How machine learning can be used to write more secure computer programs
The O’Reilly Data Show Podcast: Fabian Yamaguchi on the potential of using large-scale analytics on graph representations of code.
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).
4 tips to stop reading about AI and start doing AI
How to use AI as a tool in your business.
Machine learning tools for fairness, at scale
It’s time to think about how the systems we build interact with our world and build systems that make our world a better place.
Tips for jumpstarting your journey to developing AI apps
Use cases and tips to help businesses take full advantage of AI technology.
We need to build machine learning tools to augment machine learning engineers
As the use of analytics proliferate, companies will need to be able to identify models that are breaking bad.
5 AI trends to watch in 2018
From methods to tools to ethics, Ben Lorica looks at what's in store for artificial intelligence.
Bringing AI into the enterprise
The O’Reilly Data Show Podcast: Kris Hammond on business applications of AI technologies and educating future AI specialists.
The problem with building a “fair” system
The ability to appeal may be the most important part of a fair system, and it's one that isn't often discussed in data circles.
Uncovering hidden patterns through machine learning
Lessons from FizzBuzz for Apache MXNet.
How machine learning will accelerate data management systems
The O’Reilly Data Show Podcast: Tim Kraska on why ML will change how we build core algorithms and data structures.
The artificial intelligence computing stack
A look at why the U.S. and China are investing heavily in this new computing stack.
Open-endedness: The last grand challenge you’ve never heard of
While open-endedness could be a force for discovering intelligence, it could also be a component of AI itself.
The state of AI adoption
An overview of adoption, and suggestions to companies interested in AI technologies.
Practical applications of reinforcement learning in industry
An overview of commercial and industrial applications of reinforcement learning.
Can AI create a new game? A challenge.
Since AI's most amazing advances have been in playing games, it seems fitting that the creative challenge should involve creating games.
Highlights from Strata Data Conference in Singapore 2017
Watch highlights covering machine learning, smart cities, automation, and more. From Strata Data Conference in Singapore 2017.
The sixth wave: Automation of decisions
Amr Awadallah explains the historic importance of the next wave in automation.
Sentiment and emotion-aware natural language processing
Pascale Fung explains how emotional interaction is being integrated into machines.
Machine learning at Spotify: You are what you stream
The O’Reilly Data Show Podcast: Christine Hung on using data to drive digital transformation and recommenders that increase user engagement.
Responsible deployment of machine learning
Ben Lorica explains how to guard against flaws and failures in your machine learning deployments.
The current state of Apache Kafka
The O’Reilly Data Show Podcast: Neha Narkhede on data integration, microservices, and Kafka’s roadmap.
Building a natural language processing library for Apache Spark
The O’Reilly Data Show Podcast: David Talby on a new NLP library for Spark, and why model development starts after a model gets deployed to production.
Machine intelligence for content distribution, logistics, smarter cities, and more
The O’Reilly Data Show Podcast: Rhea Liu on technology trends in China.
How companies can navigate the age of machine learning
To become a “machine learning company,” you need tools and processes to overcome challenges in data, engineering, and models.