It's always exciting when a new robot arrives in your lab. Usually, the more expensive the robot is, the more exciting it is. With the possible exception of Boston Dynamics' ATLAS, NASA's Valkyrie has got to be one of the most expensive humanoid robots ever made, and last year, NASA promised to give away (or, at least, lend) three of them to universities in the hope that Valkyrie will learn some new skills. Within the last few weeks, the University of Massachusetts Lowell, which teamed up with Northeastern University in Boston, Mass., took delivery of their fancy new robot, as did MIT and the University of Edinburgh in Scotland. We talked to Holly Yanco at UMass Lowell and Taskin Padir at Northeastern, along with Sethu Vijayakumar at Edinburgh and Russ Tedrake at MIT, about what it's like to have a smokin' hot space robot show up on your doorstep in a bunch of pieces.
CHRISTOPHER BOOKER: The most unpleasant job in all of Cambridge, Massachusetts, Boston next door neighbor, may well belong to a robot named Luigi. These researchers from the Massachusetts Institute of Technology lower Luigi into the city's sewer system to collect samples of human wastewater. More than two-feet long this robot is on the cutting edge of data collection and public health research. He was created by M.I.T.'s Senseable City lab, which innovates new approaches to study urban environments. ERIC ALM: It's a great way to get behavioral data that would be very difficult to get on a person-by-person basis.
Yiling Chen (firstname.lastname@example.org) is Gordon McKay Professor of Computer Science at Harvard University, Cambridge, MA. Arpita Ghosh (email@example.com) is an associate professor of information science at Cornell University, Ithaca, NY. Michael Kearns (firstname.lastname@example.org) is a professor and National Center Chair of Computer and Information Science at the University of Pennsylvania, Philadelphia, PA. Tim Roughgarden (email@example.com) is an associate professor of CS at Stanford University, Stanford, CA. Jennifer Wortman Vaughan (firstname.lastname@example.org) is a senior researcher at Microsoft Research, New York, NY.
Thomas G. Dietterich Arris Pharmaceutical Corporation and Oregon State University Corvallis, OR 97331-3202 Ajay N. Jain Arris Pharmaceutical Corporation 385 Oyster Point Blvd., Suite 3 South San Francisco, CA 94080 Richard H. Lathrop and Tomas Lozano-Perez Arris Pharmaceutical Corporation and MIT Artificial Intelligence Laboratory 545 Technology Square Cambridge, MA 02139 Abstract In drug activity prediction (as in handwritten character recognition), thefeatures extracted to describe a training example depend on the pose (location, orientation, etc.) of the example. In handwritten characterrecognition, one of the best techniques for addressing thisproblem is the tangent distance method of Simard, LeCun and Denker (1993). Jain, et al. (1993a; 1993b) introduce a new technique-dynamic reposing-that also addresses this problem. Dynamicreposing iteratively learns a neural network and then reposes the examples in an effort to maximize the predicted output values.New models are trained and new poses computed until models and poses converge. This paper compares dynamic reposing to the tangent distance method on the task of predicting the biological activityof musk compounds.
Data analytics is becoming a cornerstone of the financial industry, with startups and established financial service firms looking to give investors clearer guidance with information collected and captured from multiple sources. Advances in machine learning and artificial intelligence (AI) in particular are providing greater insights and better customer experiences. AI-powered data analytics not only captures vast amounts of data in real-time, but also helps users understand how different data points relate to each other, providing insights that might otherwise be lost. Faced with a breakdown in brand loyalty as younger customers prioritize user experience, financial services are now racing to leverage data-driven cognitive technologies. Cambridge, MA-based Kensho, which recently received 58 million in funding from Goldman Sachs, San Francisco-based Alphasense, backed by Tribeca Venture Partners, and Toronto-based Bigterminal are some of the fintech players leveraging AI.