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| Challenges for Motlanthe |
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The celebration is over, and now it is time
for South Africa's new president to get to
work. CNN's Robyn Curnow reports.
Tags : CNN News World |
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Affichage : 76
Durée : 128 s |
| Challenges and Opportunities in Academic Research Networking |
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Google Tech Talks
January, 28 2008
ABSTRACT
Topics include:
* New applications and apps
usage/deployment and the challenges they pose
for networks
* Network trends in the R&E community.
o towards network virtualisation
(circuits as a service/light paths
provisioned by users or applications on
demand) - "Articulated Private Networks"
o inter-network control, managing
QoS / lightpaths and other performance
attributes across network domains
o sustainability issues - reducing
the Carbon footprint of the network itself
and our users
Speaker: Donald Clark, Chief Executive,
REANNZ (Research & Education Advanced Network
NZ Ltd)
Tags : google techtalks techtalk engedu talk talks googletechtalks education |
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Affichage : 6529
Durée : 3598 s |
| Challenges in Causality |
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Google Tech Talks
February, 11 2008
ABSTRACT
What affects your health, the economy,
climate changes? And what actions will
have beneficial effects? These are some of
the central questions of causal
discovery. A "causal model" is a model
capable of making predictions under
changing circumstances, corresponding to
actions of "external agents" on a
system of interest. For example, a doctor
administering a drug to a patient, a
government enforcing a new tax law or a new
environmental policy. It is often
necessary to assess the benefits and risks of
potential actions using available
past data and excluding the possibility of
experimenting. Experiments, which
are the ultimate way of verifying causal
relationships, are in many cases too
costly, infeasible, or unethical. For
instance, enforcing a law prohibiting to
smoke in public places is costly, preventing
people from smoking may be
infeasible, and forcing them to smoke would
be unethical. In contrast,
"observational data" are available in
abundance in many applications. Recently,
methods to devise causal models from
observational data have been proposed. Can
causal models thus obtained be relied upon to
make important decisions? In this
presentation, we will challenge the hopes an
promises of causal discovery and
present new means of assessing the validity
of causal modeling techniques.
Want to play? Check the "causation and
prediction" competition presently going
on:
http://www.causality.inf.ethz.ch/challenge.ph
p
Deadline April 30, 2008
Speaker: Isabelle Guyon
Isabelle Guyon is a researcher in machine
learning and an independent
consultant. Prior to starting her consulting
practice in 1996, she
worked at AT&T Bell Laboratories, where she
pioneered applications of neural
networks to pen computer interfaces and
invented Support Vector Machines (in
collaboration with B. Boser and V. Vapnik).
Isabelle Guyon holds a Ph.D. degree
in Physical Sciences of the University Pierre
and Marie Curie of Paris, France.
She is vice-president of the Unipen
foundation, action editor of the Journal of
Machine Learning Research, and competition
chair of the IJCNN conference.
Tags : google techtalks techtalk engedu talk talks googletechtalks education |
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Affichage : 4859
Durée : 3638 s |
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