Say a person takes their French Bulldog, Bowser, to the dog park. Identifying Bowser as he plays among the other canines is easy for the dog-owner to do while onsite.

For patients with inflammatory bowel disease, antibiotics can be a double-edged sword. The broad-spectrum drugs often prescribed for gut flare-ups can kill helpful microbes alongside harmful ones, sometimes worsening symptoms over time. When fighting gut inflammation, you don’t always want to bring a sledgehammer to a knife fight.

Say a local concert venue wants to engage its community by giving social media followers an easy way to share and comment on new music from emerging artists. Rather than working within the constraints of existing social platforms, the venue might want to create its own social app with the functionality that would be best for its community. But building a new social app from scratch involves many complicated programming steps, and even if the venue can create a customized app, the organization’s followers may be unwilling to join the new platform because it could mean leaving their connections and data behind.

Chatbots like ChatGPT and Claude have experienced a meteoric rise in usage over the past three years because they can help you with a wide range of tasks. Whether you’re writing Shakespearean sonnets, debugging code, or need an answer to an obscure trivia question, artificial intelligence (AI) systems seem to have you covered. The source of this versatility? Billions or even trillions of textual data points across the Internet.

A global cohort of eight scientists and engineers working in a variety of disciplines were named Schmidt Polymaths and will each receive up to $2.5 million over five years to pursue research in new disciplines or using new methodologies, Schmidt Sciences announced today. 

Customer data is a valuable asset for businesses, but its use presents a complex privacy challenge. Companies aim to predict customer churn, yet this process is increasingly restricted by privacy regulations such as GDPR and growing consumer concerns about data protection.

When researchers are building large language models (LLMs), they aim to maximize performance under a particular computational and financial budget. Since training a model can amount to millions of dollars, developers need to be judicious with cost-impacting decisions about, for instance, the model architecture, optimizers, and training datasets before committing to a model. To anticipate the quality and accuracy of a large model’s predictions, practitioners often turn to scaling laws: using smaller, cheaper models to try to approximate the performance of a much larger target model. The challenge, however, is that there are thousands of ways to create a scaling law.