The updated version of this presentation provides additional information on the applicability of artificial intelligence in modern medicine, shows more insights into the end-to-end life cycle of AI implementations in projects and gives more details of our software stack.
In this presentation, I give an introduction into microprocessor trends, describe two distinct eras of computing usage in training AI systems and show the wide variety of computing architectures in computer science. I also describe our advanced computing and artificial intelligence product portfolio, which focuses on innovation, continuous dedication and backward compatibility. The central part of this talk is insights into our Da Vinci architecture, descriptions of all building block, the core architecture and its micro-architectural configurations. Last, I show the process of how we execute artificial intelligence projects and the challenges which are still ahead of us.
Artificial intelligence will shape our future like no other technology. Since the role of this technology is expanding, it will optimise and improve what people do. In the medical field, artificial intelligence will help support diagnostic processes and other related processes. To bring artificial intelligence to clinical relevance, the technology industry, the medical industry and physicians are required to solve complex challenges in an interdisciplinary approach.
This week I had the honour and pleasure to give a talk at the "Emerging Technologies in Medicine" conference, which brings experienced physicians, engineers and computer scientists together to talk about topics related to the future and exchange opinions for the challenges ahead. In my talk, I presented how Huawei supports the health care industry with artificial intelligence and described the process of how we execute AI projects. I described the challenges of microprocessor trends and introduced computer architectural approaches to solve these challenges, and showed Huawei's rich AI product portfolio. I also showed where AI, specifically with our products, has been successfully used in medical research such as in retinal blood vessel segmentation in the eyeground and the prediction of protein subcellular localisation.
There are still challenges ahead, but with collaborative approaches such as this, I think we will be better equipped in facing our future.