The program for our Artificial Intelligence Conference in New York City will showcase tools, best practices, and use cases from companies leading the way in AI adoption.
How new developments in automation, machine deception, hardware, and more will shape AI.
An overview of NAS and a discussion on how it compares to hyperparameter optimization.
When it comes to automation of existing tasks and workflows, you need not adopt an “all or nothing” attitude.
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.
Jason Knight offers an overview of the state of the field for scaling training and inference across distributed systems.
Marc Warner and Louis Barson discuss the internal and external uses of AI in the UK government.
Cassie Kozyrkov shares machine learning lessons learned at Google and explains what they mean for applied data science.
Drawing on the McKinsey Global Institute’s research, Michael Chui explores commonly asked questions about AI and its impact on work.
Ben Lorica and Roger Chen highlight recent trends in data, compute, and machine learning.
Ashok Srivastava draws upon his cross-industry experience to paint an encouraging picture of how AI can solve big problems.
Ruchir Puri explains why trust and transparency are essential to AI adoption.
Amy Heineike explains how Primer created a self-updating knowledge base that can track factual claims in unstructured text.
Ian Massingham discusses the application of ML and AI within Amazon, from retail product recommendations to the latest in natural language understanding.
Yangqing Jia talks about what makes AI software unique and its connections to conventional computer science wisdom.
Jonathan Ballon explains why Intel’s AI and computer vision edge technology will drive advances in machine learning and natural language processing.
Watch highlights from expert talks covering artificial intelligence, machine learning, automation, and more.
Our bad AI could be the best tool we have for understanding how to be better people.
Manish Goyal shows you how to best unlock the value of enterprise AI.
Huma Abidi discusses the importance of optimization to deep learning frameworks.
David Patterson explains why he expects an outpouring of co-designed ML-specific chips and supercomputers.
Levent Besik explains how enterprises can stay ahead of the game with customized machine learning.
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.
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.
Dawn Song explains how AI and deep learning can enable better security and how security can enable better AI.
Joseph Sirosh tells an intriguing story about AI-infused prosthetics that are able to see, grip, and feel.
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.
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.
Kai-Fu Lee outlines the factors that enabled China's rapid ascension in AI.
Kishore Durg explains why deploying AI requires raising it to act as a responsible representative of the business and a contributing member of society.
Watch highlights from expert talks covering artificial intelligence, machine learning, security, and more.
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.
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.
Olga Russakovsky explains how her organization, AI4ALL, aims to increase diversity and inclusion in AI development and research.
George Church discusses the IARPA MICrONS project, which aims to revolutionize machine learning by reverse-engineering the algorithms of the brain.
Ron Bodkin explains what a tensor is and why you should care.
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.
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.
Ben Lorica and Roger Chen discuss the state of reinforcement learning and automation.
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.