Anyone can try to break a chatbot.   That is part of what makes AI red teaming feel accessible. Open a model, write a strange prompt, ask for something the system should refuse, reframe the request, and see what happens. Sometimes the result is funny. Sometimes it is useful. Sometimes it is a little alarming.

Introduction The integration of deep learning (DL) algorithms into clinical decision support systems holds great promise for improving diagnostic accuracy and efficiency. However, for clinicians to trust such systems, their decisions must be transparent, allowing validation against medical knowledge1. In the context of classification of electrocardiograms (ECGs), deep neural networks (DNNs) have demonstrated high accuracy

Published:  June 23, 2026 at 1:14 PM Updated:  June 23, 2026 at 1:59 PM Updates to AI forecast models in 2026 look to build upon the successes of last hurricane season The 2025 hurricane season was a coming-of-age story for AI weather models, which have been around in some capacity since around 2022. Leer en

Building AI systems at scale is demanding, requiring low-latency inference, fast vector search, strong GPU price-performance and infrastructure that can grow without multiplying operational complexity.  NVIDIA’s latest work with Amazon Web Services (AWS) addresses each of those constraints. Across Amazon OpenSearch and Amazon EC2, NVIDIA AI infrastructure is giving enterprises more practical paths to deploy

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Oxford and UCL to host new government-backed labs developing the next generation of AI that more businesses and services can readily use AI is already helping to diagnose cancer sooner and make our energy systems more resilient - these 2 new labs will ensure British people continue to benefit as the technology advances Backed with up to £60 million and access to large-scale computing

Using artificial intelligence (AI), researchers found that image-based risk scores for breast cancer derived from screening mammograms evolve over time and differ between women who develop cancer and those who do not, opening the door to a new era of dynamic breast cancer risk assessment. The new research was published today in Radiology, a journal

Data scientists sit at the intersection of analytics, machine learning (ML) and AI, translating messy, real-world data into decisions that drive business outcomes. As the volume and complexity of enterprise data has grown, so has the strategic importance of the role: today, data scientists are among the most sought-after practitioners in the modern organization. AI