Compare AI models side by side, then apply assistant suggestions to refine a TV and movie recommender prompt for engaging, friendly results.

In a new paper from OpenAI, the company proposes a framework for analyzing AI systems' chain-of-thought reasoning to understand how, when, and why they misbehave.

Discover how efficiency variance reveals the gap between expected and actual inputs in production and its impact on labor, materials, and costs.

Artificial intelligence has become an ally in cybersecurity by 2025, with 60% of organizations reporting using it in their IT ...

NotebookLM with Gemini v3 now builds slide decks from your notes and preserves citations, so you can share polished ...

Viyom Jain of Nagarro shares real-world lessons on enterprise AI adoption, balancing ambition with trust, culture, ethics, ...

Research reveals why AI systems can't become conscious—and what radically different computing substrates would be needed to ...

While many organizations are experimenting with synthetic data, few are focusing on scalability and building AI-ready data ...

Building a trusted data foundation The starting line for any successful AI journey is data readiness and quality. Before ...

Unlike the linear sprints of Agile, CAIL is a "contracts-first" framework designed to tame the probabilistic nature of AI ...

Scientists can finally hear the brain’s quietest messages—unlocking the hidden code behind how neurons think, decide, and ...

The failure of governments to agree with leading scientists on a major United Nations' report "risks impeding timely action ...