Core Viewpoint - The 2025 Global Investor Conference emphasizes the new productive forces in China and the investment opportunities in Shenzhen's open innovation market, particularly in the biopharmaceutical sector and technology advancements [1][4]. Biopharmaceutical Industry - China's biopharmaceutical sector is rapidly advancing, producing world-class products at lower costs. By 2024, 30% of global big pharma's License-in deals will come from Chinese biotech startups, a significant increase from 0% in 2019 [1][7]. - The shift from being a License-in country to a License-out origin indicates China's growing influence in the global pharmaceutical landscape [7]. Technology and AI Development - The emergence of DeepSeek, a leading AI company, showcases China's capabilities in AI, with its models achieving results comparable to international counterparts at a fraction of the cost [5][6]. - The global perception of China's AI capabilities has shifted, with reports indicating that the gap between Chinese and leading international AI technologies has narrowed to just a few months [6]. Globalization of Chinese Tech Companies - Chinese tech companies are increasingly expanding their international presence, with notable examples including BYD, CATL, ByteDance, Alibaba, and Xiaomi, all showing rising overseas revenue proportions [8][9]. - Smaller tech firms, such as Stone Technology and Insta360, are also achieving significant global market shares, demonstrating the competitive strength of Chinese innovation [10]. Investment Opportunities - The venture capital industry in China plays a crucial role in supporting technological innovation, with a focus on sectors like AI, advanced manufacturing, healthcare, and renewable energy [11][12]. - The potential for investment in Chinese technology is underscored by the fact that six out of seven companies founded after 2010 that have reached a market value of over $100 billion are from China [12]. Future Growth Areas - Key areas for future investment include artificial intelligence, biopharmaceuticals, and renewable energy, driven by demographic changes and technological advancements [13]. - The expectation is that by 2025, AI will significantly empower various industries, creating substantial investment opportunities [13].

专题：深交所2025全球投资者大会：新质生产力 投资中国新机遇——开放创新的深圳市场 5月19日至20日，深交所2025全球投资者大会在深圳举行。施罗德投资北亚区行政总裁、亚太区业务策 略主管GopiMirchandan分享最新观点。 在谈到半导体、IT行业时，GopiMirchandan称，通用生成式AI和机器人的发展至关重要。通用生成式AI 能够执行类似人类的任务，提升某些行业的生产效率、降低人工成本。服务式机器人、工业机器人以及 自动驾驶车辆等，将快速推动行业发展。而这些AI智能体和机器人的运行需要巨大算力及算力中心， 因为它们要持续执行任务。因此，他预计对云算力的需求将大幅提升，硬件和芯片提供商将从中受益。 同时，确保算力在经济上的可行性，对维持这些进展和发展十分关键。 在环境领域，施罗德也有所行动。GopiMirchandan透露，公司成立了在中国的可持续基础设施团队，该 团队对中国的可再生能源具备专业知识。欧洲的GreenGoat作为新能源的大型提供商，施罗德希望将全 球专长与本地需求相结合，助力中国的环境提升，并通过中国的能源转型创造更多价值。作为"超级联 络人"，施罗德期望帮助外资企业在中国实 ...

Core Insights - The 2025 Global Investor Conference held in Shenzhen focused on "New Quality Productivity: Investment Opportunities in China" and highlighted the investment value of Chinese assets and the A-share market [1] - Morgan Stanley's Chief China Economist, Xing Ziqiang, emphasized China's unique position in the AI sector, being the only economy besides the US capable of achieving a closed-loop in the entire AI hardware and software chain [1] - China's AI industry benefits from significant advantages in talent, algorithms, infrastructure, and data, with nearly half of the global AI talent coming from China and over 3 million engineering graduates each year [1] Industry Development - The industrial chain cluster effect in regions like Guangdong and Shenzhen has significantly contributed to the development of the AI industry, with companies like DeepSeek thriving due to local talent and supply chain support [2] - The best enterprise recommendation event hosted by Morgan Stanley in Shenzhen received strong support from the Shenzhen Stock Exchange and the Shenzhen Financial Office, underscoring Shenzhen's core role in AI manufacturing applications [2] - China's AI industry has experienced a leap forward, alleviating global investors' concerns over the past three years and reigniting confidence in China's innovation capabilities and market [2] Technological Advancements - The fields of intelligent driving and humanoid robots are also reflecting the enhancement of China's industrial chain, with intelligent driving entering a more advanced stage and humanoid robots transitioning from experimental to practical applications [2] - Xing Ziqiang noted that China's industrial chain has significantly improved over the past seven to eight years, showcasing strong vitality and competitiveness, particularly in areas such as industrial chain clusters, engineering talent, robotics, and intelligent driving [2]

当前，我国生成式人工智能产业发展迅速，相关企业数量已经超过4500家。然而，生成式人工智能与实 体经济融合的深度和广度仍有待提升，其巨大潜力尚未充分释放。究其原因，一方面在于生成式人工智 能技术本身仍处于快速发展期，成熟度有待提高；另一方面，不同产业因其自身特性和发展阶段的差 异，对生成式人工智能技术的需求呈现显著差异。为此，提升生成式人工智能技术的通用性和适用性、 推动科技创新与产业创新深度融合成为当务之急。 二是商业转化临界点尚未到来，行业落地较为缓慢。当前，生成式人工智能技术大规模商业化应用的路 径不畅，在算力资源紧张与训练成本高企的背景下，企业在实际部署中对生成式人工智能创新的投入回 报比不够理想，不同产业类型的生成式人工智能商业落地路径呈现出明显梯度。传统产业整体数字化水 平有限，模型与业务系统之间数据集成基础薄弱，短期内难以形成规模效应；新兴产业在部分场景中已 实现探索性应用，但普遍仍处于"点状突破、多点未及"的阶段；而未来产业由于具备更高的成本容忍度 与对颠覆式创新的开放态度，被认为是生成式人工智能最具战略潜力的应用场景，但产业落地的不确定 因素更多。根据行业特点发挥科技金融等政策工具的分类施策 ...

机器之心报道 机器之心编辑部 以 AI 之矛，攻 AI 之盾。 就在今年春节，DeepSeek 官网遭遇 3.2Tbps 超大规模 DDoS 攻击，黑客同步通过 API 渗透注入对抗样本，篡改模型权重导致核心服务瘫痪 48 小时，直接经济损失 超数千万美元，事后溯源发现美国 NSA 长期潜伏的渗透痕迹。 数据污染和模型漏洞同样也是一种新威胁。攻击者通过在 AI 训练数据中植入虚假信息（即数据投毒），或利用模型自身缺陷，诱导 AI 输出错误结果 —— 这会对 关键领域造成直接的安全威胁，甚至可能引发连锁灾难性后果，例如自动驾驶系统因对抗样本误判「禁止通行」为「限速标志」，或医疗 AI 将良性肿瘤误判为恶 性。 AI 还需 AI 治 面对 AI 驱动的网络安全新威胁，传统防护模式已显乏力。那么，我们又有哪些应对之策呢？ AI 崛起：技术双刃剑下的安全暗战 随着 AI 技术的快速发展，网络安全面临的威胁日益复杂化，攻击手段不仅更高效、隐蔽，还催生了新型的「AI 黑客」形态，因此引发了各类新型网络安全危机。 首先是生成式 AI 正重塑网络诈骗的「精准度」。 简单而言，就是将传统的钓鱼攻击智能化，比如在更精准的场景中， ...

Core Viewpoint - The open-source strategy of the phenomenon-level model DeepSeek is causing multi-faceted disruption, with potential commercial models comparable to the mature experiences of the open-source software industry [1] Group 1: Open-Source Strategy - DeepSeek is restructuring the global AI competitive landscape with performance comparable to GPT-4, innovative architecture, and a low-cost open-source strategy [1] - Unlike previous closed-source models, DeepSeek publishes core technologies and adopts a permissive MIT license to support free commercial use and secondary development, accelerating industry technology upgrades and expanding AI application scenarios [1] - The open-source model demonstrates strong externalities and positions "open-source" as a significant direction for global AI industry development [1] Group 2: Comparison with Red Hat - DeepSeek shares similarities with Red Hat in their open-source strategy and the early-stage industry development phase, with a focus on service as a sustainable revenue increment [2] - Both companies emphasize technology openness to drive industry development, which accelerates enterprise deployment and builds an ecosystem based on operating systems/AI models [2] - The commercial model of DeepSeek can draw from Red Hat's approach, focusing on addressing enterprise application pain points for sustainable revenue growth [2] Group 3: Market Adoption and Ecosystem Building - In the early stages of commercialization, the open-source model will attract widespread enterprise deployment of DeepSeek, helping to build a scalable ecological barrier [3] - Within 20 days of the official release of DeepSeek-R1, over 160 enterprises have connected, forming a multi-field cooperative ecosystem in the AI industry chain [3] - The open-source model lowers technical barriers and costs, accelerating technology accessibility and attracting various enterprises, including small and medium-sized businesses and government entities [3] Group 4: Revenue Model - In the mid-to-late stage, DeepSeek can achieve a commercial closure through "API call-based basic income + enterprise service subscription value-added income" [4] - The basic income will utilize a low-cost API call charging strategy, which is expected to reduce hardware investment costs through increased call frequency as the ecosystem expands [4] - Value-added income can be generated by providing technical subscription services to address the engineering deployment needs of enterprises using the model, transforming complex engineering issues into standardized service modules [4]

Core Insights - The 2025 Sohu Technology Annual Forum highlighted discussions on the timeline for achieving Artificial General Intelligence (AGI), with experts suggesting it may take 15 to 20 years for AGI to be realized [1][3] - AGI is defined as an AI system that possesses human-level or higher comprehensive intelligence, capable of autonomous perception, learning new skills, and solving cross-domain problems while adhering to human ethics [1][3] Group 1: Characteristics and Challenges of AGI - AGI can be understood through three aspects: generality, the ability for autonomous learning and evolution, and surpassing human capabilities in 99% of tasks [3] - Current challenges in achieving AGI include: 1. Information intelligence, which is expected to reach human-level capabilities in 4 to 5 years [3] 2. Physical intelligence, particularly in areas like autonomous driving and humanoid robots, which may take at least 10 years [3] 3. Biological intelligence, involving brain-machine interfaces and deep integration of AI with human biology, projected to require 15 to 20 years [3] Group 2: AI Development Trends - The forum identified two major trends in AI development by 2025: multimodality and applications closely related to GDP [4] - The lifecycle of AI large models includes five stages: data acquisition, preprocessing, model training, fine-tuning, and inference, with the first three stages requiring significant computational power typically handled by leading tech companies [5] Group 3: Perspectives on AI and Robotics - Current AI capabilities are perceived to potentially exceed human intelligence, yet it is viewed as an extension of human cognition rather than a replacement [5] - The development of humanoid robots is still in an exploratory phase, with a long maturation cycle ahead, emphasizing the need to create actual value [5]

清华大学计算机系教授、中国工程院院士郑纬民在关于人工智能大模型基础设施建设与应用探索的演讲 中表示，2025年人工智能发展呈现两大特点：一是多模态，二是应用于GDP密切相关的行业，其中中国 在推动AI落地方面具有显著优势。他进一步介绍，人工智能大模型的生命周期主要包括数据获取、预 处理、模型训练、微调和推理五个环节，前三个环节需要大量算力和存储资源，通常由阿里、华为、 DeepSeek等大型科技公司完成，一般单位只需基于已有基础模型进行领域适配的微调和后续的推理应 用。 "问道智能"圆桌论坛上，多位专家对于机器智能的认知能力和人形机器人未来发展进行了深入讨论。 多位嘉宾一致认为，AI并非人类的替代者，而是人类认知与能力的延伸。清华大学智能产业研究院院 长、中国工程院外籍院士张亚勤表示，在认知中人类仍是主宰，机器或者机器人还在从属地位。 中证报中证网讯(记者王婧涵)5月17日，2025搜狐科技年度论坛在北京举办，多位专家与产业界人士围 绕基础科学突破、技术革命产业化应用、人工智能与人类文明演进等议题进行了探讨。 搜狐创始人张朝阳表示，2024年以来，AI发展进入快车道，具身智能百花齐放。科技进步带来惊喜的 同时 ...

Core Insights - The article discusses recent advancements in utilizing "thinking time" during testing and its mechanisms, aiming to enhance model performance in complex cognitive tasks such as logical reasoning, long text comprehension, mathematical problem-solving, and code generation and debugging [4][5]. Group 1: Motivating Models to Think - The core idea is closely related to human thinking processes, where complex problems require time for reflection and analysis [9]. - Daniel Kahneman's dual process theory categorizes human thinking into two systems: fast thinking, which is quick and intuitive, and slow thinking, which is deliberate and logical [9][13]. - In deep learning, neural networks can be characterized by the computational and storage resources they utilize during each forward pass, suggesting that optimizing these resources can improve model performance [10]. Group 2: Thinking in Tokens - The strategy of generating intermediate reasoning steps before producing final answers has evolved into a standard method, particularly in mathematical problem-solving [12]. - The introduction of the "scratchpad" concept allows models to treat generated intermediate tokens as temporary content for reasoning processes, leading to the term "chain of thought" (CoT) [12]. Group 3: Enhancing Reasoning Capabilities - CoT prompting significantly improves success rates in solving mathematical problems, with larger models benefiting more from increased "thinking time" [16]. - Two main strategies to enhance generation quality are parallel sampling and sequential revision, each with its own advantages and challenges [18][19]. Group 4: Self-Correction and Reinforcement Learning - Recent research has successfully utilized reinforcement learning (RL) to enhance language models' reasoning capabilities, particularly in STEM-related tasks [31]. - The DeepSeek-R1 model, designed for high-complexity tasks, employs a two-stage training process combining supervised fine-tuning and reinforcement learning [32]. Group 5: External Tools and Enhanced Reasoning - The use of external tools, such as code interpreters, can efficiently solve intermediate steps in reasoning processes, expanding the capabilities of language models [45]. - The ReAct method integrates external operations with reasoning trajectories, allowing models to incorporate external knowledge into their reasoning paths [48][50]. Group 6: Monitoring and Trustworthiness of Reasoning - Monitoring CoT can effectively detect inappropriate behaviors in reasoning models, such as reward hacking, and enhance robustness against adversarial inputs [51][53]. - The article highlights the importance of ensuring that models faithfully express their reasoning processes, as biases can arise from training data or human-written examples [55][64].

Group 1 - DeepSeek-V3 addresses "hardware bottlenecks" through four innovative technologies: memory optimization, computation optimization, communication optimization, and inference acceleration [1] - Manus AI platform has opened registration, offering users free points and various subscription plans, indicating growing interest and potential for investment [1] - Nvidia has secured a significant chip supply agreement with Saudi Arabia's AI company Humain, providing 18,000 GB300 chips for a data center with a capacity of up to 500 megawatts [2] Group 2 - DeepSeek released a new paper detailing cost-reduction methods for the V3 model, emphasizing its ability to achieve large-scale training effects with only 2048 H800 chips [3] - Zhang Yaqin predicts that general artificial intelligence will take 15 to 20 years to achieve, highlighting the challenges in information, physical, and biological intelligence [4] - OpenAI is considering building a new data center in the UAE, which could significantly expand its operations in the Middle East [5][6] Group 3 - The US and UAE are collaborating to build the largest AI park in the Middle East, featuring a 5-gigawatt data center, showcasing the region's commitment to becoming an AI hub [7] - OpenAI launched a new AI programming assistant called Codex, aimed at simplifying software development processes, indicating a growing interest in generative AI tools [8] - Baidu has launched DeepSearch, a deep search engine based on a vast content library, marking a significant advancement in search technology [9] Group 4 - Google announced the establishment of the "AI Future Fund" to support AI startups, aiming to discover the next OpenAI and accelerate innovation in the field [10] - INAIR unveiled an AI spatial computer, set to launch in June, which combines AR glasses, a computing center, and a 3D keyboard, indicating advancements in AR technology [12] - Perplexity AI is in late-stage negotiations for a $500 million funding round at a $14 billion valuation, reflecting the company's growth amid the AI boom [13] Group 5 - Tencent reported a 91% year-on-year increase in capital expenditure in Q1 2025, primarily to support AI-related business development [14] - Tencent's president stated that the company has sufficient high-end chips to train future models, addressing the high demand for GPU resources in AI applications [15]