Highlights from the O’Reilly Artificial Intelligence Conference in New York 2019

Watch highlights from expert talks covering AI, machine learning, deep learning, ethics, and more.

By Mac Slocum
April 17, 2019
New York skyline - AI Conference in NY New York skyline - AI Conference in NY

People from across the AI world came together in New York for the O’Reilly Artificial Intelligence Conference. Below you’ll find links to highlights from the event.

AI and the robotics revolution

Martial Hebert offers an overview of challenges in AI for robotics and a glimpse at the exciting developments emerging from current research.

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Applied machine learning at Facebook

Kim Hazelwood discusses the hardware and software Facebook has designed to meet its scale needs.

Decoding the human genome with deep learning

How can machine learning decode the mysteries of life? Olga Troyanskaya explores this and other big questions through the prism of deep learning.

Computational propaganda

Sean Gourley considers the repercussions of AI-generated content that blurs the line between what’s real and what’s fake.

Software 2.0 and Snorkel

Christopher Ré discusses Snorkel, a system for fast training data creation.

Is AI human-ready?

Aleksander Madry discusses roadblocks preventing AI from having a broad impact and approaches for addressing these issues.

Machine learning for personalization

Tony Jebara explains how Netflix is personalizing and optimizing the images shown to subscribers.

Checking in on AI tools

Ben Lorica and Roger Chen assess the state of AI technologies and adoption in 2019.

Fast, flexible, and functional: 4 real-world AI deployments at enterprise scale

Gadi Singer discusses the major questions organizations confront as they integrate deep learning.

Making real-world distributed deep learning easy with Nauta

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.

Automated ML: A journey from CRISPR.ML to Azure ML

Danielle Dean explains how cloud, data, and AI came together to help build Automated ML.

Toward ethical AI: Inclusivity as a messy, difficult, but promising answer

Kurt Muehmel explores AI within a broader discussion of the ethics of technology, arguing that inclusivity and collaboration are necessary.

How AI adaptive technology can upgrade education

Joleen Liang explains how AI and precise knowledge points can help students learn.

Artificial intelligence: The “refinery” for data

Nick Curcuru explains how Mastercard is using AI to improve security without sacrificing the customer experience.

Automation of AI: Accelerating the AI revolution

Ruchir Puri discusses the next revolution in automating AI, which strives to deploy AI to automate the task of building, deploying, and managing AI tasks.

Simple, scalable, and sustainable: A methodical approach to AI adoption

Rajendra Prasad explains how leaders in large enterprises can make AI adoption successful.

Data fueling AI of the future

Thomas Henson considers how AI will shape the experiences of future generations.

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