AI delivers value to many facets of the automotive value chain, including smart manufacturing, supply chain management, and customer engagement. It is increasingly used both inside the vehicle (e.g., in advanced driving assistance systems) and outside the vehicle to improve internal business processes and decisions (e.g., during vehicle development, manufacturing, and sales and aftersales).
Andre Luckow discusses how to assess AI technologies, validate use cases, and foster fast adoption and shares lessons and best practices learned from developing computer vision and natural language understanding applications. Andre focuses on deep learning applications, exploring convolutional neural networks for computer vision use cases, such as the visual inspection process in manufacturing plants, and discussing his experience developing an end-to-end deep learning application that utilizes a mobile app for data collection and process support and a cloud backend for storage and training.
Andre Luckow is a project manager and researcher at the BMW IT Research Center in Greenville, South Carolina, where his work focuses on interdisciplinary research and applications at the intersection of data infrastructure, data science, and machine learning in the automotive domain. His specialty is the application of computing technologies to problems in business and science bridging cross-functional gaps to create value via process improvements or the enablement of new types of products. He is particularly interested in deep learning applications and system-level challenges related to deep learning, streaming, and edge computing. Previously, Andre served in a number of positions at BMW Group IT in Munich, Germany. He holds a PhD in the field of distributed computing from the University of Potsdam, Germany.
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