Core Insights - A new wave of artificial intelligence (AI) is transitioning from the cloud to various devices, reshaping human-machine interaction through AI-driven terminal experiences [1][2] - The rise of edge intelligence is not meant to sever ties with the cloud but to create a "cloud-edge collaborative" intelligent model that balances experience, cost, and security [1][2] Group 1: Key Drivers of Edge Intelligence - Real-time performance and safety are critical, especially in fields like robotics and autonomous driving, where even milliseconds of delay can pose safety risks [1][2] - Data privacy and compliance necessitate local processing of sensitive information, requiring robust models to be deployed on the edge [1][2] - Balancing cost and efficiency involves placing simpler, high-frequency tasks on the edge while reserving complex tasks for the cloud, enabling scalable commercial deployment [2] - Global collaboration and optimization are essential, with the cloud serving as a management platform that integrates dispersed devices for optimal system performance [2] Group 2: Ecosystem and Role Distribution - The ecosystem requires collaboration among hardware manufacturers, algorithm developers, and comprehensive AI cloud platforms like Alibaba Cloud, which provides a full-stack service from computation to application development [2][3] - Clear role delineation exists within the ecosystem, with hardware vendors supplying physical devices, algorithm companies focusing on model development, and platforms like Alibaba Cloud acting as the "intelligent foundation" [2][3] Group 3: Implementation Strategies - Companies are adopting a hybrid approach of edge and cloud deployment, tailoring their technology paths based on customer needs and task complexity [4][5] - Successful applications of cloud-edge collaboration have been demonstrated in educational settings, where real-time feedback and evaluations are generated efficiently [10] Group 4: Technical Challenges and Solutions - Technical hurdles in scaling edge AI include model lightweighting, computational adaptation, and inference optimization, particularly in privacy-sensitive industrial applications [8][9] - The need for real-time, robust, and secure operations in edge devices drives the requirement for local model deployment, while complex learning processes are handled in the cloud [8][9] Group 5: Future Directions and Innovations - Alibaba Cloud is committed to enhancing its infrastructure and model services to support diverse industry needs, focusing on open-source models and customizable solutions [12][11] - The development of multi-modal interaction capabilities is underway, enabling devices to understand and respond to user inputs more effectively [13]
云端携手：AI如何定义下一代智能终端？
3 6 Ke·2025-09-05 03:54