Machine Learning

In the era of artificial intelligence, privacy protection is one of the hottest topics. Neural networks often “memorize” pieces of training data. In extreme cases, an attacker could try to reconstruct the original examples just from the trained model’s parameters (so-called reconstruction attacks). Imagine a medical model that could reveal fragments of sensitive patient images — alarming, right? The new paper “No Prior, No Leakage: Revisiting Reconstruction Attacks in Trained Neural Networks” (arxiv.org) challenges this fear. It shows that without additional knowledge (priors), reconstruction is fundamentally undecidable. In other words: model parameters alone may not be enough to recover the training data. ...

Today, credit card transactions are everywhere — online shopping, bill payments, travel, etc. Unfortunately, the number of fraud cases is also growing. The challenge is that frauds are very rare compared to normal transactions. This means that simple models trained on raw data often “ignore” these rare cases — because statistically, it’s cheaper to be wrong on a few frauds than on thousands of normal payments. The paper “Credit Card Fraud Detection” (arXiv:2509.15044) analyzes how to improve fraud detection by applying data preprocessing techniques (class balancing) and comparing several models. This is crucial because the effectiveness of such systems has real-world consequences — for banks, payment platforms, and user security. ...

Graph Neural Networks (GNNs) are among the most powerful tools in modern AI. They can analyze data structured as nodes and connections – like social networks, financial links, protein structures, or transportation systems. But success comes with risk: GNNs can be attacked. A new research paper introduces JANUS – a framework that learns to inject fake nodes into graphs in a way that is extremely hard to detect. While framed as an attack, the insights are equally valuable for building defenses. ...

Can the effectiveness of an advertising system be improved by almost 10% simply by tuning the weights in the ranking function more intelligently? It turns out the answer is yes – and that’s exactly what the paper Deep Reinforcement Learning for Ranking Utility Tuning in the Ad Recommender System at Pinterest (arXiv:2509.05292) is about. Traditionally, ad ranking relies on a utility function – a linear combination of multiple model predictions, such as CTR (click-through rate), conversion probability, or other business metrics. The problem? The weights of these predictors were historically tuned manually by engineers. This approach: ...

Modern science, especially in the field of high-energy physics, generates unimaginable amounts of data. Experiments like the LCLS-II free-electron laser (FEL) at the SLAC National Accelerator Laboratory produce terabytes of data per second. Transmitting and storing all of it is impractical. The solution is to intelligently select data in real-time, right at the source. The publication “Neural Network Acceleration on MPSoC board: Integrating SLAC’s SNL, Rogue Software and Auto-SNL” is a fascinating case study of how to achieve this using artificial intelligence and specialized hardware. ...

Modern machine learning models, from image recognition systems to large language models, have achieved impressive capabilities. However, their strength can be deceptive. One of the biggest challenges in the field of AI is their vulnerability to adversarial attacks. These are intentionally crafted, small perturbations to input data (e.g., changing a few pixels in an image) that are imperceptible to humans but can completely fool the model, leading to incorrect and often absurd decisions. ...

Artificial intelligence has already transformed many industries, but the world of scientific research has been waiting for a true game-changer. While general AI models are powerful, they often lack the specialized knowledge needed for deep scientific inquiry. Enter Intern-S1, a new multimodal foundation model that’s set to bridge this gap and accelerate a new era of discovery. Developed by the Shanghai AI Laboratory, Intern-S1 is not just another large language model. It’s a specialized generalist, designed from the ground up to understand and process complex scientific data in various formats, from text and images to time-series data. ...

In the world of artificial intelligence, Continual Learning is one of the biggest challenges. The goal is to enable AI models to learn new things sequentially without forgetting what they have learned before. This is a key ability that brings us closer to creating truly intelligent systems capable of adapting to a dynamically changing world. Unfortunately, traditional neural networks suffer from so-called catastrophic forgetting. When they learn a new task, they tend to overwrite the knowledge gained from previous tasks. The publication “Monte Carlo Functional Regularisation for Continual Learning” (arXiv:2508.13006) by Pengcheng Hao, Menghao Waiyan William Zhu, and Ercan Engin Kuruoglu presents an innovative approach to this problem. ...

Modern systems based on Machine Learning (ML) are ubiquitous, from credit scoring to fraud detection. The conventional wisdom is that more data leads to better models. However, this data-centric approach directly conflicts with a fundamental legal principle: data minimization (DM). This principle, enshrined in key regulations like the GDPR in Europe and the CPRA in California, mandates that personal data collection and processing must be “adequate, relevant and limited to what is necessary in relation to the purposes for which they are processed”. ...

Imagine you’re learning to play chess. You master all the rules, strategies, and openings. You become a pretty good player. Now, someone introduces a new piece with completely new rules of movement. As you learn to play with this new piece, do you forget how to move a pawn or a knight? Of course not. Your brain can integrate new knowledge without losing what it has already acquired. Unfortunately, for many artificial intelligence systems, this is a huge challenge, known as “catastrophic forgetting”. ...