Why did companies like Intuit, JP Morgan Chase, MasterCard, and BuzzFeed deploy AI and what business advantages have they already reaped from those deployments? How have the toolsets recently developed at Google (BERT) and Microsoft (Project Brainwave) opened up AI as a mainstream business reality? How does IBM's AI Fairness 360 toolkit combat the very real problem of unwanted bias in AI applications? You'll find the answers to these questions and many more in this video compilation of the best talks from AI New York 2019. Containing hours of material to explore at your own pace, this video compilation provides an insider's view of the latest developments in AI.
- Complete video recordings of the talks delivered at AI NY 2019 by 170 of the world's top AI experts.
- Keynote addresses from AI's best thinkers, such as MIT's Aleksander Madry, Intuit's Desiree Gosby, Princeton University's Olga Troyanskaya, Netflix's Tony Jebara, Stanford University's Christopher Ré, Facebook's Kim Hazelwood, Carnegie Mellon University's Martial Hebert, and Primer's Sean Gourley, plus a look at Dell's "Sophia", the world’s first robot citizen.
- All of the Executive Briefings and detailed case studies from the exclusive AI Business Summit, including Kristian Hammond's (Northwestern Computer Science) day long tutorial offering a practical framework for bringing AI into your company; Adam Cheyer's (Samsung) look at how AI enables a totally new form of software development where humans and machines work collaboratively together; and Jennifer Fernick's (NCC Group) learned predictions of the industries that will benefit from the coming intersection of quantum computing, machine learning, and AI.
- Tutorials by AI's most experienced practitioners, including Gunnar Carlsson (Stanford University) on using topological data analysis to understand, build, and improve neural networks; Bruno Goncalves (JPMorgan Chase) on using recurrent neural networks for time series analysis; and Mo Patel (Independent) on how to build machine learning models in PyTorch.
- Sessions focused on machine learning, including Alina Matyukhina's (Canadian Institute for Cybersecurity) reveal of the methods dishonest actors use ML to mimic the coding style of software developers in open source projects; Chakri Cherukuri's (Bloomberg LP) discussion of how to apply machine learning and deep learning techniques in quantitative finance; and Cibele Montez Halasz's (Twitter) description of timeline ranking and how it's used to implement ML at production scale.
- Sessions devoted to implementing AI, including Yu Dong (Facebook) on how to build a production-scale ML platform; Sanjian Chen (Alibaba Group) on the best practices for designing machine learning algorithms for retail data analysis; and Magnus Hyttsten (Google) on how to run distributed TensorFlow on CPUs, GPUs, and TPUs with Keras and estimator APIs.
- Sessions illuminating new AI models and methods, including Ameet Talwalkar (Carnegie Mellon University) on NAS (neural architecture search); Lise Getoor's (University of California, Santa Cruz) exploration into SRL (statistical relational learning); and Siwei Lyu (University of Albany) on recent advances in techniques used to detect AI-generated DeepFake videos.