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Jane Adams examines the ways data-driven recruiting fails to achieve intended results and perpetuates discriminatory hiring practices.
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Martin Kleppmann shows how recent computer science research is helping develop the abstractions and APIs for the next generation of applications.
Omoju Miller outlines a vision where we harness human action for a better future.
Anne Currie says excessive and dirty energy use in data centers is one of the biggest ethical issues facing the tech industry.
Katrina Owen says the valuable skills that experienced professionals lack are at the vital margins of their careers.
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Chris Richardson describes microservices anti-patterns he’s observed while working with clients around the world.
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As lists are the raw material of strategy and technology architecture, MECE list-making is one of the most useful tools you can have in your tool box.
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O’Reilly’s new survey reveals the latest operations salary trends, and the skill sets that will keep your operations career on track.
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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.
The O’Reilly Data Show Podcast: Jacob Ward on the interplay between psychology, decision-making, and AI systems.
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Jonathan Ballon explains why Intel’s AI and computer vision edge technology will drive advances in machine learning and natural language processing.
Ian Massingham discusses the application of ML and AI within Amazon, from retail product recommendations to the latest in natural language understanding.
Ben Lorica and Roger Chen highlight recent trends in data, compute, and machine learning.
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.
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.
Our bad AI could be the best tool we have for understanding how to be better people.
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Jaana Dogan explains why Google teaches its tracing tools to new employees and how it helps them learn about Google-scale systems end to end.
Roger Magoulas shares insights from O'Reilly's online learning platform that point toward shifts in the systems engineering ecosystem.
Laura Thomson shares Mozilla’s approach to data ethics, review, and stewardship.
Michael Bernstein offers an unflinching look at some of the fallacies that developers believe about marketing.
Tammy Butow explains how companies can use Chaos Days to focus on controlled chaos engineering.
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Kris Beevers examines the trade-offs between risk and velocity faced by any high-growth, critical path technology business.
Kavya Joshi says performance theory offers a rigorous and practical approach to performance tuning and capacity planning.
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Jessica McKellar draws parallels between the free and open source software movement and the work to end mass incarceration.
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