Researchers built a clinical framework to study the reasoning of smaller open-source AI models to make them more accessible and cost-effective, especially in rural areas.

Learn how AI model distillation makes smaller models cheaper and why the technique is fueling disputes over competition, copyright, and data use.

As AI moves into enterprise workflows, fintech leaders are looking beyond model intelligence to reliability, governance, cost ...

A wave of financial firms is creating bespoke gen-AI models specifically for transaction data. Mastercard is one of them. We had some ...

Banks increasingly rely on complex AI models, but explainability has limits. This analysis examines model risk, regulatory ...

AI tokens determine how generative AI systems process information, calculate usage and generate costs, making tokenomics essential for effective AI budgeting.

Berkeley Lab and UC Davis scientists found a metal foil that triggers fusion at lower temperatures, a step toward cheaper, ...

Law does not exist only in statutes, regulations, and court decisions. Before law reaches courts, it already exists in ...

At VB Transform 2026, Kel Vanee, MVP of machine learning engineering at Capital One, spoke with Sam Witteveen, Senior Technology Contributor at VentureBeat, about how the bank built a scalable ...

The Chinese AI startup’s massive new model is challenging OpenAI and Anthropic, fueling a debate over AI safety. The release of the Chinese AI model Kimi K3 was a flashpoint in the AI world, ...

Zhang Weirong, 76, ordered food by speaking to his desktop device supported by an AI agent this morning as usual, with the ...