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 ...

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 ...

Generative AI, especially large language models (LLMs), present exciting and unprecedented opportunities and complex challenges for academic research and scholarship. As the different versions of LLMs ...

There is an all-out global race for AI dominance. The largest and most powerful companies in the world are investing billions in unprecedented computing power. The most powerful countries are ...

It’s no secret that large language models (LLMs) have gotten exorbitantly expensive. Companies are starting to limit their employees’ AI usage to save money; OpenAI has even discussed lowering the ...

Over the past year, the conversation around artificial intelligence (AI) has shifted. After a period of rapid progress, many teams deploying large language models (LLMs) are encountering familiar ...

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 ...

Despite increasing use of artificial intelligence (AI) in health care, a new study led by Mass General Brigham researchers from the MESH Incubator shows that generative AI models continue to fall ...

Until now, the AI revolution has been largely measured by size: the bigger the model, the bolder the claims. However, as we move closer to truly autonomous and pervasive AI systems, a new trend is ...