【e公司观察】从智能手机到智能体，芯片厂商竞逐端侧AI

Core Insights - The focus on edge AI is growing among chip manufacturers, as it allows AI models to be deployed on end devices, enhancing local processing capabilities without relying on cloud servers [1][2][3] Group 1: Edge AI vs. Cloud AI - Edge AI processes data locally, resulting in faster processing speeds and improved data security, as personal data remains on the device [1] - Cloud AI involves training and inference tasks being handled by cloud servers, which can support larger models but may introduce latency and data security concerns [1] Group 2: Industry Trends and Applications - Qualcomm's CEO highlighted a shift towards AI-driven user interfaces, indicating that devices like smartwatches and wireless earbuds are evolving to interact directly with AI agents [2] - Media reports suggest that edge AI applications are emerging, such as personalized travel planning that considers users' schedules [2] - MediaTek also emphasized edge AI capabilities in its flagship chip, claiming significant enhancements in AI computation and image recognition, reducing reliance on cloud services [3] Group 3: Future Developments - Qualcomm is working on a new computing architecture to support the demands of edge AI, which includes redesigning operating systems, software, and chips [3] - The potential for edge AI extends beyond consumer devices to industrial applications, where sensors can analyze data streams and make decisions [3] - The narrative around edge AI is just beginning, with expectations that various sectors, including manufacturing and retail, will integrate AI capabilities into their operations [3] Group 4: Collaboration Between Edge and Cloud - Emphasizing edge AI does not diminish the importance of cloud AI; the ideal scenario involves seamless collaboration between edge and cloud processing for efficient task distribution [4]