Humongous Scale Machine Learning: Technical and Commercial Challenges Lead to Successes
Following last year’s Large Scale machine learning case, I will be presenting the latest machine learning systems for large-scale engineering and commercialization at this summit, based on the experiences of Google Brain and Princeton University.
The comprehensive engineering design principles, server-side software, hardware requirements, client-side deployment and commercialization will be discussed.
Following last year’s self-driving case, this talk will include audio, image, video, and NLP. In particular, this course includes the commercialization of machine learning in consideration of the nature of events.
Dr. Yoram Singer Google Brain Principal Research Scientist, Google & Professor, Princeton University
Dr. Yoram was a senior scientist at Google Brain and is a professor of computer science at Princeton University. From 1999 to 2007 he was an associate professor at the Hebrew University of Jerusalem in Israel. From 1995 to 1999, he worked at the AT & T lab. He was co-chair of the 2004 Computational Learning Theory (COLT) conference and the 2007 Neural Information Processing System (NIPS) conference.
He has been published in Machine Learning Journal (MLJ), Journal of Machine Learning (JMLR), IEEE Signal Processing Magazine (SPM), and IEEE Trans. He has been editor-in-chief of several specialized magazines, including on Pattern Analysis and Machine Intelligence (PAMI). Dr. Yoram has been a fellow of the National Institute of Artificial Intelligence (AAAI) since 2011.
He recently gave a lecture on machine learning at the World Economic Forum.