Paige Bailey, TensorFlow product manager at Google, highlights notable features of TensorFlow 2.0 and looks ahead to near-term updates.
AI startups vied for awards at the O’Reilly Artificial Intelligence Conference in San Jose.
Experts discuss new trends, tools, and issues in artificial intelligence and machine learning.
A look at why graphs improve predictions and how to create a workflow to use them with existing machine learning tasks.
An overview of applications of new tools for overcoming silos, and for creating and sharing high-quality data.
To successfully implement AI technologies, companies need to take a holistic approach toward retraining their workforces.
Speech adds another level of complexity to AI applications—today’s voice applications provide a very early glimpse of what is to come.
Adversarial images aren’t a problem—they’re an opportunity to explore new ways of interacting with AI.
Interest in PyTorch among researchers is growing rapidly.
We shouldn't ask our AI tools to be fair; instead, we should ask them to be less unfair and be willing to iterate until we see improvement.
Experts explore the future of hiring, AI breakthroughs, embedded machine learning, and more.
Abigail Hing Wen discusses some of the most exciting recent breakthroughs in AI and robotics.
Ion Stoica outlines a few projects at the intersection of AI and systems that UC Berkeley's RISELab is developing.
Tim Kraska outlines ways to build learned algorithms and data structures to achieve “instance optimality” and unprecedented performance for a wide range of applications.
Michael James examines the fundamental drivers of computer technology and surveys the landscape of AI hardware solutions.
Pete Warden digs into why embedded machine learning is so important, how to implement it on existing chips, and shares new use cases it will unlock.
Haoyuan Li offers an overview of a data orchestration layer that provides a unified data access and caching layer for single cloud, hybrid, and multicloud deployments.
Mikio Braun takes a look at Zalando and the retail industry to explore how AI is redefining the way ecommerce sites interact with customers.
Maria Zheng examines AI and its impact on people’s jobs, quality of work, and overall business outcomes.
Neural-backed generators are a promising step toward practical program synthesis.
To successfully integrate AI and machine learning technologies, companies need to take a more holistic approach toward training their workforce.
We now are in the implementation phase for AI technologies.
We won’t get the chance to worry about artificial general intelligence if we don’t deal with the problems we have in the present.
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.
Christopher Ré discusses Snorkel, a system for fast training data creation.
How can machine learning decode the mysteries of life? Olga Troyanskaya explores this and other big questions through the prism of deep learning.
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.
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.
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.
Sean Gourley considers the repercussions of AI-generated content that blurs the line between what's real and what's fake.
Danielle Dean explains how cloud, data, and AI came together to help build Automated ML.
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.
Aleksander Madry discusses roadblocks preventing AI from having a broad impact and approaches for addressing these issues.
Watch highlights from expert talks covering AI, machine learning, deep learning, ethics, and more.
Ben Lorica and Roger Chen assess the state of AI technologies and adoption in 2019.
Martial Hebert offers an overview of challenges in AI for robotics and a glimpse at the exciting developments emerging from current research.
Joleen Liang explains how AI and precise knowledge points can help students learn.
Thomas Henson considers how AI will shape the experiences of future generations.
Tony Jebara explains how Netflix is personalizing and optimizing the images shown to subscribers.
The toughest bias problems are often the ones you only think you’ve solved.
Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors.
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.
Drawing on the McKinsey Global Institute’s research, Michael Chui explores commonly asked questions about AI and its impact on work.
Marc Warner and Louis Barson discuss the internal and external uses of AI in the UK government.
Jason Knight offers an overview of the state of the field for scaling training and inference across distributed systems.
Cassie Kozyrkov shares machine learning lessons learned at Google and explains what they mean for applied data science.