人工智能技术与应用前沿

摘要：人工智能（AI）浪潮正以前所未有的深度和广度重塑人类社会的生产与生活方式。在AI技术层面，大模型的架构创新、研发范式变革、多模态大模型算法以及大模型训推基础设施不断突破。同时，AI工程化应用显著加速，端侧AI和具身智能等新型产品与应用取得了重要进展。DeepSeek通过在模型算法和工程优化上的系统级创新，为资源受限环境下探索通用人工智能开辟了全新路径。未来，人工智能的核心竞争力将取决于：AI算法及模型架构的持续创新，AI基础设施对算力、存储、网络等资源的高效综合调度能力，AI应用的快速迭代与场景化落地能力。本文以大模型技术为主线，系统分析梳理了大模型架构与研发范式、AI基础设施、大模型应用技术等领域的最新进展与未来发展方向，为人工智能研究与实践提供参考。

关键词：大模型技术；大模型训推基础设施；强化学习；思维链；智能体

Abstract: The wave of artificial intelligence is reshaping the production modes and lifestyles of human society with unprecedented depth and breadth. At the AI technology level, there are continuous breakthroughs in the architectural innovation of large models, changes in R&D paradigms, multimodal large model algorithms, and AI infrastructure. At the same time, the engineering application of AI has been significantly accelerated, and new products and applications such as end-side AI and embodied intelligence have made important progress. DeepSeek has opened up a new path for exploring general artificial intelligence in resource-constrained environments through system-level innovations in model algorithms and engineering optimization. In the future, the core competitiveness of artificial intelligence will depend on: the continuous innovation of AI algorithms and model architectures, the efficient and comprehensive scheduling capabilities of AI infrastructure for computing power, storage, network and other resources, and the rapid iteration and scenario-based landing capabilities of AI applications. This article takes large model technology as the main line, systematically analyzes and sorts out the latest progress and future development directions in the fields of large model architecture and R&D paradigms, AI infrastructure, and large model application technology, and provides a reference for artificial intelligence research and practice.

Keywords: large model technology; AI infrastructure; reinforcement learning; chain of thought; AI agent