Dr. Michael Zargham provides a systems engineering perspective on AI agents, emphasizing accountability structures and the relationship between principals who deploy...

Part 2 of this series could have easily been renamed "AI for science: The expert’s guide to practical machine learning.” We continue our discussion with Christoph Molnar and Timo Freiesleben to look at how scientists can apply

Machine learning is transforming scientific research across disciplines, but many scientists remain skeptical about using approaches that focus on prediction over causal understanding. That’s why we are excited to have Christoph Mol...

Unlock the secrets to AI's modeling paradigms. We emphasize the importance of modeling practices, how they interact, and how they should be considered in relation to each other before you act. Using the right tool for the right job is key. We h...

Agentic AI is the latest foray into big-bet promises for businesses and society at large. While promising autonomy and efficiency, AI agents raise fundamental questions about their accuracy, governance, and the potential pitfalls of over-relian...