Forum for Artificial Intelligence


Forum for Artificial Intelligence

[ About FAI   |   Upcoming talks   |   Past talks ]



The Forum for Artificial Intelligence meets every other week (or so) to discuss scientific, philosophical, and cultural issues in artificial intelligence. Both technical research topics and broader inter-disciplinary aspects of AI are covered, and all are welcome to attend!

If you would like to be added to the FAI mailing list, subscribe here. If you have any questions or comments, please send email to Bo Xiong or Josiah Hanna.

The current schedule is also available as a Google Calendar.



[ Upcoming talks ]

Fri, December 1
11:00AM
GDC 6.302
Kenneth D. Forbus
Northwestern University
Bootstrapping Cognitive Systems
Wed, December 13
11:00AM
GDC 6.302
Brian Scassellati
Yale University
Title TBD
Fri, January 12
11:00AM
GDC 6.302
Geoffrey A. Hollinger
Oregon State University
Marine Robotics: Planning, Decision Making, and Learning

Friday, December 1, 2017, 11:00AM



GDC 6.302

Bootstrapping Cognitive Systems

Kenneth D. Forbus   [homepage]

Northwestern University

Annotation, even by crowdsourcing, is a trap: People learn without others having privileged access to their mental states. Today’s ML systems require a cadre of technical experts to train them, using massively more data than people require, whereas people manage their own learning processes over a lifetime. Understanding how to build cognitive systems that can learn well from small amounts of data, expressed in forms natural to people, and able to manage their own learning over extended periods would be a revolutionary advance. In the Companion cognitive architecture, we are exploring ways to do this, using a combination of analogy and relational representations. This talk will describe several recent advances we have made, including learning human behavior from Kinect data, analogical chaining for commonsense reasoning, and co-learning of language disambiguation and reasoning using unannotated data. Ideas for scaling an analogical approach to cognitive systems to human-sized knowledge bases and potential applications along the way will also be discussed.

About the speaker:

Kenneth D. Forbus is the Walter P. Murphy Professor of Computer Science and Professor of Education at Northwestern University. He received his degrees from MIT (Ph.D. in 1984). His research interests include qualitative reasoning, analogical reasoning and learning, spatial reasoning, sketch understanding, natural language understanding, cognitive architecture, reasoning system design, intelligent educational software, and the use of AI in interactive entertainment. He is a Fellow of the Association for the Advancement of Artificial Intelligence, the Cognitive Science Society, and the Association for Computing Machinery. He has received the Humboldt Award and has served as Chair of the Cognitive Science Society.

Wednesday, December 13, 2017, 11:00AM



GDC 6.302

Title TBD

Brian Scassellati   [homepage]

Yale University

Abstract TBD

About the speaker:

TBA

Friday, January 12, 2018, 11:00AM



GDC 6.302

Marine Robotics: Planning, Decision Making, and Learning

Geoffrey A. Hollinger   [homepage]

Oregon State University

Underwater gliders, propeller-driven submersibles, and other marine robots are increasingly being tasked with gathering information (e.g., in environmental monitoring, offshore inspection, and coastal surveillance scenarios). However, in most of these scenarios, human operators must carefully plan the mission to ensure completion of the task. Strict human oversight not only makes such deployments expensive and time consuming but also makes some tasks impossible due to the requirement for heavy cognitive loads or reliable communication between the operator and the vehicle. We can mitigate these limitations by making the robotic information gatherers semi-autonomous, where the human provides high-level input to the system and the vehicle fills in the details on how to execute the plan. These capabilities increase the tolerance for operator neglect, reduce deployment cost, and open up new domains for information gathering. In this talk, I will show how a general framework that unifies information theoretic optimization and physical motion planning makes semi-autonomous information gathering feasible in marine environments. I will leverage techniques from stochastic motion planning, adaptive decision making, and deep learning to provide scalable solutions in a diverse set of applications such as underwater inspection, ocean search, and ecological monitoring. The techniques discussed here make it possible for autonomous marine robots to “go where no one has gone before,” allowing for information gathering in environments previously outside the reach of human divers.

About the speaker:

Geoffrey A. Hollinger is an Assistant Professor in the School of Mechanical, Industrial & Manufacturing Engineering at Oregon State University. His current research interests are in adaptive information gathering, distributed coordination, and learning for autonomous robotic systems. He has previously held research positions at the University of Southern California, Intel Research Pittsburgh, University of Pennsylvania’s GRASP Laboratory, and NASA's Marshall Space Flight Center. He received his Ph.D. (2010) and M.S. (2007) in Robotics from Carnegie Mellon University and his B.S. in General Engineering along with his B.A. in Philosophy from Swarthmore College (2005). He is a recent recipient of the 2017 Office of Naval Research Young Investigator Program (YIP) award.


[ Past talks]

Fri, November 3
11:00AM
GDC 6.302
Kyunghyun Cho
New York University
Deep Learning, Where are you going?

Friday, November 3, 2017, 11:00AM



GDC 6.302

Deep Learning, Where are you going?

Kyunghyun Cho   [homepage]

New York University

There are three axes along which advances in machine learning and deep learning happen. They are (1) network architectures, (2) learning algorithms and (3) spatio-temporal abstraction. In this talk, I will describe a set of research topics I've pursued in each of these axes. For network architectures, I will describe how recurrent neural networks, which were largely forgotten during 90s and early 2000s, have evolved over time and have finally become a de facto standard in machine translation. I continue on to discussing various learning paradigms, how they related to each other, and how they are combined in order to build a strong learning system. Along this line, I briefly discuss my latest research on designing a query-efficient imitation learning algorithm for autonomous driving. Lastly, I present my view on what it means to be a higher-level learning system. Under this view each and every end-to-end trainable neural network serves as a module, regardless of how they were trained, and interacts with each other in order to solve a higher-level task. I will describe my latest research on trainable decoding algorithm as a first step toward building such a framework.

About the speaker:

Kyunghyun Cho is an assistant professor of computer science and data science at New York University. He was a postdoctoral fellow at University of Montreal until summer 2015, and received PhD and MSc degrees from Aalto University early 2014. He tries best to find a balance among machine learning, natural language processing and life, but often fails to do so.

[ FAI Archives ]

Fall 2016 - Spring 2017

Fall 2015 - Spring 2016

Fall 2014 - Spring 2015

Fall 2013 - Spring 2014

Fall 2012 - Spring 2013

Fall 2011 - Spring 2012

Fall 2010 - Spring 2011

Fall 2009 - Spring 2010

Fall 2008 - Spring 2009

Fall 2007 - Spring 2008

Fall 2006 - Spring 2007

Fall 2005 - Spring 2006

Spring 2005

Fall 2004

Spring 2004

Fall 2003

Spring 2003

Fall 2002

Spring 2002

Fall 2001

Spring 2001

Fall 2000

Spring 2000