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CSLI Calendar, Wednesday, 14 July 2004, vol. 19:44
CSLI CALENDAR OF PUBLIC EVENTS
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14 July 2004 Stanford Vol. 19, No. 44
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A weekly publication of the
Center for the Study of Language and Information (CSLI)
Stanford University, Cordura Hall, Stanford, CA 94305-4115
http://www-csli.stanford.edu/
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Happy Bastille Day!!
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ACTIVITIES FROM 14 JULY 2004 TO 23 JULY 2004
THURSDAY, 15 JULY 2004
4:00pm PARC Forum
George Pake Auditorium at PARC
"Perfect Devices:
The Amazing Endurance of Evolving Hard Disk Drives"
Giora J. Tarnopolsky
TarnoTek
http://www.parc.com/forum/
MONDAY, 19 JULY 2004
10:00am SRI AI Seminar Series
EJ228, SRI International
"A Framework for Constraint Reasoning with Uncertain Data"
Neil Yorke-Smith
Imperial College, London
http://www.ai.sri.com/seminars/
Abstract below
TUESDAY, 20 JULY 2004
11:00am UC Berkeley CIS Seminar
Soda Hall 320 (UC Berkeley)
"Programming by demonstration: two machine learning approaches"
Tessa Lau
IBM TJ Watson Research
http://www.eecs.berkeley.edu/~ywteh/cis-seminar
Abstract below
6:45pm SULUG Meetin
Gates 104
"Snort - linux intrustion detection software"
Tom Fulton
Novell/SuSE
http://sulug.stanford.edu/
THURSDAY, 22 JULY 2004
4:00pm PARC Forum
George Pake Auditorium at PARC
Title to be announced
Alan Waufle
The Hiller Aviation Museum
http://www.parc.com/forum/
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Stanford Blood Center status: Shortage of everything (especially O).
For an appointment: http://bloodcenter.stanford.edu/ or call
650-723-7831. It only takes an hour of your time.
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SRI AI SEMINAR SERIES
on Monday, 19 July 2004, 10:00am
EJ228, SRI International
http://www.ai.sri.com/seminars/
"A Framework for Constraint Reasoning with Uncertain Data"
Neil Yorke-Smith
Imperial College, London
Uncertainty is a common feature of real-life combinatorial
optimization problems. Constraint programming is an AI approach that
has developed into a powerful tool for tackling these problems, but
constraint programming with uncertain data can lead us to solve the
wrong problem because of the approximations made. This outcome is of
little help to a user who expects the right problem to be tackled and
reliable information returned. In this talk we present the certainty
closure, a framework for reliable constraint reasoning in the presence
of uncertain data. We describe how to provide the user with reliable
insight, by enclosing the uncertainty using what is known for sure
about the data, and then deriving a closure, a set of potential
solutions to the uncertain constraint problem. We outline the formal
basis of the framework, and illustrate the benefits on two case
studies in network optimization and aerospace planning. This is joint
work with Carmen Gervet.
About the Speaker: Neil Yorke-Smith received his Ph.D. in Artificial
Intelligence from Imperial College London in June 2004. His thesis
research focused on handling uncertainty in constraint-based
reasoning. During summer 2003, Neil visited the NASA Ames Research
Center, where he worked on probabilistic reasoning for temporal
planning.
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UC BERKELEY CIS SEMINAR
on Tuesday, 20 July 2004, 11:00am
Soda Hall 320 (UC Berkeley)
http://www.eecs.berkeley.edu/~ywteh/cis-seminar
"Programming by demonstration: two machine learning approaches"
Tessa Lau
IBM TJ Watson Research
The goal of programming by demonstration is to enable end users to
program computers not by writing code in an arcane language but by
simply demonstrating what they want the computer to do. This problem
raises an interesting challenge for machine learning: can we infer a
user's intent by learning from traces of her performing the same task
over and over in the user interface?
In this talk, I describe two different approaches to this problem that
address different points in this space. First, I demonstrate the
SMARTedit system, which learns to automate repetitive text-editing
procedures using an algorithm based on version space algebra. Next, I
present the Sheepdog system, which learns technical support procedures
by demonstration using a Hidden Markov Model-based algorithm. Our
user study results show that Sheepdog is capable of learning accurate
procedures from very noisy demonstrations. I conclude with a
discussion of the open problems and directions for future work.
About the Speaker: Tessa Lau is a Research Staff Member at IBM's
T.J. Watson Research Center. She completed her Ph.D. in computer
science at the University of Washington in 2001. Her primary research
interest is intelligent user interfaces: using artificial intelligence
to improve human-computer interaction by building tools that adapt and
learn from human use. She has been working in the field of
programming by demonstration since 1998. She is also a leading member
of the Watson Women's Network, a diversity group dedicated to
attracting and supporting women at IBM Research.
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