Machines will need to make ethical decisions, and we will be responsible for those decisions.
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.
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 O’Reilly Data Show Podcast: Siwei Lyu on machine learning for digital media forensics and image synthesis.
Highlights and use cases from companies that are building the technologies needed to sustain their use of analytics and machine learning.
How companies in Europe are preparing for and adopting AI and ML technologies.
The O’Reilly Data Show Podcast: Maryam Jahanshahi on building tools to help improve efficiency and fairness in how companies recruit.
A recent survey investigated how companies are approaching their AI and ML practices, and measured the sophistication of their efforts.
Consent is the first step toward the ethical use of data, but it's not the last.
An exploration of three types of errors inherent in all financial models.
The O’Reilly Data Show Podcast: Andrew Burt on the need to modernize data protection tools and strategies.
From infrastructure to tools to training, Ben Lorica looks at what’s ahead for data.
The O’Reilly Data Show Podcast: Haoyuan Li on accelerating analytic workloads, and innovation in data and AI in China.
The O’Reilly Data Show Podcast: Ben Lorica looks ahead at what we can expect in 2019 in the big data landscape.
We need to do more than automate model building with autoML; we need to automate tasks at every stage of the data pipeline.
The O’Reilly Data Show Podcast: Alex Wong on building human-in-the-loop automation solutions for enterprise machine learning.
The O’Reilly Data Show Podcast: Vitaly Gordon on the rise of automation tools in data science.
Considerations for a world where ML models are becoming mission critical.
The O’Reilly Data Show Podcast: Francesca Lazzeri and Jaya Mathew on digital transformation, culture and organization, and the team data science process.
The O’Reilly Data Show Podcast: Alon Kaufman on the interplay between machine learning, encryption, and security.
Create a coherent BI strategy that aligns data collection and analytics with the general business strategy.
The O’Reilly Data Show Podcast: Jacob Ward on the interplay between psychology, decision-making, and AI systems.
The O’Reilly Data Show Podcast: Sharad Goel and Sam Corbett-Davies on the limitations of popular mathematical formalizations of fairness.
Getting DataOps right is crucial to your late-stage big data projects.
Dinesh Nirmal explains how AI is helping supply school lunch and keep ahead of regulations.
Amber Case covers methods product designers and managers can use to improve interactions through an understanding of sound design.
Jacob Ward reveals the relationship between the unconscious habits of our minds and the way that AI is poised to amplify them, alter them, maybe even reprogram them.
Brain-based human-machine interfaces: New developments, legal and ethical issues, and potential uses
Amanda Pustilnik highlights potential applications of data from new technologies that capture brain-based processes.
Chad Jennings explains how Geotab's smart city application helps city planners understand traffic and predict locations of unsafe driving.
Julia Angwin discusses what she's learned about forgiveness from her series of articles on algorithmic accountability and the lessons we all need to learn for the coming AI future.
Ziya Ma discusses how recent innovations from Intel in high-capacity persistent memory and open source software are accelerating production-scale deployments.
Hilary Mason explores the current state of AI and ML and what’s coming next in applied ML.
Ben Sharma shares how the best organizations immunize themselves against the plague of static data and rigid process
The O’Reilly Data Show Podcast: Alan Nichol on building a suite of open source tools for chatbot developers.
Cassie Kozyrkov explores why businesses fail at machine learning despite its tremendous potential and excitement.
Ben Lorica offers an overview of recent tools for building privacy-preserving and secure machine learning products and services.
Executives from Cloudera and PNC Bank look at the challenges posed by data-hungry organizations.
Joseph Lubin explains how Ethereum can help with new innovations like cryptocurrencies, automated and self-executing legal agreements, and self-sovereign identity.
Drew Paroski and Aatif Din share how to develop modern database applications without sacrificing cost savings, data familiarity, and flexibility.
Watch highlights from expert talks covering data science, machine learning, algorithmic accountability, and more.
Ted Dunning discusses how new tools can change the way production systems work.
DD Dasgupta explores the edge-cloud continuum, explaining how the roles of data centers and cloud infrastructure are redefined through the mainstream adoption of AI, ML, and IoT technologies.
It has become much more feasible to run high-performance data platforms directly inside Kubernetes.
If we’re going to think about the ethics of data and how it’s used, then we have to take into account how data flows.
This collection of data governance resources will get you up to speed on the basics and best practices.
The O’Reilly Data Show Podcast: Eric Jonas on Pywren, scientific computation, and machine learning.
Fernando Perez talks about UC Berkeley's transition into an environment where many undergraduates use Jupyter and the open data ecosystem as naturally as they use email.
Michelle Ufford shares how Netflix leverages notebooks today and describes a brief vision for the future.
Michelle Gill discusses how data science methods and tools can link information from different scientific fields and accelerate discovery.
Tracy Teal explains how to bring people to data and empower them to address their questions.
Cristian Capdevila explains how Prognos is predicting disease.
Ryan Abernathey makes the case for the large-scale migration of scientific data and research to the cloud.
David Schaaf explains how data science and data engineering can work together to deliver results to decision makers.
Watch keynotes covering Jupyter's role in business, data science, higher education, open source, journalism, and other domains, from JupyterCon in New York 2018.
All the cool kids are doing it, maybe we should too? Jupyter, gravitational waves, and the LIGO and Virgo Scientific Collaborations
Will Farr offers lessons about the many advantages and few disadvantages of using Jupyter for global scientific collaborations.
Dan Romuald Mbanga walks through the ecosystem around the machine learning platform and API services at AWS.
Carol Willing shows how Jupyter's challenges can be addressed by embracing complexity and trusting others.
Julia Meinwald outlines effective ways to support the unseen labor maintaining a healthy open source ecosystem.