GPT-5

Galbot Unveils DexNDM & NavFoM: Major Breakthroughs in Embodied AI for Robotics Robotics firm Galbot has announced two significant technological advancements in embodied intelligence: DexNDM and NavFoM. DexNDM is a neural dynamics model designed to enhance robotic dexterous manipulation, enabling robots to perform high-precision, in-hand rotations of various objects. NavFoM is presented as the world’s first cross-embodiment, cross-task navigation foundation model, allowing robots to autonomously perform complex tasks in diverse environments. These innovations, developed with Tsinghua and Peking University, integrate DexNDM into a teleoperation system for intricate tasks like tightening screws with high-level human commands. These advancements are poised to revolutionize industries like manufacturing, logistics, and healthcare by improving the adaptability and efficiency of robots in real-world settings. ...

Medallion Raises $43M to Streamline Healthcare Credentialing with AI Platform Medallion, a healthcare technology company, has secured $43 million in new funding for its AI-powered platform, CredAlliance. The platform is designed to streamline the complex healthcare credentialing process for both insurers and clinicians. It manages the entire provider lifecycle—including credentialing, enrollment, onboarding, and compliance—aiming to significantly reduce administrative waste and combat clinician burnout. CredAlliance acts as a unified system for provider data management, enabling customers to efficiently verify credentials and maintain up-to-date network information. This latest investment, led by Acrew Capital, elevates Medallion’s total funding to $130 million and will fuel the expansion of the platform’s capabilities. ...

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. ...