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Microsoft officially announced the release of a new device-side small language model (SLM), Mu, specifically designed for the settings application in Windows 11 to create an intelligent AI agent. This marks a significant breakthrough in localized AI technology within operating system interactions. As a compact and efficient model, Mu runs through deep optimization on the neural processing unit (NPU), providing users with a low-latency, high-privacy natural language interaction experience. AIbase has compiled the highlights and industry impact of Mu, taking you on a journey to explore its features. Mu: Designed Specifically for Windows Settings

This morning, Microsoft officially announced its latest innovative small-parameter model, Mu. This model has only 330 million parameters, yet it can match the performance of Microsoft's previously released Phi-3.5-mini, while being just one-tenth the size of Phi-3.5-mini. More notably, Mu can achieve a response speed of over 100 tokens per second on offline NPU laptops, which is a rare breakthrough in the field of small-parameter models. A major highlight of the Mu model is its support for setting intelligent features in Windows.

Recently, a research team from Stanford University conducted an interesting experiment exploring the role of artificial intelligence (AI) in medical diagnosis. They found that when AI transforms from a simple tool into a doctor's partner, the doctors' diagnostic accuracy improves by 10%. The study involved 70 licensed U.S. doctors, aiming to compare the effectiveness of AI with traditional diagnostic methods. Image source note: The image is generated by AI, and the image authorization service provider is Midjourney. In the experiment, doctors and AI jointly analyzed cases, with AI generating...

Earth System Models are crucial tools for predicting environmental changes, helping us better prepare for the future. However, these models have extremely high computational demands, limiting their operation at fine enough resolutions. Currently, most models operate at a resolution of approximately 100 kilometers, equivalent to the size of Hawaii, making accurate predictions for specific regions difficult. Nevertheless, for practical applications such as agriculture, water resource planning, and disaster response, city-scale prediction accuracy around 10 kilometers is vital. Therefore, improving the resolution of these models is essential for better protection.

In just three years, U.S.-based artificial intelligence research company OpenAI has achieved remarkable performance with its popular chatbot, ChatGPT. Its annual recurring revenue (ARR) has exceeded the $10 billion mark. According to the latest data, OpenAI's ARR last year was approximately $5.5 billion, meaning this year it has grown by nearly 80%. This rapid development is truly impressive, highlighting the immense potential of the AI market. ARR refers to the expected annual revenue a company generates from subscription services or products.

Recently, the San Francisco-based startup Spott announced it had raised $3.2 million in seed funding with the goal of building an AI-native recruitment platform to help recruitment agencies streamline processes and eliminate technical fragmentation. The round was led by Base10Partners, with participation from Y Combinator, Fortino, True Equity, and several angel investors. Spott has just completed the Y Combinator 2025 Winter Accelerator.