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.
Nick Curcuru explains how Mastercard is using AI to improve security without sacrificing the customer experience.
Rajendra Prasad explains how leaders in large enterprises can make AI adoption successful.
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.
Sean Gourley considers the repercussions of AI-generated content that blurs the line between what's real and what's fake.
Thomas Henson considers how AI will shape the experiences of future generations.
Gadi Singer discusses the major questions organizations confront as they integrate deep learning.
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.
Kurt Muehmel explores AI within a broader discussion of the ethics of technology, arguing that inclusivity and collaboration are necessary.
Danielle Dean explains how cloud, data, and AI came together to help build Automated ML.
Martial Hebert offers an overview of challenges in AI for robotics and a glimpse at the exciting developments emerging from current research.
Aleksander Madry discusses roadblocks preventing AI from having a broad impact and approaches for addressing these issues.
Tony Jebara explains how Netflix is personalizing and optimizing the images shown to subscribers.
Joleen Liang explains how AI and precise knowledge points can help students learn.
Balancing risk and reward is a necessary tension we'll need to understand as we continue our journey into the age of data.
The O’Reilly Data Show Podcast: Avner Braverman on what’s missing from serverless today and what users should expect in the near future.
Or, why science and engineering are still different disciplines.
Get hands-on training in TensorFlow, cloud computing, blockchain, Python, Java, and many other topics.
Why companies are turning to specialized machine learning tools like MLflow.
The toughest bias problems are often the ones you only think you’ve solved.
Watch highlights from expert talks covering AI, machine learning, data analytics, and more.
Theresa Johnson outlines the AI powering Airbnb’s metrics forecasting platform.
Peter Singer explores the new rules of power in the age of social media and how we can navigate a world increasingly shaped by "likes" and lies.
Lauren Kunze discusses lessons learned from an analysis of interactions between humans and chatbots.
Elizabeth Svoboda explains how biosensors and predictive analytics are being applied by political campaigns and what they mean for the future of free and fair elections.
Google BigQuery co-creator Jordan Tigani shares his vision for where cloud-scale data analytics is heading.
Mike Olson describes the key capabilities an enterprise data cloud system requires, and why hybrid and multi-cloud is the future.
The Strata Data Award is given to the most disruptive startup, the most innovative industry technology, the most impactful data science project, and the most notable open source contribution.
The O’Reilly Data Show Podcast: Forough Poursabzi Sangdeh on the interdisciplinary nature of interpretable and interactive machine learning.
Shafi Goldwasser explains why the next frontier of cryptography will help establish safe machine learning.
Amy O'Connor explains how Cloudera applies an "edge to AI" approach to collect, process, and analyze data.
Dinesh Nirmal shares a data asset framework that incorporates current business structures and the elements you need for an AI-fluent data platform.
Jed Dougherty plots AI examples on a matrix to clarify the various interpretations of AI.
Machines will need to make ethical decisions, and we will be responsible for those decisions.
Our entire economy seems to have forgotten that workers are also consumers, and suppliers are also customers.
Organizations that want all of the speed, agility, and savings the cloud provides are embracing a cloud native approach.
Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors.
The O’Reilly Data Show Podcast: Kartik Hosanagar on the growing power and sophistication of algorithms.
The internet itself is a changing context—we’re right to worry about data flows, but we also have to worry about the context changing even when data doesn’t flow.
Mapping the complex forces that are reshaping organizations and changing the employee/employer relationship.
Radar spots and explores emerging technology themes so organizations can succeed amid constant change.
At O’Reilly, we seek to foster a culture that creates opportunity, rewards and recognizes accomplishments, and treats everyone with respect.
NLP systems in health care are hard—they require broad general and medical knowledge, must handle a large variety of inputs, and need to understand context.
The O’Reilly Data Show Podcast: P.W. Singer on how social media has changed, war, politics, and business.
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.
The O’Reilly Data Show Podcast: Siwei Lyu on machine learning for digital media forensics and image synthesis.
To meet the challenge of producing more food with less everything, farm bots are going to be an essential part of the mix.
Highlights and use cases from companies that are building the technologies needed to sustain their use of analytics and machine learning.
Glenn Vanderburg talks about the importance of letting your attention roam, and he shares examples of how insights from other fields have inspired software practitioners.
Stuart Halloway explains how to augment agility with principles for designing systems.
Matt Stine looks at the tricky situations that sometimes emerge from design and architecture.
Watch highlights from expert talks covering cloud-native programming, software architecture career advice, and more.
Neal Ford talks with Mark Richards about his career path and his work as a software architect.
Tamar Eilam offers an overview of cloud-native programming and outlines a path toward the unification of the cloud programming model.
Trisha Gee shares lessons she learned the hard way while managing her career as a developer, lead, and technical advocate.