Artificial Intelligence

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. ...

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. ...

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”. ...

In the era of Big Data, data has become an organization’s most valuable asset. However, access to it is often limited by a technical barrier: the need to use query languages like SQL. For years, analysts and engineers have dreamed of a system that would allow them to “talk” to a database in natural language. Text-to-SQL systems aim to realize this vision, but their path has been challenging. Older models, though promising, often failed in real-world scenarios: they were “brittle,” struggled with unseen database schemas, and required costly fine-tuning for each new domain. ...

In a world where artificial intelligence (AI) is solving increasingly complex problems, formal mathematical theorem proving remains one of the toughest challenges. It’s the Mount Everest of machine reasoning, demanding not only immense computational power but, above all, deep, logical deduction. The scientific paper “Goedel-Prover-V2: Scaling Formal Theorem Proving with Scaffolded Data Synthesis and Self-Correction” introduces a breakthrough system that elevates automated proving to a new level. 🤖 System Architecture At the heart of Goedel-Prover-V2 is an advanced language model, specially trained and adapted to work with proof assistants like Lean. The system’s architecture is based on a cyclical interaction between several key components: ...

Can AI improve how call centers operate? The paper “Optimising Call Centre Operations using Reinforcement Learning: Value Iteration versus Proximal Policy Optimisation” by Kwong Ho Li and Wathsala Karunarathne shows that it can — and with strong results. The authors compare two reinforcement learning (RL) approaches to optimize call routing: the classical Value Iteration (VI) and the modern Proximal Policy Optimisation (PPO). What is Reinforcement Learning? Reinforcement Learning is an AI method where an agent takes actions in an environment and receives rewards based on how good those actions are. The goal is to maximize the cumulative reward — essentially, to learn the best decisions. ...

Imagine you’re analyzing sensor data. Suddenly one sensor shows -999°C. That’s an outlier — a single data point that can completely ruin your analysis. 🧩 What is factorization? Matrix factorization means decomposing data $X$ into two non-negative components: $$ X \approx WH $$ Where $W$ contains “features” and $H$ shows how much of each is needed. 💡 The problem Classical methods like NMF are sensitive to noise and outliers. When data is messy, analysis breaks down. ...