Core Insights - The fifth Intelligent Computing Innovation Forum was held in Hangzhou, focusing on the theme "Computing Relies on Intelligence, Computing for Intelligence," attracting international experts to discuss advancements in AI technologies and their applications across various scientific fields [1] Group 1: AI Model Development - Scientists are exploring the potential of AI in solving scientific problems, emphasizing that current large language models have not yet reached human-level reasoning capabilities [2] - The development of scientific foundational models requires collaboration with scientists to effectively tokenize and train diverse scientific data, addressing complex interdisciplinary issues [2] - The learning paradigm of foundational models is evolving through imitation learning, reinforcement learning, and autonomous learning, with a shift towards task processing applications [2] Group 2: Efficiency and Resource Consumption - The efficiency of foundational models is critical for large-scale AI application deployment, with a noted exponential increase in token consumption correlating with model capability improvements [3] - The cost of generating tokens decreases with higher reasoning efficiency, necessitating collaborative optimization across the industry to enhance model performance [3] Group 3: Practical Applications and Collaboration - The application of intelligent systems in dynamic environments is gaining attention, highlighting the importance of responsive robotics [4] - China is recognized for its leading capabilities in intelligent manufacturing, serving as an excellent testing ground for new technology applications [4] - There is a call for scientists worldwide to establish collaborative networks to enhance research outcomes and create new possibilities through cooperation [4]

此外，他还认为，数字世界的agent有效工作时间将会越来越长。当我们去衡量模型智能的时候，就是 去看它能够自主有效的工作多长时间。而其中的挑战就在于如何对模型的推理能力进一步增强，并提升 其泛化性。 姜大昕表示，agent除了在数字世界存在，还将走向物理世界。而agent将从经验学习走向自我进化。"人 类数据是有限且有偏见，单纯依靠人类数据无法超越人类智能。智能体将从自身与环境的交互中学 习。"他表示。（罗宁） 责任编辑：江钰涵 新浪科技讯 9月12日上午消息，在Inclusion·外滩大会现场，阶跃星辰创始人姜大昕表示，模型推理效率 是决定AI大规模落地应用的关键要素。而推理效率的提升需要两方面，一是产业上下游联合优化，系 统模型co-design。 他还提到，阶跃星辰和多个国产算力芯片合作，其Step3国产卡32K上下文推理效率最高可达DeepSeek R1的3倍。 姜大昕认为，从今年年初，大模型的应用已经从聊天的时代进入到做事的时代。在他看来，未来智能终 端的特点是会做事、总在场、有记忆、能进化。"下一代智能硬件，比拼的不是硬件，而是智能。"他总 结。 ...