Schedule: Open Data, Data Generation and Data Networks sessions

Jeremy Howard describes how to leverage the latest research from the deep learning and HCI communities to train neural networks from scratch—without code or preexisting labels. He then shares case studies in fashion, retail and ecommerce, travel, and agriculture where these approaches have been used. Read more.

Data remains a linchpin of success for machine learning yet too often is a scarce resource. And even when data is available, trust issues arise about the quality and ethics of collection. Roger Chen explores new models for generating and governing training data for AI applications. Read more.

At the rate data sources are multiplying, business value can often be developed faster by joining data sources rather than mining a single source to the very end. Ken Johnston and Ankit Srivastava share four years of hands-on practical experience sourcing and integrating massive numbers of data sources to build the Microsoft Business Intelligence Graph (M360 BIG). Read more.

Data sharing necessitates stakeholders and populations of people to come together to learn the benefits, risks, challenges, and known and unknown "unknowns." Data sharing policies and frameworks require increasing levels of trust, which takes time to build. Join Mei Fung for trail-blazing stories from Solano County, California, and ASEAN (SE Asia), which offer important insights Read more.