\, focusing on developing general-purpose mac
hine learning algorithms that can solve many NLP tasks. Applications for t
hese algorithms include question answering\, representation learning\, gre
en AI\, knowledge extraction\, and conversational dialogue. Honors include
the NSF CAREER Award\, Sloan Fellowship\, Allen Distinguished Investigato
r Award\, Intel rising star award\, best paper and honorable mention award
s\, and several industry research faculty awards. Hanna received her PhD f
rom University of Illinois and spent a year as a postdoc at Disney Researc
h and CMU.
DTSTART;TZID=America/New_York:20220225T120000
DTEND;TZID=America/New_York:20220225T131500
LOCATION:Ames Hall 234 - Presented Virtually Via Zoom https://wse.zoom.us/j
/96735183473
SEQUENCE:0
SUMMARY:Hanna Hajishirzi (University of Washington & Allen Institute for AI
) “Toward Robust\, Knowledge-Rich NLP”
URL:https://www.clsp.jhu.edu/events/hanna-hajishirzi-university-of-washingt
on-allen-institute-for-ai-toward-robust-knowledge-rich-nlp/
X-COST-TYPE:free
X-TAGS;LANGUAGE=en-US:2022\,February\,Hajishirzi
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