What if the most powerful artificial intelligence models could teach their smaller, more efficient counterparts everything they know—without sacrificing performance? This isn’t science fiction; it’s ...

Single image dehazing, as a key prerequisite of high-level computer vision tasks, catches more and more attentions. Traditional model-based methods recover haze-free images via atmospheric scattering ...

Google has been a significant contributor to technological innovation, influencing various industries through its projects. The PageRank algorithm altered how information is organized and accessed ...

Businesses are increasingly aiming to scale AI, but they often encounter constraints such as infrastructure costs and computational demands. Although large language models (LLMs) offer great potential ...

Accurate emotion recognition in social media text is critical for applications such as sentiment analysis, mental health monitoring, and human-computer interaction. However, existing approaches face ...

If you’ve ever used a neural network to solve a complex problem, you know they can be enormous in size, containing millions of parameters. For instance, the famous BERT model has about ~110 million.

A new front has opened in the U.S.-China competition in artificial intelligence: open-weight, local AI models. Until recently, the most capable AI models were too big and too costly to run anywhere ...