Objectives To evaluate the performance of large language models (LLMs) in risk of bias assessment and to examine whether prompt engineering improves their accuracy and alignment with expert reasoning.

There’s a paradox at the heart of modern AI: The kinds of sophisticated models that companies are using to get real work done and reduce head count aren’t the ones getting all the attention.

The proliferation of edge AI will require fundamental changes in language models and chip architectures to make inferencing and learning outside of AI data centers a viable option. The initial goal ...

What if you could demystify one of the most fantastic technologies of our time—large language models (LLMs)—and build your own from scratch? It might sound like an impossible feat, reserved for elite ...

Are tech companies on the verge of creating thinking machines with their tremendous AI models, as top executives claim they are? Not according to one expert. We humans tend to associate language with ...

How large is a large language model? Think about it this way. In the center of San Francisco there’s a hill called Twin Peaks from which you can view nearly the entire city. Picture all of it—every ...

Editor’s note (September 9th): This article has been updated. WHEN TECH folk talk about the lacklustre progress of large language models (LLMs), they often draw an analogy with smartphones. The early ...