Neural Information Processing Systems http://nips.cc/

Since its inauguration in 1966, the ACM A.M. Turing Award has recognized major contributions of lasting importance to computing. Through the years, it has become the most prestigious award in computing. To help celebrate 50 years of the ACM Turing Award and the visionaries who have received it, ACM has launched a campaign called "Panels in Print," which takes the form of a collection of responses from Turing laureates, ACM award recipients and other ACM experts on a given topic or trend. For our fourth and final Panel in Print, we invited 2014 ACM A.M. Turing Award recipient MICHAEL STONEBRAKER, 2013 ACM Prize recipient DAVID BLEI, 2007 ACM Prize recipient DAPHNE KOLLER, and ACM Fellow VIPIN KUMAR to discuss trends in big data. Gartner estimates that there are currently about 4.9 billion connected devices (cars, homes, appliances, industrial equipment, among others) generating data.

Want to learn about applied Artificial Intelligence from leading practitioners in Silicon Valley or New York? That is one way to describe the thirteenth annual Neural Information Processing Systems (NIPS) Conference. Packed with excitement, packed with results, and packed with people. This year over 2500 top quality papers where submitted, and over 5000 people are in attendance. We too are in attendance for the week, and are excited to talk with the top scientists and teams to hear what their currently doing.

David Blei is a Professor in the Statistics and Computer Science departments at Columbia University. Prior to fall 2014 he was an Associate Professor in the Department of Computer Science at Princeton University. His work is primarily in machine learning. His research interests include topic models and he was one of the original developers of latent Dirichlet allocation. As of November 11, 2015, his publications have been cited 31,135 times, giving him an h-index of 53.[1]

Edward is a Python library for probabilistic modeling, inference, and criticism. It enables black box inference for models with discrete and continuous latent variables, neural network parameterizations, and infinite dimensional parameter spaces. Edward serves as a fusion of three fields: Bayesian statistics and machine learning, deep learning, and probabilistic programming. You can find a tutorial here for getting started with Edward, as well as a tutorial here for how to use it for research. Edward is led by Dustin Tran with guidance by David Blei.