Manish Goyal shows you how to best unlock the value of enterprise AI.
Huma Abidi discusses the importance of optimization to deep learning frameworks.
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.
David Patterson explains why he expects an outpouring of co-designed ML-specific chips and supercomputers.
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.
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.
Levent Besik explains how enterprises can stay ahead of the game with customized machine learning.
Julie Shin Choi reviews real-world customer use cases that take AI from theory to reality.
Akhilesh Tripathi shows you how to use machine learning to identify root causes of problems in minutes instead of hours or days.
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.
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.
Watch highlights from expert talks covering artificial intelligence, machine learning, security, and more.
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.
Abhijit Deshpande explains how to use machine learning to identify root causes of problems in minutes instead of hours.
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.
Olga Russakovsky explains how her organization, AI4ALL, aims to increase diversity and inclusion in AI development and research.
Meihong Wang explains how Facebook thinks about personalization and how the company uses machine learning to provide personalized experiences.
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.
Watch highlights covering artificial intelligence, machine learning, automation, and more. From the Artificial Intelligence Conference in New York 2018.
Fiaz Mohamed explains how Intel AI solves today’s business problems.
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.
Manuela Veloso looks at the role humans can play in autonomy-based AI interactions and the underlying challenges to AI.
Zoubin Ghahramani discusses recent advances in artificial intelligence, highlighting research in deep learning, probabilistic programming, Bayesian optimization, and AI for data science.
Dan Mbanga explores how accelerating AI experimentation has influenced innovations such as Amazon Alexa, Prime Air, and Go.
Kavya Kopparapu shares her inspiration for starting GirlsComputingLeague.
Ben Lorica and Roger Chen discuss the state of reinforcement learning and automation.
Fiaz Mohammed and Justin Herz discuss how artificial intelligence can improve content discovery and monetization
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.
We’re currently laying the foundation for future generations of AI applications, but we aren’t there yet.
Solving the challenges of efficiency, automation, and safety will require cooperation between researchers and engineers spanning both academia and industry.
A few ways to think differently and integrate innovation and AI into your company's altruistic pursuits.
Innovations that increase detection of, and response to, criminal attacks of financial systems.
Our survey reveals how organizations are using tools, techniques, and training to apply AI through deep learning.
The AI Conference in NY will feature tutorials, conference sessions, and executive briefings to help business leaders understand and plan for AI technologies.
Why we're taking the AI Conference to Beijing.
The top 5 ways to immerse yourself in deep learning and MXNet.
Leveraging the potential of AI to gain maximum ROI.
A look at the parallels between human and machine knowledge acquisition.
Opportunities and challenges companies will face integrating and implementing deep learning frameworks.
A step-by-step tutorial to develop an RNN that predicts the probability of a word or character given the previous word or character.