Machine Learning

1231 articles found

Xiaomi Open-Sources Robotics AI Model Trained on 100,000+ Hours of Data, But Key Technical Details Remain Missing

Xiaomi Open-Sources Robotics AI Model Trained on 100,000+ Hours of Data, But Key Technical Details Remain Missing

Aug 06, 2026
Inside AI

Xiaomi open-sources its robotics AI foundation model, Xiaomi-Robotics-1, trained on over 100,000 hours of data, offering a rare end-to-end blueprint in a proprietary-dominated field, but missing key details like model architecture and parameter count raise serious questions about its real-world utility.

Meta AI Launches Muse Code Beta With Powerful Spark 1.2 Model Capable of Autonomous Coding Across Large Repositories

Meta AI Launches Muse Code Beta With Powerful Spark 1.2 Model Capable of Autonomous Coding Across Large Repositories

Aug 06, 2026
Meta AI Research

Meta AI launches Muse Code in beta, a terminal-based coding agent powered by the new Muse Spark 1.2 model that can autonomously plan, write, and validate code across large repositories, with benchmark tests showing it successfully optimizes GPU kernels over 1,000+ tool calls for significant performance gains on NVIDIA Hopper …

Google Shakes Up AI Leadership: Hassabis Steps Back, Jeff Dean Departs After 27 Years to Launch New AI Venture

Google Shakes Up AI Leadership: Hassabis Steps Back, Jeff Dean Departs After 27 Years to Launch New AI Venture

Aug 06, 2026
Google

Google shakes up its AI leadership as Demis Hassabis steps back from day-to-day operations at DeepMind to focus on AGI strategy, Koray Kavukcuoglu takes the reins overseeing Gemini's 950 million users, and legendary engineer Jeff Dean departs after 27 years to co-found a new AI-focused public benefit corporation with Google …

MIT Study Finds AI Diagnostic Tools Help Clinicians But Lead Non-Experts to Blindly Trust Wrong Answers

MIT Study Finds AI Diagnostic Tools Help Clinicians But Lead Non-Experts to Blindly Trust Wrong Answers

Aug 05, 2026
MIT News | Massachusetts Institute of Technology

A new MIT study published in Nature Medicine finds that while AI diagnostic tools help clinicians catch errors, non-experts dangerously defer to AI recommendations — even wrong ones — with LLM-generated explanations making overconfident misdiagnoses worse, prompting researchers to call for expertise-tailored AI systems.

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