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).
Reclaiming the stories that algorithms tell
Getting curious about the numbers attached to other people can help us to use data wisely—and to see others clearly.
What to Do When AI Fails
Practical Skills for The AI Product Manager
When models are everywhere
How data privacy leader Apple found itself in a data ethics catastrophe
Companies that succeed will protect, fight for, and empower their users
What you need to know about product management for AI
A product manager for AI does everything a traditional PM does, and much more.
The unreasonable importance of data preparation
Your models are only as good as your data.
6 trends framing the state of AI and ML
O’Reilly usage analysis shows continued growth in AI/ML and early signs that organizations are experimenting with advanced tools and methods.
AI adoption in the enterprise 2020
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.
5 key areas for tech leaders to watch in 2020
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.
The state of data quality in 2020
O’Reilly survey highlights the increasing attention organizations are giving to data quality and how AI both exacerbates and alleviates data quality issues.
Reinforcement learning for the real world
Edward Jezierski on the science of bringing creativity and curiosity together in a learning system.
8 AI trends we’re watching in 2020
Roger Magoulas looks at developments in automation, hardware, tools, model development, and more that will shape (or accelerate) AI in 2020.
AI is computer science disguised as hard work
Rob Thomas and Tim O’Reilly discuss the AI Ladder framework.
Why debugging ML models matters
Understanding and fixing problems in ML models is critical for widespread adoption.
AI and the road to Software 2.0
It’s clear that AI can and will have a big influence on how we develop software.
Moving AI and ML from research into production
Dean Wampler discusses the challenges and opportunities businesses face when moving AI from discussions to production.
There’s a path to an AI ROI
Ankur Patel discusses challenges and opportunities in enterprise machine learning and AI applications.
“AI is a lie”
Eric Jonas on AI hype and questions of ethics.
Dealing with a world of deepfakes
We need to remember that creating fakes is an application, not a tool—and that malicious applications are not the whole story.
Highlights from TensorFlow World 2019
Experts explore TensorFlow 2.0's machine learning capabilities as well as the broader tools and applications of TensorFlow.
Sticker recommendations and AI-driven innovations on the Hike messaging platform
Ankur Narang discusses sticker recommendations with multilingual support, a key innovation driven by sophisticated natural language processing (NLP) algorithms.
“Human error”: How can we help people build models that do what they expect
Anna Roth discusses human and technical factors and suggests future directions for training machine learning models.