Enabling AI’s potential through wafer-scale integration
Andrew Feldman discusses the Wafer Scale Engine, the largest chip ever built.
Few technologies have the potential to change the nature of work and how we live as artificial intelligence (AI) and machine learning (ML).
Andrew Feldman discusses the Wafer Scale Engine, the largest chip ever built.
Eric Gardner shares a four-step journey that all kinds of organizations can use to evaluate their unique paths from data to insights.
Experts discuss new trends, tools, and issues in artificial intelligence and machine learning.
Srinivas Narayanan takes a deep look into the next change we’re seeing in AI—going beyond fully supervised learning techniques.
Sarah Bird discusses the major challenges of responsible AI development and examines promising new tools and technologies to help enable it in practice.
Dinesh Nirmal examines how organizations can unlock the value of their data for AI with a unified, prescriptive information architecture.
Machine learning, artificial intelligence, data engineering, and architecture are driving the data space.
An overview of applications of new tools for overcoming silos, and for creating and sharing high-quality data.
As organizations embrace machine learning, the need for new deployment tools and strategies grows.
The O’Reilly Data Show Podcast: Kesha Williams on how she added machine learning to her software developer toolkit.
To successfully implement AI technologies, companies need to take a holistic approach toward retraining their workforces.
The O’Reilly Data Show Podcast: Alex Ratner on how to build and manage training data with Snorkel.
Speech adds another level of complexity to AI applications—today’s voice applications provide a very early glimpse of what is to come.
The O’Reilly Data Show Podcast: Cassie Kozyrkov on connecting data and AI to business.
Adversarial images aren’t a problem—they’re an opportunity to explore new ways of interacting with AI.
The O’Reilly Data Show Podcast: Roger Chen on the fair value and decentralized governance of data.
A look at how guidelines from regulated industries can help shape your ML strategy.
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.
Experts explore the future of hiring, AI breakthroughs, embedded machine learning, and more.
Tim Kraska outlines ways to build learned algorithms and data structures to achieve “instance optimality” and unprecedented performance for a wide range of applications.
Michael James examines the fundamental drivers of computer technology and surveys the landscape of AI hardware solutions.
Mikio Braun takes a look at Zalando and the retail industry to explore how AI is redefining the way ecommerce sites interact with customers.
Haoyuan Li offers an overview of a data orchestration layer that provides a unified data access and caching layer for single cloud, hybrid, and multicloud deployments.
Abigail Hing Wen discusses some of the most exciting recent breakthroughs in AI and robotics.