Highlights from the O’Reilly Artificial Intelligence Conference in Beijing 2019
Experts explore the future of hiring, AI breakthroughs, embedded machine learning, and more.
All Articles
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.
AI and retail
Mikio Braun takes a look at Zalando and the retail industry to explore how AI is redefining the way ecommerce sites interact with customers.
Data orchestration for AI, big data, and cloud
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.
Top AI breakthroughs you need to know
Abigail Hing Wen discusses some of the most exciting recent breakthroughs in AI and robotics.
AI and systems at RISELab
Ion Stoica outlines a few projects at the intersection of AI and systems that UC Berkeley's RISELab is developing.
The future of machine learning is tiny
Pete Warden digs into why embedded machine learning is so important, how to implement it on existing chips, and some of the new use cases it will unlock.
The future of hiring and the talent market with AI
Maria Zheng examines AI and its impact on people’s jobs, quality of work, and overall business outcomes.
Four short links: 8 July 2019
Algorithmic Governance, DevOps Assessment, Retro Language, and Open Source Satellite
Four short links: 5 July 2019
Online Not All Bad, Emotional Space, Ted Chiang, Thread Summaries
Four short links: 4 July 2019
Debugging AI, Serverless Foundations, YouTube Bans, and Pathological UI
Four short links: 3 July 2019
Models, More Models, robots.txt, and Event Sourcing
Four short links: 2 July 2019
Lock Convoys, AI Hardware, Lambda Observability, and AI for Science
RISELab’s AutoPandas hints at automation tech that will change the nature of software development
Neural-backed generators are a promising step toward practical program synthesis.
Four short links: 1 July 2019
General-Purpose Probabilistic Programming, Microsoft's Linux, Decolonizing Data, Testing Statistical Software
Four short links: 28 June 2019
Heartbeat Identity, Seam Carving, Q&A Facilitation, and Secure Data in Distributed Systems
How to become a truly data-driven organization
From basic BI to using AI to automate and augment human endeavors, data-driven systems are increasingly powerful and pervasive in the enterprise.