AI

Large‑scale epidemic modeling is a key tool for public health—but it often requires sensitive data (e.g., hospital admissions, financial records, mobility). A recent paper, “A Framework for Multi‑source Privacy Preserving Epidemic Analysis” (June 27, 2025), introduces a hybrid neural‑mechanistic model that respects Differential Privacy (DP). This means we can use private data without compromising individuals’ privacy. 🌍 Why It Matters 🚑 Accurate predictions help allocate resources (like vaccines, ICU beds). 🕵️‍♂️ But using private data poses a privacy risk. 🔐 Differential Privacy (DP) adds controlled randomness—protecting individuals at a formal, mathematical level. 🧠 Inside the Framework: Neural + Mechanistic The model is a hybrid system combining: ...

🧠 Mind2Web 2: Evaluating Agentic Search with Agent-as-a-Judge Agentic Search is one of the most promising applications of modern AI. Imagine a virtual assistant that doesn’t just look up information for you but can autonomously search the web, navigate pages, find facts, and return well-structured answers with citations. That’s the idea behind tools like OpenAI’s Deep Research. However, how do we evaluate if such an AI is doing a good job? ...

Can a machine discover the laws of physics by itself—like Newton, but without the apple and without writing the equation by hand? In June 2025, a new method called H-FEX (Hamiltonian Finite Expression) was published. It doesn’t just predict system behavior—it writes down the math behind it. And crucially, in a form humans can understand. It’s a form of symbolic learning, increasingly popular over black-box neural networks that work, but don’t tell us why. ...