Trends in data, machine learning, and AI
The O’Reilly Data Show Podcast: Ben Lorica looks ahead at what we can expect in 2019 in the big data landscape.
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).
Tools for generating deep neural networks with efficient network architectures
The O’Reilly Data Show Podcast: Alex Wong on building human-in-the-loop automation solutions for enterprise machine learning.
Building tools for enterprise data science
The O’Reilly Data Show Podcast: Vitaly Gordon on the rise of automation tools in data science.
Managing risk in machine learning
Considerations for a world where ML models are becoming mission critical.
Lessons learned while helping enterprises adopt machine learning
The O’Reilly Data Show Podcast: Francesca Lazzeri and Jaya Mathew on digital transformation, culture and organization, and the team data science process.
A new vision for the global brain: Deep learning with people instead of machines
Omoju Miller outlines a vision where we harness human action for a better future.
Machine learning on encrypted data
The O’Reilly Data Show Podcast: Alon Kaufman on the interplay between machine learning, encryption, and security.
Bringing AI into the enterprise: A functional approach to the technologies of intelligence
Kristian Hammond maps out simple rules, useful metrics, and where AI should live in the org chart.
Building artificial people: Endless possibilities and the dark side
Supasorn Suwajanakorn discusses the possibilities and the dark side of building artificial people.
Fireside chat with Marc Warner and Louis Barson
Marc Warner and Louis Barson discuss the internal and external uses of AI in the UK government.
Deep learning at scale: A field manual
Jason Knight offers an overview of the state of the field for scaling training and inference across distributed systems.
The missing piece
Cassie Kozyrkov shares machine learning lessons learned at Google and explains what they mean for applied data science.
Notes from the frontier: Making AI work
Drawing on the McKinsey Global Institute’s research, Michael Chui explores commonly asked questions about AI and its impact on work.
How social science research can inform the design of AI systems
The O’Reilly Data Show Podcast: Jacob Ward on the interplay between psychology, decision-making, and AI systems.
Highlights from the Artificial Intelligence Conference in London 2018
Watch highlights from expert talks covering artificial intelligence, machine learning, automation, and more.
Rethinking software engineering in the AI era
Yangqing Jia talks about what makes AI software unique and its connections to conventional computer science wisdom.
AI in production: The droids you’re looking for
Jonathan Ballon explains why Intel’s AI and computer vision edge technology will drive advances in machine learning and natural language processing.
The state of automation technologies
Ben Lorica and Roger Chen highlight recent trends in data, compute, and machine learning.
Why we built a self-writing Wikipedia
Amy Heineike explains how Primer created a self-updating knowledge base that can track factual claims in unstructured text.
AI for a better world
Ashok Srivastava draws upon his cross-industry experience to paint an encouraging picture of how AI can solve big problems.
What we learn from AI’s biases
Our bad AI could be the best tool we have for understanding how to be better people.
ML on code: Machine learning will change programming
Francesc Campoy Flores explores ways machine learning can help developers be more efficient.
Why it’s hard to design fair machine learning models
The O’Reilly Data Show Podcast: Sharad Goel and Sam Corbett-Davies on the limitations of popular mathematical formalizations of fairness.
Practical ML today and tomorrow
Hilary Mason explores the current state of AI and ML and what’s coming next in applied ML.