Adapting ideas from neuroscience for AI
Inspiration from the brain is extremely relevant to AI; it’s time we pushed it further.
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).
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?
Ray: A distributed execution framework for emerging AI applications
Michael I. Jordan explores applications in artificial intelligence.
Machine learning at Google
Rob Craft shares some of the ways machine learning is being used inside of Google.
The machine learning renaissance
Mike Olson says without big data and a platform to manage big data, machine learning and artificial intelligence just don’t work.
Highlights from Strata + Hadoop World in San Jose 2017
Watch highlights covering data science, data engineering, data-driven business, and more. From Strata + Hadoop World in San Jose 2017.
Applying data and machine learning to scale education
Daphne Koller explains how Coursera is using large-scale data processing and machine learning in online education.
Ideas on interpreting machine learning
Mix-and-match approaches for visualizing data and interpreting machine learning models and results.
Saving the world—or at least the world’s scientific and government data
The O’Reilly Data Show Podcast: Max Ogden on data preservation, distributed trust, and bringing cutting-edge technology to journalism.
Deep learning that’s easy to implement and easy to scale
The O’Reilly Data Show Podcast: Anima Anandkumar on MXNet, tensor computations and deep learning, and techniques for scaling algorithms.
Tom Coates on conversational devices
The O’Reilly Bots Podcast: Conversational interfaces for the Internet of Things.
Tim Hwang on bots that cause chaos
The O’Reilly Bots Podcast: Automating “psyops” with AI-driven bots.
Building machine learning solutions that can withstand adversarial attacks
The O’Reilly Data Show Podcast: Parvez Ahammad on minimal supervision, and the importance of explainability, interpretability, and security.
Artificial intelligence: Cooperation vs. aggression
Machines learn what we teach them. If you don't want AI agents to shoot, don't give them guns.
Amir Shevat on workplace communication
The O’Reilly Bots Podcast: Slack’s head of developer relations talks about what bots can bring to Slack channels.
The dirty secret of machine learning
David Beyer talks about AI adoption challenges, who stands to benefit most from the technology, and what's missing from the conversation.
Deep learning for Apache Spark
The O’Reilly Data Show Podcast: Jason Dai on BigDL, a library for deep learning on existing data frameworks.
The key to building deep learning solutions for large enterprises
The O’Reilly Data Show Podcast: Adam Gibson on the importance of ROI, integration, and the JVM.
Chris Messina on conversational commerce
The O’Reilly Bots Podcast: The 2017 bot outlook with one of the field’s early adopters.
How big compute is powering the deep learning rocket ship
The O’Reilly Data Show Podcast: Greg Diamos on building computer systems for deep learning and AI.
Brad Abrams on Google Assistant
The O’Reilly Bots Podcast: A universal bot for messaging, mobile voice, and the home.
The 2017 machine learning outlook
Drew Paroski and Gary Orenstein on the rapid spread of machine learning and predictive analytics
Practical artificial intelligence in the cloud
Mike Barlow examines the growth of sophisticated cloud-based AI and machine learning services for a growing market of developers and users in business and academia.