As social media becomes the core domain of information interaction in the era of big data, the emotional information contained in the vast amount of user-generated content provides an unprecedented ...

A new study from researchers at Stanford University and Nvidia proposes a way for AI models to keep learning after deployment — without increasing inference costs. For enterprise agents that have to ...

DeepSeek published a paper outlining a more efficient approach to developing AI, illustrating the Chinese artificial intelligence industry’s effort to compete with the likes of OpenAI despite a lack ...

While we may have gotten away with high-volume, high-intensity training and minimal recovery in our twenties, we lose some of that flexibility as time goes on. Gone are the days when we could down a ...

Researchers at Google Cloud and UCLA have proposed a new reinforcement learning framework that significantly improves the ability of language models to learn very challenging multi-step reasoning ...

Lindsey Ellefson is Lifehacker’s Features Editor. She currently covers study and productivity hacks, as well as household and digital decluttering, and oversees the freelancers on the sex and ...

It was a big sample group. The researchers examined nearly 20,000 employees at UC San Diego Health. People who got cybersecurity training were compared to those who got none. Some people with training ...

In the current climate, generic and expensive programs to promote diversity, equity, and inclusion—for example, trainings—are increasingly falling out of favor. In fact, most of the existing research ...

This blog post is the second in our Neural Super Sampling (NSS) series. The post explores why we introduced NSS and explains its architecture, training, and inference components. In August 2025, we ...