Highlights from the O’Reilly Artificial Intelligence Conference in San Jose 2019

Experts discuss new trends, tools, and issues in artificial intelligence and machine learning.

By Mac Slocum
September 11, 2019
O'Reilly AI Conference San Jose 2019

People from across the artificial intelligence (AI) world came together in San Jose, Calif. for the O’Reilly Artificial Intelligence Conference. Below you’ll find links to highlights from the event.

Open-endedness: A new grand challenge for AI

Kenneth Stanley discusses how open-ended algorithms can offer an entirely different level of automated creation.

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Going beyond fully supervised learning

Srinivas Narayanan takes a deep look into the next change we’re seeing in AI—going beyond fully supervised learning techniques.

Practical insights into deep reinforcement learning

Sahika Genc dives deep into state-of-the-art techniques in deep reinforcement learning for a variety of use cases.

Enabling AI’s potential through wafer-scale integration

Andrew Feldman discusses the Wafer Scale Engine, the largest chip ever built.

On gradient-based methods for finding game-theoretic equilibria

Michael Jordan details several recent results that blend gradient-based methodology with game-theoretic goals.

Getting from A to AI

Eric Gardner shares a four-step journey that all kinds of organizations can use to evaluate their unique paths from data to insights.

Developing AI responsibly

Sarah Bird discusses the major challenges of responsible AI development and examines promising new tools and technologies to help enable it in practice.

Accelerate with purpose

Ananth Sankaranarayanan discusses three three key shifts in the AI landscape, how to navigate them, and when to explore hardware acceleration.

Unlocking the value of your data

Dinesh Nirmal examines how organizations can unlock the value of their data for AI with a unified, prescriptive information architecture.

Safe and smarter driving, powered by AI

Lei Pan explores how Nauto uses AWS to continually evolve smarter data for driver behavior.

AI for ophthalmology: Doing what doctors can’t

Daniel Russakoff discusses how AI is being used to predict age-related macular degeneration progression.

Post topics: AI & ML

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