Kristian Hammond maps out simple rules, useful metrics, and where AI should live in the org chart.
Supasorn Suwajanakorn discusses the possibilities and the dark side of building artificial people.
Marc Warner and Louis Barson discuss the internal and external uses of AI in the UK government.
Drawing on the McKinsey Global Institute’s research, Michael Chui explores commonly asked questions about AI and its impact on work.
Cassie Kozyrkov shares machine learning lessons learned at Google and explains what they mean for applied data science.
Jason Knight offers an overview of the state of the field for scaling training and inference across distributed systems.
Ashok Srivastava draws upon his cross-industry experience to paint an encouraging picture of how AI can solve big problems.
Ian Massingham discusses the application of ML and AI within Amazon, from retail product recommendations to the latest in natural language understanding.
Ruchir Puri explains why trust and transparency are essential to AI adoption.
Watch highlights from expert talks covering artificial intelligence, machine learning, automation, and more.
Yangqing Jia talks about what makes AI software unique and its connections to conventional computer science wisdom.
Amy Heineike explains how Primer created a self-updating knowledge base that can track factual claims in unstructured text.
Ben Lorica and Roger Chen highlight recent trends in data, compute, and machine learning.
Jonathan Ballon explains why Intel’s AI and computer vision edge technology will drive advances in machine learning and natural language processing.
Our bad AI could be the best tool we have for understanding how to be better people.
Hagay Lupesko explores key trends in machine learning, the importance of designing models for scale, and the impact that machine learning innovation has had on startups and enterprises alike.
Joseph Sirosh tells an intriguing story about AI-infused prosthetics that are able to see, grip, and feel.
Manish Goyal shows you how to best unlock the value of enterprise AI.
David Patterson explains why he expects an outpouring of co-designed ML-specific chips and supercomputers.
Peter Norvig says one of the most exciting aspects of AI is the diversity of applications in fields far astray from the original breakthrough areas.
Huma Abidi discusses the importance of optimization to deep learning frameworks.
Levent Besik explains how enterprises can stay ahead of the game with customized machine learning.
Dawn Song explains how AI and deep learning can enable better security and how security can enable better AI.
Watch highlights from expert talks covering artificial intelligence, machine learning, security, and more.
Kishore Durg explains why deploying AI requires raising it to act as a responsible representative of the business and a contributing member of society.
Kai-Fu Lee outlines the factors that enabled China's rapid ascension in AI.
Akhilesh Tripathi shows you how to use machine learning to identify root causes of problems in minutes instead of hours or days.
Julie Shin Choi reviews real-world customer use cases that take AI from theory to reality.
Soups Ranjan describes the machine learning system that Coinbase built to detect potential fraud and fake identities.
Meredith Whittaker says the benefits of AI will only come if we have a clear-eyed perspective on its dark side.
Tim O'Reilly and Kai-Fu Lee discuss differences in how China and the U.S. approach AI and why AI might give humanity larger purpose.
Ben Lorica and Roger Chen provide a glimpse into tools and trends poised to accelerate AI innovation.
A conversation with Paul Taylor, chief architect in Watson Data and AI, and IBM fellow.
Chatbots are just the first step in the journey to achieve true AI assistants and autonomous organizations.
Ray is beginning to be used to power large-scale, real-time AI applications.
Tricks to visualize and understand how neural networks see.
O'Reilly survey results and usage data reveal growing trends and topics in artificial intelligence.
General intelligence or creativity can only be properly imagined if we peel away the layers of abstractions.
The program for our Artificial Intelligence Conference in London is structured to help companies that are still very much in the early stages of AI adoption.
“Human in the loop” software development will be a big part of the future.
An overview and framework, including tools that can be used to enable automation.
This collection of AI resources will get you up to speed on the basics, best practices, and latest techniques.
The personal robot temi refactors robotic human behaviors we encounter in the “iPhone Slump,” and moves those back to actual robots.
Dave Patterson and other industry leaders discuss how MLPerf will define an entire suite of benchmarks to measure performance of software, hardware, and cloud systems.
MLPerf is a new set of benchmarks compiled by a growing list of industry and academic contributors.
Using machine learning, deep learning, and cognitive computing in concert can help enterprises gain competitive edges.
Get a basic overview of machine learning and then go deeper with recommended resources.
Meihong Wang explains how Facebook thinks about personalization and how the company uses machine learning to provide personalized experiences.
Abhijit Deshpande explains how to use machine learning to identify root causes of problems in minutes instead of hours.
Ron Bodkin explains what a tensor is and why you should care.
George Church discusses the IARPA MICrONS project, which aims to revolutionize machine learning by reverse-engineering the algorithms of the brain.
Thomas Reardon offers an overview of brain-machine interface (BMI) technology and shares CTRL-Labs’s transformative and noninvasive neural interface approach.
Dario Gil explores state-of-the-art computing for AI as it exists today as well as an innovation that will lead us into the decades to come: quantum computing for AI.
Olga Russakovsky explains how her organization, AI4ALL, aims to increase diversity and inclusion in AI development and research.
Food production needs to double by 2050 to feed the world’s growing population. Jennifer Marsman details a solution that uses sensors in the soil, aerial imagery from drones, and machine learning.
Mary Beth Ainsworth offers an overview of SAS deep learning and computer vision capabilities that help map wildlife and scale conservation efforts around the world.
Zoubin Ghahramani discusses recent advances in artificial intelligence, highlighting research in deep learning, probabilistic programming, Bayesian optimization, and AI for data science.
Manuela Veloso looks at the role humans can play in autonomy-based AI interactions and the underlying challenges to AI.
Dan Mbanga explores how accelerating AI experimentation has influenced innovations such as Amazon Alexa, Prime Air, and Go.
Watch highlights covering artificial intelligence, machine learning, automation, and more. From the Artificial Intelligence Conference in New York 2018.