 Let me walk you through our AI lab. I'll show you some exciting examples of the applications that you can build when you leverage the power of edge AI and the diversity of STM32 microcontrollers. Here we're using machine learning for vibration analysis to detect anomalies on this industrial motor. The system is built around the STM32 WB55 microcontroller and 3D accelerometers. The algorithm running on the STM32 will learn when the machine works well and once done, it will detect unusual patterns very accurately. This is an easy way to add predictive maintenance features to any machine and prevent downtime. To create this library, we used our AutoML tool, NanoAJI Studio. The resulting code can be deployed to any STM32 starting from the C0 series. It requires no expertise in machine learning and is completely free of charge. Our second demo is all about detecting a person's body posture. Here we're using a neural network model called MoveNet to identify 13 key points on a person and use them for pose estimation. It's a great feature to analyze movement during fitness workouts or for health and safety applications, for example to detect a fall. The system is built around the upcoming STM32 MP2 and the images are captured by a 5 megapixel camera. The algorithm is running on the neural processing unit of the STM32 MP2. This dedicated hardware accelerator is mandatory for this kind of computer vision application. This use case has been built using X-Linux AI, which provides users with an extensive framework to implement edge AI on STM32 MPUs. Our last demo is also about computer vision with a 3D camera stabilizer that tracks people autonomously. This feature is handled by the STM32 N6, our forthcoming microcontroller which features a dedicated edge AI IP called the Neural Art Accelerator. For this specific application, using a microcontroller makes a lot of sense because of their low power consumption and cost effectiveness. This smart gimbal embeds a camera that automatically tracks the subject using a person recognition algorithm combined with a tracking function. We used the STM32 Cube.AI software ecosystem to benchmark, optimize and compile this model for our neural microcontroller. With the Neural Art Accelerator, the STM32 N6 achieves unique performance in this range and opens the door to unique applications that were previously intractable or reserved to MPUs or GPUs. So this gives you a glimpse of the potential of embedding AI in STM32, keeping in mind that all the software tools provided here are free of charge. And now let's hear how FIFER Vacuum, an ST customer in the south of France, is using AI to transform their applications.