Alright, let’s start simple. Everyone who’s dabbled a bit in machine learning knows one thing: neural networks are nonlinear. That’s what makes them powerful — they can model weird, curvy, complex relationships, not just straight lines. But the authors of the paper “Who Said Neural Networks Aren’t Linear?” (Nimrod Berman, Assaf Hallak, Assaf Shocher) asked a cheeky question: what if that’s not entirely true? What if nonlinearity is just… a matter of perspective? ...

In apps like online stores, streaming platforms, or social media, we want to show users things they might like — “Hey, maybe you’ll enjoy this too.” That’s what recommendation systems do. Usually, those models live in the cloud — big servers crunch data and send you suggestions. But lately, more and more of that work is moving onto the user’s device (phone, tablet). Why? Because: it’s faster (less waiting), it’s more private (fewer data uploads), it saves server resources. But here’s the catch: devices vary. Some phones are monsters, others barely keep up. So how do you fit a good AI model on both? ...

Large Language Models (LLMs) are no longer just text generators — they are becoming reasoners, capable of solving mathematical problems, logical puzzles, or planning tasks step by step. One of the key challenges is how to improve the quality of this reasoning. Traditional Reinforcement Learning (RL) rewards only the final outcome, but in complex reasoning it makes more sense to evaluate each intermediate step. This is called process-supervised RL (PSRL). ...

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. ...

Imagine your computer not only analyzing financial charts but also learning to make investment decisions on its own – faster and smarter than a human. Now add a touch of quantum physics. Sounds like science fiction? Yet, recent research shows that combining reinforcement learning, quantum-inspired neural networks, and classical financial data can provide a real edge in trading. This is exactly the focus of a publication from National Taiwan Normal University and Wells Fargo. The researchers built a trading agent that uses quantum-enhanced neural networks to trade the USD/TWD (US Dollar/Taiwan Dollar) currency pair. ...

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: ...

We’re used to hearing that AI sometimes “hallucinates” — making funny or random mistakes. Hallucinations are unintended errors caused by the limits of statistical prediction. But the new research goes further: it shows that AI can knowingly choose to lie when deception helps it achieve a goal. The publication Can LLMs Lie? takes us into a world where AI acts more like a strategic agent, capable of manipulating information to maximize outcomes. ...

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. ...