Open-endedness: A new grand challenge for AI
Kenneth Stanley discusses how open-ended algorithms can offer an entirely different level of automated creation.
AI & ML
Few technologies have the potential to change the nature of work and how we live as artificial intelligence (AI) and machine learning (ML).
Understanding deep neural networks
The O’Reilly Data Show Podcast: Michael Mahoney on developing a practical theory for deep learning.
AI for ophthalmology: Doing what doctors can’t
Daniel Russakoff discusses how AI is being used to predict age-related macular degeneration progression.
Enabling AI’s potential through wafer-scale integration
Andrew Feldman discusses the Wafer Scale Engine, the largest chip ever built.
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.
O'Reilly AI Conference keynote highlights
Experts discuss new trends, tools, and issues in artificial intelligence and machine learning.
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.
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.
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.
Topics to watch at Strata NY '19
Machine learning, artificial intelligence, data engineering, and architecture are driving the data space.
How new data and AI tools are being used in health care
An overview of applications of new tools for overcoming silos, and for creating and sharing high-quality data.
ML needs different deployment
As organizations embrace machine learning, the need for new deployment tools and strategies grows.
Becoming a machine learning practitioner
The O’Reilly Data Show Podcast: Kesha Williams on how she added machine learning to her software developer toolkit.
Organizations are sharpening their AI skills
To successfully implement AI technologies, companies need to take a holistic approach toward retraining their workforces.
Labeling, transforming, and structuring training data sets for machine learning
The O’Reilly Data Show Podcast: Alex Ratner on how to build and manage training data with Snorkel.
Guidelines for building voice applications
Speech adds another level of complexity to AI applications—today’s voice applications provide a very early glimpse of what is to come.
Make data science more useful
The O’Reilly Data Show Podcast: Cassie Kozyrkov on connecting data and AI to business.
Learning from adversaries
Adversarial images aren’t a problem—they’re an opportunity to explore new ways of interacting with AI.
Acquiring and sharing high-quality data
The O’Reilly Data Show Podcast: Roger Chen on the fair value and decentralized governance of data.
Managing machine learning in the enterprise: Lessons from banking and health care
A look at how guidelines from regulated industries can help shape your ML strategy.
The circle of fairness
We shouldn't ask our AI tools to be fair; instead, we should ask them to be less unfair and be willing to iterate until we see improvement.
Highlights from the O’Reilly Artificial Intelligence Conference in Beijing 2019
Experts explore the future of hiring, AI breakthroughs, embedded machine learning, and more.
Toward learned algorithms, data structures, and systems
Tim Kraska outlines ways to build learned algorithms and data structures to achieve “instance optimality” and unprecedented performance for a wide range of applications.
Designing computer hardware for artificial intelligence
Michael James examines the fundamental drivers of computer technology and surveys the landscape of AI hardware solutions.