Category: Machine Learning

Abstract Depression is a heterogeneous disorder, often diagnosed based on symptom co-occurrence. However, individuals may present with markedly different symptom profiles, potentially reflecting distinct underlying mechanisms. Identifying common patterns of symptoms using data-driven approaches could help clarify the heterogeneity of depression. Furthermore, examining the sociodemographic and lifestyle characteristics, health status, and polygenic scores of individuals […]

Abstract Foundation models (FMs) promise to standardise predictive modeling across domains, yet their clinical value for tabular data remains unproven. To test this, we performed a large, fully reproducible benchmark of TabPFN, a leading FM for tabular prediction, against twelve established machine learning (ML) methods across twelve binary clinical tasks. Cohorts spanned 788 – 139,528 […]

Overview of MatterChat Figure 1a presents the architecture of MatterChat, designed to process both material structures and user requests as inputs to generate text-based outputs for tasks such as material property prediction, structural analysis and descriptive language generation. MatterChat consists of three core components: the material processing branch, the language processing branch and the bridge […]

Image by author # Introduction to quantum machine learning Quantum machine learning combines ideas from quantum computing and machine learning. Many researchers are studying how quantum computers can help with machine learning tasks. Several open source projects support this work. GitHub Share learning resources, examples, and code. These repositories make it easy to understand the […]

If you’ve ever spent weeks combing through hundreds of academic papers, you already know why finding the right AI tool for literature review can be transformative. The research landscape has changed dramatically: over 5 million academic articles are now published annually, making comprehensive manual reviews practically impossible for individual researchers. AI tools can now scan […]

In April 1986, a late-night safety test at reactor 4 at the Chernobyl nuclear power plant in what was then the Soviet Union turned into one of the worst industrial disasters in history. The explosion and subsequent fire released radioactive material across much of Europe, displacing communities and leaving a legacy that still shapes public […]

Once again, there is a gap between official statements and facts on the ground. Microsoft’s Community-First Initiative sounds great, but it doesn’t have an independent accountability mechanism built in. OpenAI’s new white paper signals a move toward progressive technology policy, but its president, Greg Brockman, has poured millions of dollars into a SuperPAC that opposes […]

Tailen: 2026.04.23 09:00 Mixed signals: Machine learning helps detect roars from lion collars without recording actual audio Roaring over long distances is an important lion behavior. They use unique sequences of moans and growls to communicate within their pride as well as with other animals. Scientists from the GAIA Initiative have published a machine learning […]

Integrated Quantum Technologies flagship products veil This allows businesses to protect data before inputting it into ML models using a new privacy protection framework that removes personally identifiable information (PII) while preserving and enhancing the data’s usefulness. VEIL enables scalable innovation without trade-offs and is designed to be future-proof. The threat of quantum computing. The […]

Artificial intelligence and blockchain technology were once viewed as parallel tracks of innovation, but in April 2026, they collided to create a high-velocity economic engine known as Decentralized AI (DeAI). As Large Language Models (LLMs) require increasing amounts of power and data, the traditional centralized silos of Big Tech are facing a formidable challenger: a […]