Today, we live in a data-driven world and new directions in data-driven research have already revolutionized big data applications such as gaming, Internet vision, machine translation, and spell checking by bringing machine learning to the core of the information revolution. But to continue to drive innovation, three fundamental aspects of data-driven research need to change: real world large scale data must be made available to the research community to allow for benchmarking, reproducibility and transparency of experimentation need to happen to enable science, and researchers need to be able to share their work on solving big and real world problems to advance the state of the art. In this talk I will talk about all three aspects of data-driven research by exploring the status of data and code sharing, and will present approaches and tools to help with the democratization of large scale data-driven research.
I will end the talk with examples of machine learning and artificial intelligence services.
Bio: Evelyne Viegas is the director of Artificial Intelligence at Microsoft Research Redmond. In her current role, she is building initiatives that focus on information seen as an enabler of innovation, working in partnership with universities and government agencies worldwide. In particular, she is creating programs around computational-intelligence research to drive open innovation and agile experimentation via cloud-based services, and projects to advance the state of the art in machine learning, knowledge representation and reasoning under uncertainty at web scale.
Before her present role, Evelyne Viegas worked as a technical lead at Microsoft, delivering natural language processing components to projects for MSN, Office, and Windows. Before Microsoft, and after completing her Ph.D. in France, she worked as a principal investigator at the Computing Research Laboratory in New Mexico on an ontology-based machine-translation project. She serves on international editorial, program, and award committees.