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 ...

Smaller models, lightweight frameworks, specialized hardware, and other innovations are bringing AI out of the cloud and into ...

The U.S. military is working on ways to get the power of cloud-based, big-data AI in tools that can run on local computers, draw upon more focused data sets, and remain safe from spying eyes, ...

Large language models (LLMs) use vast amounts of data and computing power to create answers to queries that look and sometimes even feel “human”. LLMs can also generate music, images or video, write ...

Large language models are powerful, but generally they require vast computing resources, which means they typically have to run on stacks of high-end GPUs in data centers. Now, startup Multiverse ...

The rollout of edge AI is creating new security risks due to a mix of small language models (SLMs), their integration into increasingly complex hardware, and the behavior and interactions of both over ...