An overview of commercial and industrial applications of reinforcement learning.
A unified methodology for scheduling workflows, managing data, and offloading to GPUs.
Since AI's most amazing advances have been in playing games, it seems fitting that the creative challenge should involve creating games.
Using the keras TensorFlow abstraction library, the method is simple, easy to implement, and often produces surprisingly good results.
Analytical frameworks come with an entire ecosystem.
The IBM team encourages developers to ask tough questions, be patient, and be ready to fail gracefully.
How to build and train a DCGAN to generate images of faces, using a Jupyter Notebook and TensorFlow.
How to create your own custom object detection model.
What you need know before committing to AI.
A tutorial on how to use machine learning to build recommender systems.
With rich data sources already in place, investments in both technology and organizational change pay off.
Using advanced neural networks to tackle challenging natural language tasks.
Using deep neural networks to make sense of unstructured text.
MXNet’s origins show through in its power and flexibility.
Tim O'Reilly says the algorithms that shape our economy must be rewritten if we want to create a more human-centered future.
Lili Cheng shares two examples of AI that were inspired by nature.
Steve Jurvetson examines the state of artificial intelligence.
Michael Jordan discusses recent results in gradient-based optimization for large-scale data analysis.
Philippe Poutonnet discusses how you can harness the power of machine learning, whether you have a machine learning team of your own or you just want to use machine learning as a service.
A discussion on the impact and opportunities of artificial intelligence.
Jia Li explains why a democratized approach to AI ensures that the components behind these technologies reach the widest possible audience.
Rana el Kaliouby lays out a vision for an emotion-enabled world of technology.
Ruchir Puri addresses the opportunities and challenges of AI for business and focuses on what's needed to scale AI across the breadth of enterprises.
Andrew Ng shares his thoughts on where the biggest opportunities in AI may lie.
Peter Norvig speaks with Abu Qader, the 18-year-old CTO of GliaLab who taught himself machine learning and launched an AI company for breast cancer diagnostics.
Watch highlights covering artificial intelligence, machine learning, applied deep learning, and more. From the Artificial Intelligence Conference in San Francisco 2017.
AI Conference chairs Ben Lorica and Roger Chen reveal the current AI trends they've observed in industry.
Building convnets from scratch with TensorFlow and TensorBoard.
A deep dive into startup TuSimple’s use of Apache MXNet.
Artificial intelligence is emerging as a creative force; in the process, it reveals something of itself.
Turning abstract AI into real business solutions.
Generate new images and fix old ones using neural networks.
Learn how to use IBM Watson's APIs and natural language understanding to analyze the tone of social media posts like tweets.
Learn how to use IBM Watson's APIs and natural language understanding to extract information about people and companies from news articles.
Machine learning opens the door for the use of training rather than programming in game development.
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.
Doug Eck discusses Magenta, a Google Brain project aimed at developing new machine learning models for art and sound creation.
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.
Naveen Rao explains how Intel Nervana is evolving the AI stack from silicon to the cloud.
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.
Damion Heredia explores augmented intelligence, IBM's alternative definition of AI.
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.
Richard Socher explains how Salesforce is doing the heavy lifting to deliver scalable AI to customers.
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.