James Burke asks if we can use data and predictive analytics to take the guesswork out of prediction.
Watch highlights from expert talks covering machine learning, predictive analytics, data regulation, and more.
More than anything else, O'Reilly's AI Conference was about making the leap to AI 2.0.
The O’Reilly Data Show Podcast: Neelesh Salian on data lineage, data governance, and evolving data platforms.
Rajendra Prasad explains how leaders in large enterprises can make AI adoption successful.
Christopher Ré discusses Snorkel, a system for fast training data creation.
Sean Gourley considers the repercussions of AI-generated content that blurs the line between what's real and what's fake.
Nick Curcuru explains how Mastercard is using AI to improve security without sacrificing the customer experience.
How can machine learning decode the mysteries of life? Olga Troyanskaya explores this and other big questions through the prism of deep learning.
Kim Hazelwood discusses the hardware and software Facebook has designed to meet its scale needs.
Carlos Humberto Morales offers an overview of Nauta, an open source multiuser platform that lets data scientists run complex deep learning models on shared hardware.
Thomas Henson considers how AI will shape the experiences of future generations.
Kurt Muehmel explores AI within a broader discussion of the ethics of technology, arguing that inclusivity and collaboration are necessary.
Danielle Dean explains how cloud, data, and AI came together to help build Automated ML.
Gadi Singer discusses the major questions organizations confront as they integrate deep learning.
Tony Jebara explains how Netflix is personalizing and optimizing the images shown to subscribers.
Ben Lorica and Roger Chen assess the state of AI technologies and adoption in 2019.
Aleksander Madry discusses roadblocks preventing AI from having a broad impact and approaches for addressing these issues.