AI transformation cannot be "AI for everything." Successful enterprises focus on a limited set of high-impact use cases with measurable outcomes.

AI agents are powerful, but without a strong control plane and hard guardrails, they’re just one bad decision away from chaos.

Real-world optimization problems often require an external “modeling engine” that computes fitnesses or data that are then input to an objective function. These programs often have much longer ...

Overall, I predict a shift from systems that merely process data to ones that genuinely understand context, ushering in an ...

Relying on GA4 alone leaves AI SEO analytics lost in the Bermuda Triangle of measurement. Learn what’s missing – and what to track instead.

As AI models migrate from secure data centers to exposed edge devices, a new threat vector has emerged: model theft. Popat identified this vulnerability early, pioneering a novel defense mechanism ...

From Deep Blue to modern AI, how chess exposed the shift from brute-force machines to learning systems, and why it matters AI ...

Better understanding of the design, implementation and operation of these cyber-physical systems can enable optimized process ...

Salesforce's Vara Imandi is pioneering "Digital Diplomacy," using autonomous AI agents to bridge the gap between legacy ...

Michelle Lee, PhD, unpacks how physical AI that integrate scientific reasoning with the wet lab will accelerate biological discovery.

Cristani, C. and Tessera, D. (2026) A Foundational Protocol for Reproducible Visualization in Multivariate Quantum Data. Open Access Library Journal, 13, 1-13. doi: 10.4236/oalib.1114704 .