南方财经记者李振 广州报道 "十五五"时期是基本实现社会主义现代化承上启下的关键攻坚期。 尤其在国际环境复杂多变、国际规则深度重塑的大背景下，"十五五"规划建议的制定与实施，对于引领 未来五年乃至更长远的经济社会发展方向，有着无可替代的关键作用。 12月8日，《中共广东省委关于制定广东省国民经济和社会发展第十五个五年规划的建议》（以下简 称"规划建议"）正式印发。规划建议系统总结了"十四五"时期广东发展取得的重大成就，并对未来5年 发展作出顶层设计和战略擘画。 面向"十五五"，广东列出了一份详实的"任务清单"。下一个五年，经济总量连续36年位居全国首位的广 东站在了"要和自己比"的新节点上，提出了内需拉动经济增长主动力作用持续增强、全面融入全国统一 大市场、科技创新和产业创新深度融合等一系列目标。 南方财经记者梳理发现，在规划建议中，"产业"一词被提及多达109次，体现出产业建设在广东未来五 年发展规划重点的核心地位。值得注意的是，规划建议还首设8大"专栏"，鼓励发展低空经济、智能经 济、首发经济、湾区经济、绿色经济、人文经济、体育经济、银发经济等八大新经济业态。 而在各地"十五五"规划建议中，均提出要扩大内 ...

第二个瓶颈则来自高企不下的成本。"现在AI推理的成本偏高，限制了AI的大规模应用。"郑文先指出， 大模型训练所需的算力资源、数据中心背后的电力和用能支出等一系列成本，最终都会叠加到企业身 上，推高整体研发投入，使AI产品和解决方案的商业化承压。 对于外界反复讨论的"AI泡沫"问题，郑文先态度相对理性，"泡沫和繁荣本就是一体两面。"他认为，人 工智能作为第四次工业革命的关键性技术，未来必然是一个底层"根技术"，将深度嵌入各个产业的发展 进程之中，"即便存在泡沫，也只能说是阶段性的短暂过热。随着各种壁垒和瓶颈不断被打通，未来仍 将是一项具有划时代意义的技术。" 郑文先介绍，云天励飞自2014年成立起就在持续投入AI推理芯片研发，已推出四代基于深度神经网络 架构的自研NPU，并基于最新的NPU架构推出多款芯片，可应用于端侧与边缘侧AI推理场景。 郑文先还谈到，公司正在研发的新一代芯片会采用GPNPU架构，"一方面更好适应GPU的CUDA生态， 为客户模型牵引提供方便；另一方面又兼顾NPU的高效和灵活。"他表示，这种架构下的产品在成本端 更具优势，也更符合未来大模型在端侧与边缘侧规模化落地的实际需求。 21世纪经济报 ...

Core Insights - The 20th "EY Entrepreneur Of The Year" awards were announced, recognizing 12 outstanding entrepreneurs from mainland China and Hong Kong/Macau, emphasizing innovation and commitment to the private economy in the new era [1] - The theme of this year's awards is "Twenty Years of Unwavering Original Intent, Building Dreams in the New Era," highlighting the importance of technology and innovation in driving business success [1] - The awards align with China's 14th Five-Year Plan, focusing on high-level technological self-reliance and the development of new productive forces [1][2] Group 1: Award Significance - The awards serve as a platform for entrepreneurs to exchange ideas and collaborate, reflecting the pulse of national economic development [2] - The event took place in Hong Kong, leveraging its unique position as a gateway for mainland companies to expand internationally [2] - The recognition of entrepreneurs is crucial for promoting new productive forces and supporting the construction of a modern economic system in China [2][3] Group 2: Industry Representation - Award winners come from various sectors, including technology, life sciences and healthcare, manufacturing, and services, all demonstrating innovation through technology [1][3] - The private economy has become an indispensable force in driving high-quality economic development and deepening global economic cooperation [3] - The implementation of the "Private Economy Promotion Law" in May aims to create a stable and transparent legal environment for private enterprises [3] Group 3: Notable Winners - In the technology sector, notable winners include: - Chen T, CEO of Shenzhen Yuntian Lifei Technology Co., Ltd. - Che, CEO of Yunzhisheng Intelligent Technology Co., Ltd. - Guijia Ya, Chairman of Simu Technology [5] - In the manufacturing sector, winners include: - Ge Jiang, Chairwoman of Weisheng Information Technology Co., Ltd. - Li Peiliang, Chairman of Dongjiang Group (Holdings) Limited [6] - In the life sciences and healthcare sector, Lu Xianping, Founder and CEO of Shenzhen Microchip Biotechnology Co., Ltd., was recognized [8]

Group 1 - The State Intellectual Property Office of China has granted a patent to Qingdao Yuntian Lifa Technology Co., Ltd. and Shenzhen Yuntian Lifa Technology Co., Ltd. for a method and device for monitoring objects on water surfaces, with the patent announcement number CN115841619B and application date of December 2022 [1] - Qingdao Yuntian Lifa Technology Co., Ltd. was established in 2017, located in Qingdao, primarily engaged in technology promotion and application services, with a registered capital of 5 million RMB. The company has participated in 54 bidding projects and holds 102 patents, along with 8 administrative licenses [1] - Shenzhen Yuntian Lifa Technology Co., Ltd. was founded in 2014, located in Shenzhen, mainly involved in software and information technology services, with a registered capital of 358.82666 million RMB. The company has invested in 33 enterprises, participated in 276 bidding projects, holds 415 trademark registrations, 1872 patents, and has 34 administrative licenses [1]

Core Viewpoint - The A-share market shows a mixed trend with the aviation sector performing well while the robotics sector experiences fluctuations, influenced by recent developments in humanoid robotics and U.S. policy shifts towards robotics technology [1][5][7]. Robotics Sector Performance - As of 11:30, the Robotics ETF (159213) saw a slight increase of 0.52%, with over 1.7 million yuan flowing into the fund yesterday [1]. - The top components of the Robotics ETF include significant gains from companies like Dazhong Laser, which rose over 4%, while others like Huichuan Technology and iFlytek experienced minor declines [3][4]. U.S. Robotics Policy Developments - The U.S. is shifting focus towards robotics, planning to release an executive order on robotics technology next year, following a five-month acceleration of AI development plans [5][7]. - The U.S. Department of Commerce is actively meeting with robotics CEOs, and a national robotics committee is being proposed, indicating a strong governmental push towards the robotics sector [7]. Impact on Robotics Industry - The entire robotics supply chain is expected to benefit from increased competition and policy support, enhancing the industry's maturity and technological capabilities [8][9]. - Key areas poised for growth include core components, complete machine manufacturers, and application scenarios, with a focus on cost reduction and market expansion [9][10]. Broader Industry Opportunities - The high-end manufacturing sector is anticipated to improve due to advancements in robotics, potentially increasing competitiveness against foreign high-end manufacturing [10]. - The AI and technology supply chains, including algorithms, chips, and sensors, are also expected to see benefits from the growth of the robotics industry [11]. - Long-term, robotics is projected to enhance productivity across various sectors, including daily use, defense, and elder care, indicating a broad demand for robotic solutions [12].

AI芯片板块领跌，下跌1.56%，其中景嘉微下跌3.4%，寒武纪下跌3.36%，海光信息下跌3.34%，国科 微、云天励飞、瑞芯微跌超2%。（AI生成） AI芯片板块领跌，下跌1.56%，其中景嘉微下跌3.4%，寒武纪下跌3.36%，海光信息下跌3.34%，国科 微、云天励飞、瑞芯微跌超2%。（AI生成） ...

中证报中证网讯(记者齐金钊)日前，在2025GIS全球创新峰会上，云天励飞董事长兼CEO陈宁与"AI教 父"杰弗里.辛顿亮相峰会对话环节，围绕算力效率、AI向善与普惠未来等话题进行了深度对话。双方共 同认为，AI的未来，不再只是"更聪明"的竞赛，而是"更高效、更安全、更普惠"的系统性竞争。陈宁表 示，云天励飞将推出通用神经网络处理器架构，推动AI生成的成本和效率大幅提升。 陈宁介绍，云天励飞已向国际相关机构提出建议，希望推动建立统一的AI处理芯片与推理网络标准， 让不同国家和地区都能在同一张互联互通的推理网络上共享能力，尤其在医疗与教育等关乎民生的领域 真正实现"AI for All"。从"更聪明"到"更高效"，从"能不能做到"到"能不能让更多人用得起、用得好"。 陈宁认为，GPU(图形处理器)在深度学习早期扮演了重要角色，但本质仍是通用计算架构，并非为神经 网络量身定制。 结合产业发展现状，陈宁预判，到2030年，全球AI芯片产业规模有望达到约5万亿美元，其中训练芯片 约占1万亿美元，而面向终端与行业侧的推理/处理芯片有望达到4万亿美元，占比约80%。随着智能体 能力持续下沉，AI处理芯片将被广泛嵌入眼镜、 ...

Core Insights - The dialogue emphasized the importance of AI safety governance and the need for AI to develop in a "good" direction, with a consensus among experts that AI should serve humanity rather than deceive it [1][2]. Group 1: AI Development and Governance - AI systems have a learning efficiency and knowledge transfer speed that is exponentially higher than that of humans, with improvements by several billion times [2]. - The global community is encouraged to collaborate in ensuring AI develops safely and beneficially for all [2]. Group 2: AI Training Costs and Efficiency - Training large AI models currently incurs costs in the range of billions of dollars, prompting discussions on how to reduce these costs significantly [2][3]. - The goal is to lower the cost of generating tokens from $1 to just $0.01, representing a 99% reduction [2]. Group 3: Future of AI Chips - By 2025, the AI industry is expected to transition from a training phase to an application reasoning phase, focusing on low-cost and low-power reasoning chips [3]. - The market for reasoning chips is projected to reach nearly $4 trillion by 2030, surpassing the $1 trillion market for training chips [4].

Core Insights - The future of AI is shifting from a competition of "smarter" systems to a systemic competition focused on "more efficient, safer, and more inclusive" solutions [1][8] Group 1: AI Bottlenecks and Efficiency - The bottleneck in AI is transitioning from "algorithms" to "computational efficiency," with current computing systems facing increasing pressure on energy consumption and efficiency [2][3] - Geoffrey Hinton emphasizes the need for exploration in new computing paradigms such as simulated computing and brain-like chips, although current research in organoid-based computing is still in its early stages [2] - Cloud Tianli's CEO Chen Ning highlights the limitations of GPUs in deep learning and proposes a new architecture, GPNPU, aimed at reducing the cost of generating 1 million tokens from approximately $1 to $0.01, achieving a hundredfold efficiency improvement [2][3] Group 2: AI for Good - Hinton stresses the importance of addressing AI risks proactively, advocating for a dual approach that advances both AI capabilities and safety measures [4] - Chen Ning adds that meaningful AI must be accessible to a broader population, not just a select few, emphasizing that AI usage costs should be reduced to the level of basic utilities [5] Group 3: Global Competition and Market Outlook - Both Hinton and Chen view "inclusive capability" as a core metric for future competition, with Hinton noting the strengths of different regions in algorithm development and hardware manufacturing [6] - Chen predicts that by 2030, the global AI chip industry could reach approximately $5 trillion, with training chips accounting for $1 trillion and inference/processing chips making up about $4 trillion [7] - To ensure global accessibility, Cloud Tianli has proposed the establishment of unified AI processing chip and inference network standards to facilitate shared capabilities across countries, particularly in critical sectors like healthcare and education [7]

Core Insights - The dialogue emphasized the importance of AI safety governance and the need for AI to develop in a "good" direction, as highlighted by Jeffrey Hinton, a prominent figure in AI research [5][6][8] - The transition from AI training to application reasoning is expected to occur by 2025, with a significant focus on reducing AI training costs and improving efficiency [7][14] Group 1: AI Safety and Governance - Jeffrey Hinton reiterated the necessity of ensuring AI develops safely and beneficially for humanity, stating that AI's learning efficiency surpasses human capabilities by billions of times [5][6] - The consensus among experts is that while AI development cannot be halted, measures must be taken to ensure its safety and ethical use [5][6] Group 2: Cost Reduction in AI Training - The current cost of training large AI models can reach billions of dollars, and there is a strong push to reduce this cost significantly, aiming to lower it from $1 to just $0.01 per token [8][14] - Chen Ning emphasized that making AI affordable and accessible to a broader population is crucial for its meaningful application in various sectors, including education and healthcare [6][8] Group 3: Future of AI Chips - The industry is transitioning from training chips to reasoning chips, with predictions that the market for reasoning chips could reach $4 trillion by 2030, surpassing the $1 trillion market for training chips [14] - Chen Ning highlighted the potential for AI to redefine digital applications and consumer electronics, suggesting that AI processing chips could become as ubiquitous as utilities like water and electricity [14]