Windows 11 has quietly added a new feature to your PC: AI-powered editing to the Windows 11 Notepad app, called Rewrite. In November, Microsoft began testing the addition of AI rewrite capabilities in Notepad, and then developed it further as part of a February update. Now, Windows Latest has reported that the AI-powered Notepad appears to have rolled out to everyone. It has certainly shown up on my PCs, whether they're running the stable version of Windows or one of its test Insider editions. In the upper right-hand corner of Notepad, you should see a little "magic pen" icon. You may not see it if you don't subscribe to Microsoft 365, as those AI capabilities appear to be part of Microsoft's productivity subscription.

In addition to today's launch of OpenAI's GPT-4, which is now confirmed to be the GPT model running in Bing, Microsoft also announced the stable version of its Edge web browser will now include the new Bing AI chatbot in a sidebar. The feature was first introduced at Microsoft's AI press event in February but was previously only available as a developer preview, not a public release. With today's official unveiling of GPT-4, Microsoft is shipping the feature, which it calls the "Edge Copilot," in the stable version of its Microsoft Edge browser. The update reimagines the concept of the sidebar, which previously hosted Edge's "Discover" feature to provide users with context about the page they're visiting. Now the new sidebar will offer an AI chatbot instead.

If you want to be a successful data scientist or AI engineer, you must master the various deep learning frameworks that are currently available. In this article, we'll enlighten you about the best option for data scientists. TensorFlow and PyTorch both provide valuable abstractions that make model creation easier by minimizing boilerplate code. They vary in that PyTorch takes a more "pythonic" approach and is object-oriented, whereas TensorFlow provides a wide range of possibilities. PyTorch is used for many deep learning projects today, and its popularity among AI researchers is growing, despite being the least popular of the three main frameworks.

If you do not have the time to read the full article, consider reading the 30 seconds version. If you have Machine Learning (ML) pipelines in production, you have to worry about backward compatibility of changes made to the pipeline. It may be tempting to increase test coverage, but a high test coverage cannot guarantee that your recent changes have not broken the pipeline or generated low quality results. To do that, you need to develop end-to-end tests that can be executed as part of the continuous integration pipelines. Developing such a test requires sampling the dataset that powers the pipeline from a run that produces acceptable result and on which you have an in-depth knowledge.