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Google Research just released a fascinating new paradigm called “Nested Learning” to solve one of the most persistent headaches in AI: “catastrophic forgetting.” In traditional machine learning, models are often static. When you teach them a new task, they tend to overwrite what they learned previously. To learn the “new,” they forget the “old.” Google’s… Read more: Does your AI have amnesia?

When cars and airplanes arrived, did we spend less time traveling? The opposite happened. Initially, yes, cars made existing trips faster. But then we realized what else became possible. Weekend getaways. Cross-country moves. Global business. We didn’t reduce travel. We expanded what travel meant. AI and programming will follow the same pattern. Right now, we’re… Read more: Will AI Replace Programmers? History Says No.

I recently researched the future of AI in pharmacovigilance, analyzing relevant literature and discussing with three top AI models (GPT-5.1, Claude Opus-4.5, and Gemini-3.0). Here is the realistic roadmap and the critical focus areas that emerged from my research stripped of marketing hype. The “Pilot Purgatory” (Now) Despite the headlines, very few companies let AI… Read more: A Reality Check on AI in Pharmacovigilance: From Hype to Action.

A recent MIT report highlighted a startling statistic. 95% of investments in Generative AI have produced zero returns. This number is alarming, but for those of us in drug discovery and pharmacovigilance, it shouldn’t be surprising. The problem isn’t the technology itself. The problem is how we measure it. Most AI tools are deployed without… Read more: Why 95% of GenAI Investments Are Failing (And How to Fix It).

We are drowning in data but starving for insight. Every day, the biomedical literature grows by thousands of new publications. Clinical trial databases expand. Real-world evidence accumulates. Adverse event reports pile up. And somewhere buried in all of that noise is the connection that could accelerate your next discovery. The problem is not access. The… Read more: Why Knowledge Graphs Matter More Than Ever