Opportunities for agentic AI. AI agents go beyond basic in-context learning by enabling LLMs to iteratively plan, reason, and ...

Objectives To evaluate the performance of large language models (LLMs) in risk of bias assessment and to examine whether ...

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 ...

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 ...

Though large language models remain popular, IT decision-makers are increasingly trying out smaller, open models that may be a better fit for enterprise AI needs.

Gary Marcus, professor emeritus at NYU, explains the differences between large language models and "world models" — and why he thinks the latter are key to achieving artificial general intelligence.

SandboxAQ today announced the integration of its Large Quantitative Models (LQMs) with Claude, Anthropic's frontier AI model, making it possible to directly connect a large language model to a large ...

Many businesses have been developing SLMs for some time, training them on data so they can provide accurate responses.

AI is entering its diffusion phase, and the microeconomic pressures driving that transition are operating with unusual speed.