AI

In recent years, AI progress has been largely defined by size: bigger models, bigger datasets, bigger compute budgets. GPT-4, Claude, Gemini – each new model pushes the limits further. But is bigger always better? A group of researchers (Baek, Park, Ko, Oh, Gong, Kim) argue in their recent paper "AI Should Sense Better, Not Just Scale Bigger" (arXiv:2507.07820) that we’ve hit diminishing returns. Instead of growing endlessly, they propose a new focus: adaptive sensing. ...

In the era dominated by language models and machine learning, the importance of structured data is growing rapidly: social networks, biological relationships, and business connections. This data is represented in the form of graphs, which are often not homogeneous: they contain nodes of different types (e.g., people, products, companies) and different types of edges (e.g., “purchased”, “recommended”, “works at”). Processing such heterogeneous graphs requires specialized methods. What are heterogeneous graphs? A heterogeneous graph is a structure in which: ...

Diagnosing mental disorders such as autism, depression, or schizophrenia goes beyond taking simple brain images. Resting-state fMRI (rs-fMRI) observes brain activity while at rest, revealing which regions activate simultaneously. This forms the basis for functional connectivity. Traditional studies have used graphs and neural networks, but they mostly focus on pairwise interactions — asking “do regions A and B co-activate?” But what about higher-order relationships, like among regions A, B, and C all at once? ...

Can we predict when and where people will rent electric scooters? Yes — and with impressive accuracy. A recent publication shows how advanced algorithms like XGBoost can revolutionize the management of micromobility in cities. 🌍 Context: Micromobility and Demand In many cities, dockless electric scooters have become a daily transport option. But for operators, a crucial question remains: Where and when will people want to rent a scooter? Too many vehicles in one location is wasteful. Too few — lost revenue and frustrated users. That’s why accurately predicting demand is so important. ...

When we train deep neural networks, they often get stuck — not in a bad result, but in a “flat region” of the loss landscape. The authors of this paper introduce ghost nodes: extra, fake output nodes that aren’t real classes, but help the model explore better paths during training. Imagine you’re rolling a ball into a valley. Sometimes the valley floor is flat and the ball slows down. Ghost nodes are like adding new dimensions to the terrain — giving the ball more freedom to move and find a better path. ...

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. ...