Issue link: https://resources.mouser.com/i/1442826
3 AI rtificial intelligence (AI) and machine learning (ML) require an inseparable combination of processing power and software. The shift of high-performance processing from the cloud to the edge significantly decreases bandwidth requirements, reduces latency, and maintains privacy. These are the key factors for automotive, industrial, and an infinite range of Internet of Things (IoT) applications. NXP provides the processing performance that scales to support a three-axis trade-off between accuracy, inference time (user experience), and cost. Beginning with an optimized portfolio of ICs that range from low- end MCUs to high-end application and networking processors, customers can select the exact device for their Machine Learning applications. Software is the second ingredient required for AI deployment, and it is clear that proprietary solutions for machine learning deployment will never be able to keep up with the rapid progress happening with open source. NXP's mission is to enable open source options, such as TensorFlow Lite, GLOW, Arm NN, and others, and apply device-specific optimizations as needed to achieve more competitive results. This comprehensive offering allows our customers to implement Machine Learning across the landscape of automotive, industrial, and IoT applications easily. In this eBook, NXP will share its commitment to AI, communicating how our company's solutions are incorporating and using this burgeoning technology. Before we dive in, let's clarify how we—NXP—think about a few key terms you will see throughout this book: • Artificial Intelligence (AI) refers to the very broad concept of using machines to do "smart" things and act intelligently, like a human. Solutions continue to be very specialized and limited. • Machine Learning (ML) is one of many ways to implement AI. The concept is that if you give self-learning machines a large amount of data, they can learn how to do smart things on their own, without being explicitly programmed for every action. This is happening now and rapidly expanding. • Deep Learning is one of many ways to implement ML, commonly using Neural Networks (NN) for the learning phase, to automatically determine the most relevant data aspects to analyze in order to infer the most appropriate response. NN techniques need copious amounts of data during the training phase, so there is still much opportunity for improvement here. Take a look at how NXP, a leader in the semiconductor industry, employs AI technology to reshape solutions, products, applications, and industries for tomorrow and well into the future, and allow yourself to reimagine what is truly possible. ■ Imagine the Possibilities! Foreword VOICE CONTROL, FACIAL RECOGNITION, AND ANOMALY DETECTION AT CES 2019 By Paul Golata, Senior Technologist, Mouser Electronics ❝ ❞ Deep learning is a subset of machine learning, and machine learning is a subset of Artificial Intelligence, which is a broad term for any machine that does something "smart." A