Programming collective intelligence for financial trading
The O’Reilly Data Show Podcast: Geoffrey Bradway on building a trading system that synthesizes many different models.
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).
When will automation reach your industry?
How to understand machine learning adoption in the enterprise.
Creating large training data sets quickly
The O’Reilly Data Show Podcast: Alex Ratner on why weak supervision is the key to unlocking dark data.
What is machine learning debt?
Overcome three types of debt to ship quality machine learning code.
What are machine learning engineers?
A new role focused on creating data products and making data science work in production.
The machine learning paradox
Nothing says machine learning can't outperform humans, but it's important to realize perfect machine learning doesn't, and won't, exist.
Peeking into the black box: Lessons from the front lines of machine-learning product launches
Grace Huang shares lessons learned from running and interpreting machine-learning experiments.
Using AI to create new jobs
Tim O’Reilly delves into past technological transitions, speculates on the possibilities of AI, and looks at what's keeping us from making the right choices to govern our creations.
Is finance ready for AI?
Aida Mehonic explores the role artificial intelligent might play in the financial world.
Data science and deep learning in retail
The O’Reilly Data Show Podcast: Jeremy Stanley on hiring and leading machine learning engineers to build world-class data products.
Jason Laska and Michael Akilian on using AI to schedule meetings
The O’Reilly Bots Podcast: The technical and social dynamics of solving scheduling problems.
Highlights from Strata Data Conference in London 2017
Watch highlights covering data-driven business, data engineering, machine learning, and more. From Strata Data Conference in London 2017.
Language understanding remains one of AI’s grand challenges
The O’Reilly Data Show Podcast: David Ferrucci on the evolution of AI systems for language understanding.
Chris Messina on Facebook as a utility
The O’Reilly Bots Podcast: The social impact of Facebook.
Data preparation in the age of deep learning
The O’Reilly Data Show Podcast: Lukas Biewald on why companies are spending millions of dollars on labeled data sets.
Bringing interactive BI to big data
A look at Apache Kylin’s architecture and features in version 2.0.
Scaling machine learning
The O’Reilly Data Show Podcast: Reza Zadeh on deep learning, hardware/software interfaces, and why computer vision is so exciting.
Tom Hadfield on bots in the enterprise
Messaging as the operating system for the enterprise.
Architecting and building end-to-end streaming applications
The O’Reilly Data Show Podcast: Karthik Ramasamy on Heron, DistributedLog, and designing real-time applications.
Prabhat on deep learning for science
The O’Reilly Bots Podcast: Solutions from big data sets.
Becoming a machine learning engineer
The O’Reilly Data Show Podcast: Aurélien Géron on enabling companies to use machine learning in real-world products.
Adapting ideas from neuroscience for AI
Inspiration from the brain is extremely relevant to AI; it’s time we pushed it further.
Natural language analysis using Hierarchical Temporal Memory
The O’Reilly Data Show Podcast: Francisco Webber on building HTM-based enterprise applications.
On computational ethics
Is it possible to imagine an AI that can compute ethics?