Machine Learning

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. ...

Modern emotion-recognition systems increasingly leverage data from multiple sources—ranging from physiological signals (e.g., heart rate, skin conductance) to facial video. The goal is to capture the richness of human feelings, where multiple emotions often co-occur. Traditional approaches, however, focused on single-label classification (e.g., “happy” or “sad”). The paper “HeLo: Heterogeneous Multi-Modal Fusion with Label Correlation for Emotion Distribution Learning” introduces an entirely new paradigm: emotion distribution learning, where the model predicts the probability of each basic emotion being present. ...

Imagine you track your morning commute times by recording 50 real-world trips with your GPS-enabled phone. You also run a traffic simulator to generate 5,000 possible commute scenarios. You want a reliable estimate of the 95th percentile of commute time—the duration you won’t exceed 95% of the days. Using only your 50 recorded trips yields a wide confidence interval. Using only the simulator risks systematic biases: it might ignore sudden road closures or special events. ...

The text-to-SQL task involves converting natural language questions into executable SQL queries on a relational database. While modern large language models (LLMs) excel at many generative tasks, generating correct and complex SQL queries remains challenging. In the paper RetrySQL: text-to-SQL training with retry data for self-correcting query generation, the authors introduce a training paradigm that teaches the model to self-monitor and correct its reasoning steps during generation, rather than relying solely on post-processing modules. ...

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. ...