In Part 3, we shift from theory to practice — exploring how to build the infrastructure needed to support agentic AI at scale. We’ll look at orchestration, governance, and modular architecture as key ...

This research paper presents a proactive approach to congestion control in IoT networks using an encoder–decoder LSTM (ED-LSTM) model to predict packet loss ratios ahead of time. By forecasting ...

Artificial Intelligence is rapidly shaping industries that heavily rely on data, from healthcare and finance to national defense. As AI systems take on increasingly critical roles, securing their data ...

Early detection saves lives, but no single imaging test fits every patient or every tumor. Breast imaging balances sensitivity (how often cancers are found), specificity (how often healthy people aren ...

Our author, Arun Sahu, explores why in today’s rapidly evolving technology landscape, AI systems are expected to do far more than just respond with outputs. They are now required to reason, adapt, and ...

For data scientists, the rise of AI presents both exciting opportunities and new challenges. While AI models are becoming increasingly powerful and automated, the role of the data scientist is not ...

Medical imaging has long been a cornerstone of modern healthcare, enabling doctors to detect diseases, monitor progress, and guide treatments. Today, the integration of machine learning is pushing the ...

In Part 2 of this series, our author Arun Sahu dives into the core design patterns that make agentic AI systems truly intelligent and adaptive. From self-reflection to multi-agent collaboration, these ...

A old temple of documents, as imagined by DALL-E A few months ago, I stood across from a potential client who described something that sounded like the tech version of a buried temple. No vines or ...

There’s a word I keep coming back to lately: rewilding. In ecology, it’s the practice of restoring ecosystems to their natural, messy state. It means taking down fences, letting rivers flood their ...

If you’ve run a fine-tuning job on a Large Language Model (LLM), you’re familiar with the part that gets all the attention — the training process. In tutorials and notebooks, success is often ...