Enterprises face key challenges in harnessing unstructured data so they can make the most of their investments in AI, but several vendors are addressing these challenges.

Research shows erroneous training in one domain affects performance in another, with concerning implications Large language models (LLMs) trained to misbehave in one domain exhibit errant behavior in ...

Solumina AI operates within a secure, self-contained architecture that supports air-gapped and restricted environments. Each deployment includes an embedded large language model (LLM) running in ...

The next major evolution will come from multi-agent systems—networks of smaller, specialized AI models that coordinate across ...

As AI adoption matures in the enterprise, the spotlight is shifting from massive, general-purpose models to smaller, bespoke, ...

MDSA is a production-ready Python framework for orchestrating domain-specialized small language models (SLMs). It provides cost-efficient AI system architecture with autonomous agent behavior across ...

The final, formatted version of the article will be published soon. Understanding how cognitive abilities support mathematical learning across development remains a central question in developmental ...

For enterprises racing to integrate AI, one barrier keeps resurfacing no matter how quickly the technology advances: hallucinations. A recent Bain & Company report found that output quality remains a ...

According to God of Prompt on Twitter, a new research paper from PayPal and NVIDIA demonstrates that significant performance improvements in agentic AI do not require massive general-purpose language ...

Abstract: Despite the recent advances in large language models, problems with generation of domain-specific texts are still exist. For example, medical and chemical texts cannot be created by large ...