Enhancing Member Experience Through Strategic Collaboration Ozzie Sutherland, Iroro Orife, Chih-Wei Wu, Bhanu Srikanth At Netflix, delivering the best possible experience for our members is at the heart of everything we do, and we know we can’t do it alone. That’s why we work closely with a diverse ecosystem of technology partners, combining their deep expertise …
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This post is co-written with Kilian Zimmerer and Daniel Ringler from Deutsche Bahn. Every day, Deutsche Bahn (DB) moves over 6.6 million passengers across Germany, requiring precise time series forecasting for a wide range of purposes. However, building accurate forecasting models traditionally required significant expertise and weeks of development time. Today, we’re excited to explore …
In the AI era, where data fuels intelligent applications and drives business decisions, demand for accurate and consistent data insights has never been higher. However, the complexity and sheer volume of data coupled with the diversity of tools and teams can lead to misunderstandings and inaccuracies. That’s why trusted definitions managed by a semantic layer …
Read more “How Looker’s semantic layer enables trusted AI for business intelligence”
Anthropic launches web search API for Claude as Apple considers AI search alternatives to Google, signaling a major shift in how users discover information online.Read More
In a rare moment of global consensus, AI researchers from the US, Europe, and Asia came together in Singapore to form a plan for researching AI risks.
A research team develops disinfection robot combining physical wiping and UV-C sterilization.
Wouldn’t it be great if music creators had someone to brainstorm with, help them when they’re stuck, and explore different musical directions together? Researchers at KAIST and Carnegie Mellon University (CMU) have developed AI technology similar to a fellow songwriter who helps create music.
A well-structured marketing AI RFP helps you evaluate vendors effectively, avoid costly mistakes, and choose the right AI solution.
As large language models have already become essential components of so many real-world applications, understanding how they reason and learn from prompts is critical.
A few years ago, training AI models required massive amounts of labeled data.