Core Insights - The article discusses a groundbreaking research by JD's Exploration Research Institute on large models, which has been published in a Nature journal, focusing on a system that trains and updates large models in open environments while collaborating with smaller models [1][2]. Group 1: Innovations and Efficiency - The research introduces four innovative methods that enhance inference efficiency by an average of 30% and reduce training costs by 70% [8]. - The four innovations include model distillation, data governance, training optimization, and cloud-edge collaboration [1][11]. - Model distillation employs dynamic hierarchical distillation technology, achieving efficient training in low-resource scenarios by adjusting only 0.5% of parameters, thus lowering deployment costs for large models [5][11]. Group 2: Practical Applications and Solutions - JD's large model development technology supports enterprises in model training and production, transforming bulky AI models into efficient smaller models without losing intelligence [3][4]. - The JoyBuild platform offers customized solutions for large model development and industry applications, enabling rapid transformation of general models into specialized models tailored to business needs [10][12]. - The platform can complete the entire process from data preparation to model deployment in less than a week, significantly reducing the required workforce from over 10 scientists to just 1-2 algorithm personnel, and saving 90% on inference costs [10]. Group 3: Data Governance and Optimization - The data governance method involves cross-domain dynamic sampling algorithms that automatically mix data from different fields while incorporating privacy protection and active learning techniques to enhance the generalization ability of large models [11]. - Training optimization utilizes a Bayesian optimization framework for hyperparameter tuning and architecture search, improving resource utilization by 40% in MPMD scenarios [11]. Group 4: Future Prospects - JD aims to further enhance the efficiency of large model development and computation, enabling both small and large enterprises to build proprietary AI applications at low costs and drive the large-scale application of AI [12].

Core Insights - JD Cloud launched nine products including the JoyScale AI computing platform, JoyBuild large model development platform, and JoyAgent intelligent agent, aimed at helping enterprises reconstruct AI infrastructure and accelerate deep application adoption [1] - The company emphasized that the employment rate of digital employees will become a standard for measuring enterprise advancement, indicating that the extent of AI integration will determine future operational speed [1] - The new generation of agents, represented by JD Cloud's JoyAgent 2.0, is designed to assist enterprises in generating specialized digital employees, marking a significant step towards large-scale application and standardization of AI infrastructure [1]