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).
Data is often biased. But that isn’t the real issue. Why is it biased? How do we build teams that are sensitive to that bias?
A Bad Outcome Doesn't Mean a Bad Decision
Getting curious about the numbers attached to other people can help us to use data wisely—and to see others clearly.
O’Reilly usage analysis shows continued growth in AI/ML and early signs that organizations are experimenting with advanced tools and methods.
O’Reilly survey results show that AI efforts are maturing from prototype to production, but company support and an AI/ML skills gap remain obstacles.
Our annual analysis of the O’Reilly online learning platform reveals Python’s continued dominance and important shifts in infrastructure, AI/ML, cloud, and security.
O’Reilly survey highlights the increasing attention organizations are giving to data quality and how AI both exacerbates and alleviates data quality issues.
Edward Jezierski on the science of bringing creativity and curiosity together in a learning system.
Roger Magoulas looks at developments in automation, hardware, tools, model development, and more that will shape (or accelerate) AI in 2020.
Rob Thomas and Tim O’Reilly discuss the AI Ladder framework.
Understanding and fixing problems in ML models is critical for widespread adoption.
It’s clear that AI can and will have a big influence on how we develop software.
Dean Wampler discusses the challenges and opportunities businesses face when moving AI from discussions to production.
Ankur Patel discusses challenges and opportunities in enterprise machine learning and AI applications.