From data quality to personalization, to customer acquisition and retention, and beyond, AI and ML will shape the customer experience of the future.
More than anything else, O'Reilly's AI Conference was about making the leap to AI 2.0.
Kim Hazelwood discusses the hardware and software Facebook has designed to meet its scale needs.
Rajendra Prasad explains how leaders in large enterprises can make AI adoption successful.
Nick Curcuru explains how Mastercard is using AI to improve security without sacrificing the customer experience.
Ruchir Puri discusses the next revolution in automating AI, which strives to deploy AI to automate the task of building, deploying, and managing AI tasks.
How can machine learning decode the mysteries of life? Olga Troyanskaya explores this and other big questions through the prism of deep learning.
Christopher Ré discusses Snorkel, a system for fast training data creation.
Sean Gourley considers the repercussions of AI-generated content that blurs the line between what's real and what's fake.
Carlos Humberto Morales offers an overview of Nauta, an open source multiuser platform that lets data scientists run complex deep learning models on shared hardware.
Thomas Henson considers how AI will shape the experiences of future generations.
Ben Lorica and Roger Chen assess the state of AI technologies and adoption in 2019.
Tony Jebara explains how Netflix is personalizing and optimizing the images shown to subscribers.
Martial Hebert offers an overview of challenges in AI for robotics and a glimpse at the exciting developments emerging from current research.
Watch highlights from expert talks covering AI, machine learning, deep learning, ethics, and more.
Danielle Dean explains how cloud, data, and AI came together to help build Automated ML.
Aleksander Madry discusses roadblocks preventing AI from having a broad impact and approaches for addressing these issues.
Joleen Liang explains how AI and precise knowledge points can help students learn.
Kurt Muehmel explores AI within a broader discussion of the ethics of technology, arguing that inclusivity and collaboration are necessary.
Gadi Singer discusses the major questions organizations confront as they integrate deep learning.
The toughest bias problems are often the ones you only think you’ve solved.
The software industry has demonstrated, all too clearly, what happens when you don’t pay attention to security.
The most promising area in the application of deep learning methods to time series forecasting is in the use of CNNs, LSTMs, and hybrid models.
There are growing numbers of users and contributors to the framework, as well as libraries for reinforcement learning, AutoML, and data science.
An overview of emerging trends, known hurdles, and best practices in artificial intelligence.
Much like human speech, bird song learning is social; perhaps we'll discover machine learning is social, too.
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.
Supasorn Suwajanakorn discusses the possibilities and the dark side of building artificial people.
Kristian Hammond maps out simple rules, useful metrics, and where AI should live in the org chart.
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.
Ruchir Puri explains why trust and transparency are essential to AI adoption.
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.
Watch highlights from expert talks covering artificial intelligence, machine learning, automation, and more.
Amy Heineike explains how Primer created a self-updating knowledge base that can track factual claims in unstructured text.
Yangqing Jia talks about what makes AI software unique and its connections to conventional computer science wisdom.
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.
Dawn Song explains how AI and deep learning can enable better security and how security can enable better AI.
Huma Abidi discusses the importance of optimization to deep learning frameworks.
Manish Goyal shows you how to best unlock the value of enterprise AI.
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.
David Patterson explains why he expects an outpouring of co-designed ML-specific chips and supercomputers.
Joseph Sirosh tells an intriguing story about AI-infused prosthetics that are able to see, grip, and feel.
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.
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.
Soups Ranjan describes the machine learning system that Coinbase built to detect potential fraud and fake identities.
Kishore Durg explains why deploying AI requires raising it to act as a responsible representative of the business and a contributing member of society.