Invited talks

Invited Talk by Lillian Lee

“Big data pragmatics!”, or, “Putting the ACL in computational social science”, or, if you think these title alternatives could turn people on, turn people off, or otherwise have an effect, this talk might be for you.

What effect does language have on people?

You might say in response, "Who are you to discuss this problem?" and you would be right to do so; this is a Major Question that science has been tackling for many years. But as a field, I think natural language processing and computational linguistics have much to contribute to the conversation, and I hope to encourage the community to further address these issues.

This talk will focus on the effect of phrasing, emphasizing aspects that go beyond just the selection of one particular word over another. The issues we'll consider include: Does the way in which something is worded in and of itself have an effect on whether it is remembered or attracts attention, beyond its content or context? Can we characterize how different sides in a debate frame their arguments, in a way that goes beyond specific lexical choice (e.g., "pro-choice" vs. "pro-life")? The settings we'll explore range from movie quotes that achieve cultural prominence; to posts on Facebook, Wikipedia, Twitter, and the arXiv; to framing in public discourse on the inclusion of genetically-modified organisms in food.

Joint work with Lars Backstrom, Justin Cheng, Eunsol Choi, Cristian Danescu-Niculescu-Mizil, Jon Kleinberg, Bo Pang, Jennifer Spindel, and Chenhao Tan.

Lillian Lee is a professor of computer science and of information science at Cornell University, and the co-Editor-in-Chief, together with Michael Collins, of Transactions of the ACL. Her research interests include natural language processing and computational social science. She is the recipient of the inaugural Best Paper Award at HLT-NAACL 2004 (joint with Regina Barzilay), a citation in “Top Picks: Technology Research Advances of 2004” by Technology Research News (also joint with Regina Barzilay), and an Alfred P. Sloan Research Fellowship; and in 2013, she was named a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI). Her group’s work has received several mentions in the popular press, including The New York Times, NPR’s All Things Considered, and NBC’s The Today Show, and one of her co-authored papers was publicly called “boring” by Youtubers Rhett and Link, in a video viewed over 1.8 million times.

Invited Talk by Fei-fei Li

A Quest for Visual Intelligence in Computers

More than half of the human brain is involved in visual processing. While it took mother nature billions of years to evolve and deliver us a remarkable human visual system, computer vision is one of the youngest disciplines of AI, born with the goal of achieving one of the loftiest dreams of AI. The central problem of computer vision is to turn millions of pixels of a single image into interpretable and actionable concepts so that computers can understand pictures just as well as humans do, from objects, to scenes, activities, events and beyond. Such technology will have a fundamental impact in almost every aspect of our daily life and the society as a whole, ranging from e-commerce, image search and indexing, assistive technology, autonomous driving, digital health and medicine, surveillance, national security, robotics and beyond. In this talk, I will give an overview of what computer vision technology is about and its brief history. I will then discuss some of the recent work from my lab towards large scale object recognition and visual scene story telling. I will particularly emphasize on what we call the "three pillars" of AI in our quest for visual intelligence: data, learning and knowledge. Each of them is critical towards the final solution, yet dependent on the other. This talk draws upon a number of projects ongoing at the Stanford Vision Lab.

Dr. Fei-Fei Li is an Associate Professor in the Computer Science Department at Stanford, and the Director of the Stanford Artificial Intelligence Lab and the Stanford Vision Lab. Her research areas are in machine learning, computer vision and cognitive and computational neuroscience, with an emphasis on Big Data analysis. Dr. Fei-Fei Li has published more than 100 scientific articles in top-tier journals and conferences, including Nature, PNAS, Journal of Neuroscience, CVPR, ICCV, NIPS, ECCV, IJCV, IEEE-PAMI, etc. Dr. Fei-Fei Li obtained her B.A. degree in physics from Princeton in 1999 with High Honors, and her PhD degree in electrical engineering from California Institute of Technology (Caltech) in 2005. She joined Stanford in 2009 as an assistant professor, and was promoted to associate professor with tenure in 2012. Prior to that, she was on faculty at Princeton University (2007-2009) and University of Illinois Urbana-Champaign (2005-2006). Dr. Fei-Fei Li is a speaker at TED2015 main conference, a recipient of the 2014 IBM Faculty Fellow Award, 2011 Alfred Sloan Faculty Award, 2012 Yahoo Labs FREP award, 2009 NSF CAREER award, the 2006 Microsoft Research New Faculty Fellowship and a number of Google Research awards. Work from Fei-Fei’s lab have been featured in a number of popular press magazines and newspapers including New York Times, Wired Magazine, and New Scientists.