In this talk I will discuss recent trends and developments in deep learning research. I’ll then touch on the significant progress that this research has produced in a number of areas, including computer vision, language understanding, translation, healthcare, and robotics. These advances are driven by both new algorithmic approaches to some of these problems, and by the ability to scale computation for training ever large models on larger datasets. I’ll discuss both of these issues. Finally, one of the reasons for the rapid spread of the ideas and techniques of deep learning has been the availability of open source libraries such as TensorFlow. I’ll give an overview of why these software libraries have an important role in making the benefits of machine learning available throughout the world.
Stanford University Professor Oussama Khatib and CEO of Affectiva, Rana el Kaliouby, discuss the future of human-robot collaboration.
As Machine Learning (ML) systems are increasingly becoming part of user-facing applications, their reliability and robustness are key to building and maintaining trust with users and customers, especially for high-stake domains. While advances in learning are continuously improving model performance on expectation, there is an emergent need for identifying, understanding, and mitigating cases where models may fail in unexpected ways. This session is going to discuss ML reliability and robustness from both a theoretical and empirical perspective. In particular, the session will aim at summarizing important ongoing work that focuses on reliability guarantees but also on how such guarantees translate (or not) to real-world applications. Further, the talks and the panel will aim at discussing (1) properties of ML algorithms that make them more preferable than others from a reliability and robustness lens such as interpretability, consistency, transportability etc. and (2) tooling support that is needed for ML developers to check and build for reliable and robust ML. The discussion will be grounded on real-world applications of ML in vision and language tasks, healthcare, and decision making. Session Lead: Besmira Nushi, Microsoft Speaker: Thomas Dietterich, Oregon State University Talk Title: Anomaly Detection in Machine Learning and Computer Vision Speaker: Ece Kamar, Microsoft Talk Title: AI in the Open World: Discovering Blind Spots of AI Speaker: Suchi Saria, Johns Hopkins University Talk Title: Implementing Safe & Reliable ML: 3 key areas of development Q&A panel with all 3 speakers See more on-demand sessions from Microsoft Research’s Frontiers in Machine Learning 2020 [More]
Anima Anandkumar (NVIDIA): Large-scale Machine Learning: Deep, Distributed and Multi-Dimensional