Artificial Intelligence & Machine Learning

Esaote Unveils AI-Powered Cardiac Ultrasound at ESC 2025 for Enhanced Diagnostics Medical imaging company Esaote is set to introduce significant AI enhancements to its cardiac ultrasound technology at the European Society of Cardiology (ESC) 2025 conference. The system utilizes machine learning algorithms to improve image clarity and diagnostic accuracy. This innovation in AI-driven medical imaging aims to enhance cardiac workflows and enable quicker, more reliable interpretation of results for healthcare professionals. ...

AI & ML Developments: NVIDIA GPUs, Mozambique’s Tax Strategy, and University Partnerships Today’s top stories in Artificial Intelligence and Machine Learning feature Lambda’s deployment of Supermicro’s NVIDIA Blackwell GPU servers to expand its AI cloud infrastructure. In other news, Mozambique is adopting a new AI-centric strategy to transform its tax system, and Elizabeth City State University is collaborating with Amazon to advance AI and ML education for its students. Sources: ...

Cyware Launches Open-Source AI Server to Secure Threat Intelligence At the Black Hat USA 2025 conference, cybersecurity firm Cyware announced the launch of its open-source Model Context Protocol (MCP) Server. This new tool is designed to securely connect large language models (LLMs) with threat intelligence and automation systems. The MCP Server allows security teams to use natural language inputs to initiate real-world actions and investigations while maintaining full context and control. The move reflects a broader trend of viewing AI as a practical tool for improving security operations by reducing investigation times and improving data correlation. By open-sourcing the server, Cyware aims to foster collaboration and interoperability within the cybersecurity community. ...

How Google AI’s New Method Reduces LLM Training Data by 10,000x Google Research has unveiled a groundbreaking method for fine-tuning large language models (LLMs) that reduces required training data by up to 10,000 times. This innovative approach utilizes active learning to focus expert labeling on the most informative examples, especially “boundary cases” where model uncertainty is highest. In experiments with Gemini Nano models, this technique matched or surpassed the quality of models trained on 100,000 random labels with as few as 250 to 450 targeted examples. This development promises to make AI model development significantly leaner, more agile, and more cost-effective. ...