Language models can be augmented with a context retriever to incorporate knowledge from large external databases. By leveraging retrieved context, the neural network does not have to memorize the massive amount of world knowledge within its internal parameters, leading to better parameter efficiency, interpretability and mod-ularity.

Are you interested in learning more about the ways in which AI is being used? We've launched a new weekly newsletter series exploring just that: digging into how generative AI is being used and deployed across sectors and what professionals need to know to apply it in their everyday work. Each edition of Making AI Work begins with a case study, examining a specific use case of AI in a given industry. Then we'll take a deeper look at the AI tool being used, with more context about how other companies or sectors are employing that same tool or system. Finally, we'll end with action-oriented tips to help you apply the tool. The first edition takes a look at how AI is changing health care, digging into the future of medical note-taking by learning about the Microsoft Copilot tool used by doctors at Vanderbilt University Medical Center.

Valve's Steam Machine: Everything we know Toyota and Pony.ai start mass producing robotaxis for China The first bZ4X robotaxi is ready to be deployed. It's the first of many, if the companies stick to their plan, which is to produce more than 1,000 bZ4X robotaxis this year. The bZ4X is one of the three autonomous vehicle models Pony.ai intends to use for commercial services in Tier 1 Chinese cities, including Beijing and Shanghai. The other two vehicles are already being used for Pony.ai's Pony.ai's goal is to operate 3,000 vehicles by the end of 2026.

To overcome the shortcomings of existing approaches, we propose the budgeted multi-step expected improvement, a non-myopic acquisition function that generalizes classical expected improvement to the setting of heterogeneous and unknown evaluation costs.

The problem(1) with µy > 0 is called a weakly convex-strongly concave(WCSC) saddle-point problem, whereas forµy =0,itiscalledaweakly convex-merely concave(WCMC) saddle-point problem.