Enterprise AI is at an inflection point. What began with centralized, cloud-scale large language models (LLMs) is moving ...

General-purpose models struggle with messy, industry-specific data. A three-layer AI stack from Trunk Tools cut document review cycles from 60 days to 10.

NLP and LLM teams often grow their training corpuses to improve model performance but they still do not always obtain p ...

Real-world testing is expensive, slow, and difficult to reproduce. For a robot’s performance in the real world to be ...

Thomson Reuters’ AI approach shows why trusted outputs depend on authoritative data, rigorous governance, and domain expertise behind the model.

For Executive Chair Rishad Premji, the current climate is one in which clients have moved beyond AI pilot phase and begun ...

Retrieval-augmented generation is a standard way to ground large language models in enterprise information, but new research ...

Healthcare organizations benefit when they can use reliable predictive models to better match their workforce to demand.

PsyEval is a new benchmark that tests how large language models perform on mental health knowledge, diagnostic assessment, ...

To make the shift from model-centric to systemic intelligence tangible, we use an autonomous order management system for machines as an example, through which customers can not only order but also ...

Cybersecurity researchers tested Open AI GPT 5.5’s offensive cyber capabilities – and the results showed how effective a ...

Spurred by Washington's sudden curb on Anthropic, global corporations are shifting away from general-purpose, rented AI to prevent policy changes from hampering their core business operations ...