AI in Windows: AI-Powered Features in Windows

Microsoft has invested a lot of manpower and material resources in the field of artificial intelligence and has achieved remarkable results. This article will explore artificial intelligence applications in Windows systems, including artificial intelligence tools and features in Windows 11 and Windows 10.

As we all know, Microsoft is constantly adding more artificial intelligence features in Windows 10 and Windows 11. Artificial intelligence in Windows systems is a hot topic today! Microsoft has ushered in a new era of artificial intelligence. This article will introduce you to the functions based on artificial intelligence in Windows systems.

Windows Copilot

Windows Copilot is a brand new AI feature in Windows 11 (also available in Windows 10). It is a smart assistant that helps users get answers and inspiration from the web, supports creativity and collaboration, and helps you focus on tasks.

In Windows 11 23H2, Windows Copilot is available on Build 22631.3007 or later. In Windows 11 22H2, it is available on Build 22621.3007 or later. In addition, artificial intelligence in Windows systems requires Microsoft Edge 120.0.2210.121 or higher. If you are still running Windows 10, you can enable Copilot using ViVeTool.

To see if Copilot is available on your device, you can check the following registry keys:

• Registry path: HKEY_CURRENT_USER\Software\Microsoft\Windows\Shell\Copilot
• Registry key name: IsCopilotAvailable
• Possible values: 0 means unavailable, 1 means available.

Click here to learn more about Copilot in Windows.

Windows AI Studio

Windows AI Studio is also one of the Windows artificial intelligence tools. It simplifies the development of generative AI applications by integrating cutting-edge AI development tools and models from Azure AI Studio and other directories such as Hugging Face.

Windows AI Studio enables developers to fine-tune, customize, and deploy state-of-the-art small language model (SLM) for local use in their Windows applications. It provides an end-to-end guided workspace setup, including model configuration UI and bootstrapping tutorials for fine-tuning popular SLMs such as Phi and state-of-the-art models such as Llama 2 and Mistral.

Developers can quickly test their fine-tuned models using Prompt Flow and Gradio templates integrated into their workspaces.

You can visit this Windows AI Studio GitHub page for the latest related documentation. You can also download Windows AI Studio for further use.

Windows Machine Learning

Leverage the power of Windows ML to implement machine learning in your Windows applications. Windows ML is a high-performance, reliable API that helps deploy hardware-accelerated ML inference on Windows devices.

Windows ML is integrated into the latest versions of Windows 10 and Windows Server 2019 and can also be used as a NuGet package for backward compatibility with Windows 8.1. It provides developers with the following benefits:

• Simplified Development: Thanks to the latest versions of Windows 10 and Windows Server 2019 natively integrated Windows ML, you only need Visual Studio and a trained ONNX model that you can bundle with your Windows applications. Additionally, if you need to extend your AI driver capabilities to older versions of Windows (as low as 8.1), Windows ML is available as a NuGet package for distribution with your application at any time.
• Extensive hardware support: Windows ML allows you to write ML workloads once and achieve highly optimized performance on a variety of hardware vendors and silicon types including CPUs, GPUs, and AI accelerators. Additionally, Windows ML ensures consistent performance across the supported hardware range.
• Low latency and real-time results: ML models can be evaluated using the processing power of Windows devices, enabling local real-time analysis of large data sets such as images and videos. The results are immediately and effectively provided to performance-intensive tasks (such as game engines) or background processes (such as index search).
• Enhanced flexibility: The ability to evaluate ML models locally on Windows devices allows you to handle a wider range of scenarios. For example, ML model evaluation can be performed even if the device is offline or the connection is interrupted. This also addresses privacy or data sovereignty issues that prevent all data from being transferred to the cloud.
• Reduce operational costs: Training ML models in the cloud and then evaluating them locally on Windows devices can significantly reduce bandwidth costs because the minimum data is sent to the cloud, which may require continuous improvements to your ML model. Furthermore, this approach minimizes the impact on cloud resources, thus reducing overall operating costs.

Click here to learn more.

Summarize

Artificial intelligence in Windows systems is very useful. It can simplify your work and help you solve puzzles faster. You will find more and more AI features in Windows 11 24H2. Let's look forward to it together.

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