How organizations can create more fluid interactions between humans and machines.
Tips and tricks for treating learning rate as a hyperparameter, and using visualizations to see what’s really going on.
Recent AI breakthroughs transform speech recognition.
A look ahead at the tools and methods for learning from sparse feedback.
How to build a class of RL agents using a TensorFlow notebook.
Apache MXNet and the middle path between declarative and imperative programming.
Classifying traffic signs with Apache MXNet: An introduction to computer vision with neural networks
Step-by-step instructions to implement a convolutional neural net, using a Jupyter Notebook.
The quest to evolve neural networks through evolutionary algorithms.
Naveen Rao explains how Intel Nervana is evolving the AI stack from silicon to the cloud.
Amy Unruh demonstrates Google Cloud machine learning APIs and highlights OSS TensorFlow models.
The workflow of the AI researcher has been quite different from the workflow of the software developer. Peter Norvig explores how the two can come together.
Tuomas Sandholm explains how domain-independent algorithms are being applied to a variety of imperfect-information games, like poker.
Doug Eck discusses Magenta, a Google Brain project aimed at developing new machine learning models for art and sound creation.
Anca Dragan introduces a mathematical formulation that accounts for cars responding to people and people responding to cars.
The AI ecosystem just might be resilient enough to live up to the hype.
Watch highlights covering artificial intelligence, machine learning, applied deep learning, and more. From the O'Reilly Artificial Intelligence Conference in New York 2017.
Richard Socher explains how Salesforce is doing the heavy lifting to deliver scalable AI to customers.
Suchi Saria discusses the medical applications of artificial intelligence.
Jim McHugh explains why a new computing paradigm and deep learning software stack will be required to power, predict, and act on data.
Josh Tenenbaum looks at the intersection of computing and thought.
David Ferrucci offers an overview of Elemental Cognition, a company focused on creating AI systems that autonomously learn from human language and interaction.
Damion Heredia explores augmented intelligence, IBM's alternative definition of AI.
An AI-first strategy will only work if it puts the user first.
AI fighting extremism, intuitive physics, and schema networks.
Drawing with AI, Apple AI API, United Nations and AI for good, and smart oil and gas.
The O'Reilly Podcast: Andy Hickl on sources of bias in artificial intelligence—and how to address them.
Sukiyaki in French style, brick-and-mortar conversion tracking, route-based pricing, and technological productivity.
Aida Mehonic explores the role artificial intelligent might play in the financial world.
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.
AutoML, AI photo editing, AI product studio, and Apple and dark data.
Rana el Kaliouby discusses the techniques, possibilities, and challenges around emotion AI today.
Medical ImageNet, NVIDIA GTC, corporate responsibility in tech, online pricing
Aman Naimat discusses what he learned from building a knowledge graph of the entire business world.
How Stitch Fix systematizes collaboration between stylists and AI software.
Caffe2, deep learning best practices, intelligent design and wizard hats
Improving software with the help of a community takes patience and organization.
Creative deep neural networks, AI black box, robot food delivery, and brute force productivity.
Diogo Almeida examines the capabilities and challenges in deep learning.
Kenny Daniel on implementing neural networks in production.
Song Han on compression techniques and inference engines to optimize deep learning in production.
A closer look at the reasoning inside your deep networks.
Inspiration from the brain is extremely relevant to AI; it’s time we pushed it further.
Is it possible to imagine an AI that can compute ethics?
DIY with Amazon Echo and Raspberry PI: Recognize thousands of people at your door every month, for pennies.
Machines learn what we teach them. If you don't want AI agents to shoot, don't give them guns.
Bots are made possible by recent advances in artificial intelligence, user interface, and communication.
David Beyer talks about AI adoption challenges, who stands to benefit most from the technology, and what's missing from the conversation.
Data, algorithms, and better business results are key to developing AI.
Turning physical resource management into a data and learning problem.
Oren Etzioni talks about the current AI landscape, projects at the Allen Institute, and why we need AI to make sense of AI.
From tools, to research, to ethics, Ben Lorica looks at what’s in store for artificial intelligence in 2017.
We need more philosophers, psychologists, poets, artists, politicians, anthropologists, social scientists, and critics of art in the conversation.
Cathy Pearl on how to think about conversations when designing for voice interactions.
We need AI researchers who are actively trying to defeat AI systems and exposing their inadequacies.
Lili Cheng provides insights into the planning and release of Microsoft's bot, Tay.
Are bots your new best friend?
Andy Mauro discusses pitfalls, opportunities, and the future of conversational bot interactions.
A framework for thinking about AI.
Improving prediction accuracy using deep compression and DSD training.
It's the ensemble of technologies that will make the impossible possible.