Carnegie Mellon University
Department of Computer Science
AI Seminar 98/99 Schedule
Time: Tuesday 3:30-4:30pm
Place: Wean Hall 5409
The AI seminars are open to the public and will
be held on most Tuesdays at 3:30pm in Wean Hall, Carnegie Mellon University.
Special AI seminars can be arranged for visitors on dates other than Tuesdays.
The schedule will be updated daily. To volunteer to give an AI seminar
or to nominate an outside speaker, contact Dr.
Tai Sing Lee at tai@cs.cmu.edu.
All AI seminars will be posted to both cboards
and bboards (cs & robotics). Meanwhile, if you would like to/not
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mailing list by sending email to Sebastian_Thrun@heaven.learning.cs.cmu.edu.
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09/22/98 - Herb Simon Carnegie
Mellon University
Brand Names and Cumulative Science
In AI, we typically (and properly) implement our research with
large complex systems that employ a collection of interacting
mechanisms to achieve their results. Hence, we often think of
advances in AI knowledge in terms of the names of such programs:
e.g., LT, GPS, EPAM, PRODIGY, Soar, BACON, Act-R --just to mention
a few of local origin. However, it can be argued that the real
"action" lies largely in the mechanisms embedded in these programs,
and in issues about how such mechanisms can be combined effectively.
The "brand names" tend to make difficult the analysis and comparison
of these mechanisms or the exchange of knowledge between research
groups. One can argue that it has caused and causes an enormous
amount of duplication of effort. Physicists did not divide
quantum mechanics into the Heisenberg Brand, the Schrodinger
Brand,and the Dirac Brand, but analyzed in detail the relations
among these and use one or the other according to their
computational power in particular situations. When specific
"brand name" choices have arisen (wave v. particle theories of
light, Ampere's v. Faraday"s theories of electromagnetism,
phlogisten v. oxygen theories of production), they used
experimental techniques to analyze both similarities and
differences and to sort them out.
How can we develop AI theory that goes beyond Brand Names?
What techniques do we have for comparing theories (more than
staging horse races between them in particular tasks)? How
do we trace particular mechanisms and their development through
the theories in which they develop? The aim of the talk will
not be to provide answers to all of these questions but to
stimulate constructive discussion about new forms of research
activity that will address them.
Herb
Simon
09/29/98 - Sebastian Thrun Carnegie Mellon University
When Robots Meet and Lead People
A few weeks ago, a mobile robot named Minerva was "hired" by the
Smithsonian's National Museum of American History, to give tours to
visitors. During a two-week period, the robot successfully traversed
44km through the "unmodified" museum, interacting with more than
50,000 people along the way.
This talk presents some of the underlying scientific methods for
navigation and human-robot interaction. The speaker will argue that
Minerva's ability to negotiate crowds of people in natural
environments arises from three aspects: an ability to learn, an
ability to handle uncertainty in a principled manner, and the use of
any-time algorithms for decision making. In addition, special
emphasis was placed on a "lifelike" appearance when designing the
robot, using speech, a moving head, and a motorized face to
communicate simple "emotions" to museum visitors. Videos will be
shown!
The work was jointly carried out with Dieter Fox, Nicholas Roy, Greg
Armstrong, Chuck Rosenberg, Jamie Schulte, and Anne Watzman from CMU,
and Wolfram Burgard, Dirk Haehnel, Dirk Schulz, and Maren Bennewitz
from the University of Bonn (Germany).
Sebastian Thrun is affiliated with Carnegie Mellon University's
Computer Science Department, pursuing research on AI, machine
learning, and robotics.
10/02/98 - Andrew Moore
Robotics Institute and School of Computer Science,
Carnegie Mellon University
Multi-Value-Functions: Efficient Automatic Action Hierarchies for Multi-Goal Markov Decision and Reinforcement Learning Tasks
When you've planned a strategy to get to some goal in an uncertain,
noisy, world, but then someone asks about a different goal, what do
you do?
In this talk, we will review some approaches, and then come up
with a somewhat surprising result: it's actually not much harder
to plan in advance for all possible start/goal combinations
than for just one start and/or goal. This statement is subject to
a number of important caveats, described in the talk.
An interesting feature of this technique is that it is based
on natural and automatic generation of a hierarchy of abstract
Markov decision process controllers.
The talk will describe previous approaches to the "all-goals" problem.
It will then describe the new hierarchy, how to construct it, how to
prove its performance guarantees and some animated examples of its
empirical performance. We describe its role in motion planning and
inventory management. We believe the greatest use will come in
hierarchical control systems in which a higher level controller wishes
to not merely call one of a set of atomic lower level controllers, but
instead one of a set of lower level controllers that can be
parameterized with a subgoal. At the end of the talk we will discuss
how this is used for a task with hunters and prey which would not have
been computationally feasible with previous methods.
Andrew
Moore received a Phd in Computer Science from the University of Cambridge
in 1991 (thesis topic: Robot Learning). He worked as a post-doc with Chris
Atkeson at MIT AI lab for three years before joining CMU as an assistant
professor in Robotics/CS in 1993. His research interests include: Autonomous
learning systems for manufacturing, efficient algorithms for machine learning
and reinforcement learning, finite production scheduling, and machine learning
applied to optimization.
10/13/98 - R. Ravi
Operation Research and Department of Computer Science, Carnegie Mellon University
Computational Challenges in Molecular Biology
The past few decades have witnessed an explosion of knowledge in the
area of genomic sequences - their content, organization and function.
The assembly and analysis of this sequence information involves solving
several challenging computational problems en route.
After briefly describing some basic biological concepts and protocols, I
will outline the steps of a typical gene-hunting project to highlight
and describe the computational problems that arise in the course of such
a search. These problems include physical mapping, sequence assembly,
sequence alignments, phylogeny reconstruction, and gene prediction; A
variety of techniques from combinatorial optimization and machine
learning have been used to address these problems.
R. Ravi is an assistant professor of Operations Research at GSIA with a
courtesy appointment in the Computer Science Department. He has been at CMU since Fall 95. His primary
research interests
are Combinatorial Optimization, Approximation Algorithms and
Computational Molecular Biology. Before joining Carnegie Mellon,
Ravi was a DIMACS post-doctoral fellow at Princeton University during
94-95 as part of the special year on Mathematical Support for Molecular
Biology. Ravi's training and work in computational biology began during
a year of postdoctoral work at UC Davis in 93-94. Ravi has a Ph. D. in
Computer Science from Brown
University in '93, and a B. Tech. in Computer Science and Engineering
from the Indian Institute of Technology (IIT), Madras, in '89.
10/27/98 - Moises Goldszmidt SRI International
Getting the Best of Both Discretization and Parametric Fitting
Machine learning problems often confront users with many representation
choices
including the level of granularity for discrete features, and the family of
distributions to be used for continuous features. Needless to say that these
choices involve tradeoffs that influence the resulting model and its
performance. In this talk I will describe an approach, based on Bayesian
networks, for simultaneously representing features in different forms. I will
illustrate this approach in the context of pattern classification, where
continuous features are simultaneously represented in both discrete and
(semi)parametric form. This dual representation frees the classifier from
committing to one or the other, and enables different features to correlate to
either representation in the same model. Our empirical results show that this
classifier usually achieves performance that is as good as or better than
similar classifiers that commit to a single representation, or that include
both representations without modeling their relation. During the talk I will
place this method in the general context of inducing Bayesian networks from
data and discuss a set of open problems and future work.
Part of this talk includes joint work with Nir Friedman of the Hebrew
University and Tom Lee of SRI.
Dr. Moises Goldszmidt is currently a senior computer scientist with SRI
International performing research in the area of learning and adaptive
systems. Previous to that he was a member of the technical staff and
principal
investigator at the Rockwell Science Center in Palo Alto. He holds an
Electrical Engineering degree from Simon Bolivar University (Venezuela), a MSc
in Electrical Engineering from UC Santa Barbara, and a PhD in Computer Science
from UC Los Angeles. He has numerous publications on topics related to
representation and reasoning under uncertainty, decision making, and automatic
induction of probabilistic models. His research interests include: decision
making under uncertainty, automatic induction of probabilistic models, pattern
classification, data mining, planning and control, machine learning, knowledge
representation and AI.
01/26/99 - Kristian Hammond
Department of Computer Science, Northwestern University
Anticipating Users' Needs: Redeeming Big Brother in the Information Age
One of the core problems running through work in information systems is
"the query". Few people know how to build them and even fewer systems
provide aids that help to construct them. What is needed, is a new way of
communicating with information systems.
The research at Northwestern's Intelligent Information Laboratory (The
InfoLab) is aimed directly at this problem of reshaping the way in which
users interact with information to better fit the goals on the human side
of the equation. In this talk, I will discuss three of our approaches to
interaction with information access. The first---Q&A---takes its lead from
work in case-based reasoning and involves restructuring of the
communication to reflect user goals. The other two---Watson and the Content
and Control Project---take a somewhat more radical approach in which
explicit communication with the information systems vanishes, and is
replaced by a set of applications that observe users and provide
information as it is needed rather than requested. In all three, the
ultimate goal is to remove the query from the information access equation
altogether.
Kristian Hammond
received his Ph.D. in Computer Science from Yale
University in May of 1986. From 1986 until September of 1998, he was the
Director of the Artificial Intelligence Laboratory at the University of
Chicago and guided the development of intelligent agents in domains ranging
from radiation treatment therapy to geometry problem solving and real-time
game playing. His focus has been on the development of models of cognition
based on episodic memory and the view of reasoning as reminding.
Four years ago, Dr. Hammond formed The Chicago Intelligent Information
Laboratory (InfoLab). The InfoLab's mission is focused on issues of
information access and management that rise out of the high-speed computer
connectivity of the modern world. Its charter is to invent and creatively
exploit new information technologies in the development of systems that are
responsive to and supportive of human goals and their achievement within
complex computer environments. The InfoLab's mandate is to seize the
opportunities of the information age and develop technologies that forward
rather than impede human endeavors.
In the Summer of 1998, Professor Hammond and the InfoLab moved to
Northwestern University's Computer Science Department and the Institute for
Learning Sciences.
02/23/99 - Paul Viola MIT
AI Laboratory
A Non-parametric Multi-scale Statistical Model for Texture: from Psychophysics to Graphics
Several theories of texture perception use Gabor-like image
representations to model human performance on texture segmentation and
recogntion tasks. I will present a new generative statistical model
for texture which explictly incorporates what we have learned from
these models. The generative process is hierarchical, conditioning
fine scale details on coarser scales. The conditional distributions
are non-parametric and can be estimated from example textures. Our
model is quite general and can be used to model textures which are
"noisy" as well as those that contain textons. The model can be used
to recognize textures, but interestingly it can also generate novel
textures. While these textures are different from the original
textures, they are almost indistinguishable from the original. The
model is useful for computer vision (recognition), psychophysics and
computer graphics (texture mapping).
Paul Viola is an Assistant Professor of Computer Science and
Engineering at the Massachusetts Institute of Technology. Professor
Viola received his Ph.D. from MIT in 1995. Before returning to MIT
as a professor he spent two years as a visiting scientist in the
Computational Neurobiology of the Salk Institute in San Deigo. He has
a broad background in advanced computational techniques, publishing in
the fields of computer vision, neurobiological vision, medical
imaging, mobile robotics, machine learning, and automated drug design.
Professor Viola's research focuses on statistical models of images and
the underlying structures that create them. He has also done work on
the grammatical structure of shapes and image database retrieval.
03/16/99 - Pietro Perona
California Institute of Technology
Recognition of Object Classes via Photometry and Probabilistic Geometry
Joint work with Mike Burl, Thomas Leung and Markus Weber.
Objects that share a similar visual appearance, e.g. dogs, sneakers,
sedan cars, are commonly grouped into classes by human observers.
Despite the fact that no two faces/dogs are identical we can easily
detect, and recognize as such, a previously unseen face/dog amongst a
clutter of other objects and background texture.
We develop a model for these `visual' classes of objects. It consists
of `features' and `shape'. The features are localized texture patches
at different scales of resolution (e.g. eye-corners, eyes, mouth,
jawline). The shape is the mutual position of those features.
The intrinsic variability of an object class is represented by a
probability density which assigns a likelihood to any variation of
the appearance of the textured patches and shape.
The shape is constructed in such a way to be invariant with respect
to translation, rotation and scale - and to affine deformations if needed.
Occlusion and deformation are handled in a principled way.
Pietro Perona
03/30/99 - Bernardo A. Huberman Xerox Palo Alto Research Center
The Laws of the Web
The World Wide Web has become in a short period a standard source of
information for a large part of the world's population. Its exponential
growth has rapidly transformed it into an ecology of knowledge in which a
highly
diverse quantity of information is linked in extremely complex and
arbitrary fashion.
In spite of the sheer size of the Web and its highly interactive nature,
there exist strong statistical regularities that govern the way it grows,
and how people use it and interact with each other. This talk will discuss
the existence and nature of such laws and their experimental verification.
Dr. Bernardo A. Huberman is a Research Fellow at the Xerox Palo Alto Research Center, processes in social organizations and computational systems. He received
his Ph.D. in Physics from the University of Pennsylvania, and is currently
a Consulting Professor of Physics at Stanford University. He has worked in
condensed matter physics and the theory of critical phenomena, and is one
of the discoverers of chaos in a number of physical systems. He established
a number of universal properties in nonlinear dynamics, and his research
into the dynamics of complex systems led to the discovery of ultradiffusion
in hierarchical structures.
In the field of computation, he predicted the existence of phase
transitions in artificial intelligence and large scale computational
systems, which have now been observed in a number of computationally hard
problems. Dr. Huberman is also one of the pioneers in the field of ecology
of computation, and editor of a book on the subject.
Recently, Dr. Huberman has been studying the power of cooperation in
collective problem solving by groups, and the dynamics of the Internet,
where he discovered a number of strong regularities, such as the law of
surfing and the signature of social dilemmas underlying congestion.
At the practical level his team designed and implemented Spawn, a market
system for the allocation of idle resources among machines in computer
networks, and a thermal market mechanism for the control of building
environments. He also holds a number of patents on self-repairing parallel
computers, a parallel motion detector and an electronic auction for
distributed document services over the Internet.
Dr. Huberman is a Fellow of the American Physical Society, a former trustee
of the Aspen Center for Physics and Fellow of the Japan Society for the
Promotion of Science. He is co-winner of the 1990 CECOIA prize in Economics
and Artificial Intelligence and he recently received the IBM Prize of the
Society for Computational Economics. He is also the Chairman of the
Council of Fellows at Xerox Corporation, and a faculty member of the Symbolics
Systems Program at Stanford University. He has held visiting professorships
at the University of Paris and the University of Copenhagen.
04/06/99 - Yoram Singer AT&T Shannon Laboratory
"80 and tea: ho May I hell Pooh ?": Boosting Algorithms for Text and Speech Categorization
When an AT&T customer dials 00, she gets an operator who asks: ``AT&T:
how may I help you?''. A possible answer might be: ``Ah yes, I would
like to call 908-508-1464 and charge it to my Visa.'' Being able to
automatically determine what the caller actually wants (in this case a
`dial-for-me' request and a `credit-card-call' request) might be a
difficult task especially since there is more than one action to be
taken due to the caller's request.
The talk focuses on algorithms which learn from examples to perform
text and speech categorization tasks such as the one above. I will
first describe a new family of algorithms for classification problems.
These algorithms use a machine-learning technique called boosting, a
method based on the idea of combining many simple and only moderately
accurate rules into a single highly accurate classification rule. I
will give a simple analysis of new boosting algorithms and describe
various extensions to multiclass settings. I will conclude the talk
with a demonstration of a system for automatic call-type identification
from unconstrained spoken/written requests.
This is joint work with Rob Schapire (AT&T Labs).
Yoram Singer is a member of the technical staff at AT&T Shannon Laboratory.
He received his PhD in computer science from the Hebrew University, Jerusalem,
Israel. His research focuses on statistical models and algorithms for written
and spoken language understanding.
04/13/99 - Usama Fayyad
Microsoft Research
Data Mining and Databases: Recent Developments
Systems for extracting interesting structure from databases, especially
large data stores are becoming a necessity. The existing data access
model is clearly hitting its limits. Data Mining methods provide a
way to address some of these problems. These methods have their origins
in statistics, databases, pattern recognition, learning, visualization,
and parallel computing. I'll outline some recent advances towards scaling
mining algorithms to large database, and cover the research challenges
and opportunities posed by the problem of extracting models from massive
data sets. The talk will particularly focus on the decomposition of
classification and clustering algorithm so they work effectively with
a database system backend.
Usama Fayyad is a Senior Researcher at Microsoft Research. His research interests
include scaling data mining algorithms to large databases,
learning algorithms, and statistical pattern recognition, especially
classification and clustering. After receiving the Ph.D. degree from
The University of Michigan, Ann Arbor in 1991, he joined the Jet
Propulsion Laboratory (JPL), California Institute of Technology,
where (until 1996) he headed the Machine Learning Systems Group and
developed data mining systems for automated science data analysis. He
received the 1994 NASA Exceptional Achievement Medal and the JPL 1993
Lew Allen Award for Excellence in Research for his work on developing
data mining systems to solve challenging science analysis problems in
astronomy and remote sensing. He remains affiliated with JPL as a
Distinguished Visting Scientist. He is a co-editor of Advances
in Knowledge Discovery and Data Mining (AAAI/MIT Press, 1996) and is an
Editor-in-Chief of the journal: Data Mining and Knowledge Discovery. He was
program co-chair of KDD-94 and KDD-95 (the First International Conference on Knowledge Discovery and Data Mining) and a general chair of KDD-96. He
co-chaired the 1997 Workshops on the role of KDD in Visualizations held at
KDD-97 and IEEE Vis-97 conferences.
04/20/99 - Dana Ballard
University of Rochester
A Model of Predictive Coding Based on Spike Timing
It is remarkeable that so much progress has been made in understanding
cognitive processes without a comprehensive understanding of how
neurons work. Several decades of research have made many advances
towards the goal of interpreting the neural spike train but a
comprehensive understanding remains elusive. A new class of neural
models termed predictive models characterize the cortex as a
memory whose parameters can be used to predict its input. This allows
the input to be economically coded as a residual difference between
itself and the prediction. Such models have had considerable success
in modeling features of visual cortex. We show that the predictive
coding model can be extended to a lower level of detail that includes
individual neural spikes as primitives. This is a significant improvement in
perspicuity compared to the firing rate variables used by most current
models. The specific model we describe exploits the use of coincidence
of spike arrival times and the fact that neural representations can be
distributed over large numbers of cells.
Dana Ballard
Ph.D. (1974) University of California at Irvine. Visiting Consultant, Laboratorio Technol. Biomediche, Rome, Italy (74-75). Assistant Professor of Computer Science and Radiology (75-82), Associate Professor of Computer Science (82-87), Professor (87-present); University of Rochester.
Co-author of Computer Vision and author of Introduction to Natural Computation.
Dana Ballard's main research interest is in computational theories of the brain with emphasis on human vision. In 1985 with Chris Brown, he led a team that designed and built a high speed binocular camera control system that is capable of simulating human eye movements. The system is mounted on a robotic arm that allows it to move at one meter per second in a two meter radius workspace. This system has led to an increased understanding of the role of behavior in vision. The theoretical aspects of that system were summarized in a paper "Animate Vision", which received the Best Paper Award at the 1989 International Joint Conference on Artificial Intelligence.
Dana is also interested in models of the brain that relate to detailed neural models. A position paper on this work appeared in the Behavioral and Brain Sciences and can be accessed.
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