Using AI to help organizations eliminate misconfigurations, tighten security posture and maximize ROI -- all while keeping sensitive data private.
AI "readiness" is not enough; for organizations to reach true AI productivity, they need to work on their data quality.
In the era of AI workloads, even hyperscalers need help tackling latency and power challenges -- and that means rewriting old infrastructure rules.
Using AI to outpace modern threat actors and help customers safely adopt Microsoft Copilot, while preparing for the next wave of AI-powered attacks and agentic AI risks.
Reinforcement learning is key to how LLMs like ChatGPT improve and avoid mistakes -- but it also introduces potential bias. We break down how it works and why understanding it matters for anyone using or building with AI.
- By Pure AI Editors
- 06/02/2025
Finding a way to supercharge generative AI by embedding reasoning engines and knowledge graphs into Snowflake.
No-code doesn't just help IT; it also helps business users overcome foundational data challenges, and unlock AI's full enterprise potential.
Safeguarding enterprise AI from dev to production requires a layered security platform.
Traditional machine learning tools have limits when it comes to getting insights out of data; that's where AI comes in.
A factory analogy demystifies how Transformers power AI like GPT -- perfect for non-data scientists seeking to grasp the basics.
- By Pure AI Editors
- 05/01/2025