编辑丨量子位 点击下方 卡片 ，关注" 具身智能之心 "公众号 >> 点击进入→ 具身 智能之心 技术交流群 更多干货，欢迎加入国内首个具身智能全栈学习社区 ： 具身智能之心知识星球 (戳我) ， 这里包含所有你想要的。 人多，好办事。agent多，照样好办事！ 在最新的Andrew's Letters中， 吴恩达 老师 就指出： 并行智能体正在成为提升AI能力的新方向。 信中，他描绘了这样的一些场景： 在这些场景中，多个agent协作，就像一支高效的agent team同时处理不同任务，速度快、效率高。 此外， 大语言模型token成本的不断下降，也让多个agent并行处理的方法变得可行。 多个agent 并行抓取分析网页， 更快速地生 成深度研究报告 。 多个agent 协同处理代码库的不同部分， 加快编程任务完成速度。 多个agent 在后台并行工作， 同时由一个 监督agent向用户提供反馈， 实现并行异步控制。 但就像网友指出的：如何协调多个agent呢？ 这为我们理解AI能力的提升提供了新视角—— 不仅仅依靠更多的数据和算力，更重要的是让多个智能体 协同并行 工作。 并行智能体才是未来 以往，当我 ...

Core Viewpoint - The article emphasizes the emerging importance of parallel agents in enhancing AI capabilities, suggesting that collaboration among multiple agents can significantly improve efficiency and speed in task execution [1][3][4]. Summary by Sections Parallel Agents as the Future - The traditional approach to improving AI performance has relied heavily on scaling laws, which focus on increasing data and computational power. However, the article argues that the future lies in the ability of multiple agents to work in parallel [4][8]. Validation of Parallel Agents - Andrew Ng cites his previous work at Baidu and OpenAI as evidence that parallel agent methodologies can yield faster results compared to conventional methods that often require lengthy processing times [5][6]. Challenges in Coordination - The article highlights the inherent challenges in coordinating multiple agents to perform complex tasks, such as web analysis or software development, which can be difficult even for human teams [9][10]. Recent Research Developments - Two recent papers are mentioned that contribute to the understanding of parallel agents: - The first paper discusses how large language models can generate multiple trajectories during inference to enhance problem-solving efficiency in programming [11][13]. - The second paper introduces the Together Mixture Of Agents (MoA) architecture, which utilizes multiple large language models simultaneously to improve performance and allows for adjustments in the hierarchical structure of agents [14][15]. Future Research Directions - Ng concludes that there is still much research and engineering work needed to optimize the use of parallel agents, suggesting that the number of agents capable of working efficiently in parallel could be substantial [18]. Historical Context - The article references Ng's 2009 paper that demonstrated the large-scale application of GPUs in deep learning, marking a significant milestone in the field and underscoring the importance of parallel processing [19][20].