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Seminar Series

May 26

Towards machines that perceive and communicate

Kevin Murphy
Google

Part Of

4:00P
- 5:30P

Location

MIT Singleton Auditorium (46-3002)
Add to Calendar 2017-05-26 16:00:00 2017-05-26 17:30:00 America/New_York Towards machines that perceive and communicate Schedule: Talk (60 min) + Q&A (15 min)Reception to follow (MIT, Bldg. 46 Atrium)Abstract: In this talk, I summarize some recent work in my group related to visual scene understanding and "grounded" language understanding. In particular, I discuss the following topics:- Our DeepLab system for semantic segmentation (PAMI'17, https://arxiv.org/abs/1606.00915).- Our object detection system, that won first place in the COCO'16 competition (CVPR'17, https://arxiv.org/abs/1611.10012).- Our instance segmentation system, that won second place in the COCO'16 competition (unpublished).- Our person detection/ pose estimation system, that won second place in the COCO'16 competition (CVPR'17, https://arxiv.org/abs/1701.01779).- Our work on visually grounded referring expressions (CVPR'16, https://arxiv.org/abs/1511.02283).- Our work on discriminative image captioning (CVPR'17, https://arxiv.org/abs/1701.02870).- Our work on optimizing semantic metrics for image captioning using RL (submitted to ICCV'17, https://arxiv.org/abs/1612.00370).- Our work on generative models of visual imagination (submitted to NIPS'17).I will explain how each of these pieces can be combined to develop systems that can better understand images and words.Bio: Kevin Murphy is a research scientist at Google in Mountain View, California, where he works on AI, machine learning, computer vision, and natural language understanding. Before joining Google in 2011, he was an associate professor (with tenure) of computer science and statistics at the University of British Columbia in Vancouver, Canada. Before starting at UBC in 2004, he was a postdoc at MIT. Kevin got his BA from U. Cambridge, his MEng from U. Pennsylvania, and his PhD from UC Berkeley. He has published over 80 papers in refereed conferences and journals, as well as an 1100-page textbook called "Machine Learning: a Probabilistic Perspective" (MIT Press, 2012), which was awarded the 2013 DeGroot Prize for best book in the field of Statistical Science. Kevin is also the (co) Editor-in-Chief of JMLR (the Journal of Machine Learning Research). MIT Singleton Auditorium (46-3002)

May 05

What Can Machines Learn, and What Does It Mean for Occupations and Industries?

Erik Brynjolfsson
MIT Sloan School of Management

Part Of

4:00P
- 5:00P

Location

MIBR 3rd Fl. Seminar Room (46-3189)
Add to Calendar 2017-05-05 16:00:00 2017-05-05 17:00:00 America/New_York What Can Machines Learn, and What Does It Mean for Occupations and Industries? * Please note the change in the talk's location; talk will be held in MIT Bldg 46, Seminar Room #46-3189.Abstract: This talk will present a preliminary framework and approach for understanding the potential effects of machine learning (ML) on tasks, occupations and industries. Digital technologies have already had a substantial effect on the wages and income. The increased availability of high quality data and rapid advances in ML have the potential to generate even larger effects in the coming decade. The ultimate impact will depend in part on the feasibility, costs and capabilities of ML-based applications for various types tasks and the speed with which they are implemented. Workers, firms and industries with complementary investments (e.g. relevant skills, data, and technologies) are well positioned to benefit, while those whose tasks are easily substituted for by ML will likely face downward pressure on wages and prices. We are developing a taxonomy of tasks most suitable for ML and plan to estimate some implications of our model by analyzing data from a major online resume and job postings marketplace.Biography: Erik Brynjolfsson is Director of the MIT Initiative on the Digital Economy, Professor at MIT Sloan School, and Research Associate at NBER. His research examines the effects of information technologies on business strategy, productivity and performance, digital commerce, and intangible assets. At MIT, he teaches courses on the Economics of Information and the Analytics Lab. Author or co-editor of several books including NYTimes best-seller The Second Machine Age: Work, Progress and Prosperity in a Time of Brilliant Technologies, Brynjolfsson is editor of SSRN’s Information System Network and has served on the editorial boards of numerous academic journals. MIBR 3rd Fl. Seminar Room (46-3189)

April 21

Perceptual Organization From a Bayesian Point of View

Jacob Feldman
Rutgers University

Part Of

4:00P
- 5:00P

Location

MIT Singleton Auditorium, 46-3002
Add to Calendar 2017-04-21 16:00:00 2017-04-21 17:00:00 America/New_York Perceptual Organization From a Bayesian Point of View Abstract: Perceptual organization is the process by which the visual system groups the visual image into distinct clusters or units. In this talk I'll sketch a Bayesian approach to grouping, formulating it as an inverse inference problem in which the goal it to estimate the organization that best explains the observed configuration of visual elements. We frame the problem as an instance of mixture estimation, in which the image configuration is assumed to have been generated by a set of distinct data-generating components or sources (``objects''), whose structure, locations, and number we seek to estimate. I'll show how the approach works in a variety of classic problems of perceptual organization, including clustering, contour integration, figure/ground estimation, shape representation, part decomposition, object detection, and shape similarity. Because the Bayesian framework unifies a diverse array of grouping rules under a single principle, namely maximization of the Bayesian posterior---or, equivalently, minimization of descriptive complexity---I'll argue that it provides a useful formalization of the somewhat vague Gestalt notion of Prägnanz (simplicity or "good form").Joint work with Manish Singh, Erica Briscoe, Vicky Froyen, John Wilder and Seha Kim. MIT Singleton Auditorium, 46-3002

April 12

Machine Learning Techniques and Applications in Finance, Healthcare and Recommendation Systems

David S. Vogel
Voloridge Investment Management, LLC

Part Of

2:00P
- 3:00P

Location

MIT 46-3002, Singleton Auditorium
Add to Calendar 2017-04-12 14:00:00 2017-04-12 15:00:00 America/New_York Machine Learning Techniques and Applications in Finance, Healthcare and Recommendation Systems Abstract: The introductory portion of this talk will review some state-of-the-art machine learning techniques. We will discuss concepts of ensembles and popular methodologies within this category. We’ll touch upon collaborative filtering techniques used for recommendation systems, and we’ll present certain algorithms published specifically for healthcare models.We will later focus on application of the mentioned machine learning techniques covering healthcare, recommendation systems and portfolio construction in finance. We will refer to some past data modeling competitions such as Netflix (2007) and the Heritage Health Prize (2014) where thousands of algorithms were pitted against each other and evaluated impartially on a withheld data set. Within the financial application we will present risk management models that can be used to dictate/constrain positions within the portfolio construction process.Biography: David S. Vogel is an award-winning predictive modeling scientist. In 2009, he founded the Voloridge Investment Management, LLC and also serves as its Chief Scientist, Chief Executive Officer, Chief Technology Officer and Managing Member. He has earned international recognition for models ranging from medical applications to direct marketing and has won numerous modeling competitions. David has also been invited to speak at conferences and research institutes worldwide. MIT 46-3002, Singleton Auditorium

March 24

The Convergence of Machine Learning and Artificial Intelligence Towards Enabling Autonomous Driving

Amnon Shashua
Hebrew University of Jerusalem, Mobileye

Part Of

4:30P
- 5:30P

Location

10-250
Add to Calendar 2017-03-24 16:30:00 2017-03-24 17:30:00 America/New_York The Convergence of Machine Learning and Artificial Intelligence Towards Enabling Autonomous Driving Abstract: The field of transportation is undergoing a seismic change with the coming introduction of autonomous driving. The technologies required to enable computer driven cars involves the latest cutting edge artificial intelligence algorithms along three major thrusts: Sensing, Planning and Mapping. I will describe the challenges and the kind of machine learning algorithms involved, and will do that through the perspective of Mobileye’s activity in this domain.Bio: Prof. Amnon Shashua holds the Sachs chair in computer science at the Hebrew University of Jerusalem. His field of expertise is computer vision and machine learning. For his academic achievements, he received the MARR prize Honorable Mention in 2001, the Kaye innovation award in 2004, and the Landau award in exact sciences in 2005.In 1999 Prof. Shashua co-founded Mobileye, an Israeli company developing a system-on-chip and computer vision algorithms for a driving assistance system, providing a full range of active safety features using a single camera. Today, approximately 10 million cars from 23 automobile manufacturers rely on Mobileye technology to make their vehicles safer to drive. In August 2014, Mobileye claimed the title for largest Israeli IPO ever, by raising $1B at a market cap of $5.3B. In addition, Mobileye is developing autonomous driving technology with more than a dozen car manufacturers. An early version of Mobileye’s autonomous driving technology was deployed in series as an "autopilot" feature in October, 2015, and will evolve to support more autonomous features in 2016 and beyond. The introduction of autonomous driving capabilities is of a transformative nature and has the potential of changing the way cars are built, driven and own in the future.In 2010 Prof. Shashua co-founded OrCam which harnesses the power of artificial vision to assist people who are visually impaired or blind. The OrCam MyEye device is unique in its ability to provide visual aid to hundreds of millions of people, through a discreet wearable platform. Within its wide-ranging scope of capabilities, OrCam’s device can read most texts (both indoors and outdoors) and learn to recognize thousands of new items and faces. 10-250