4月18日15:00 2025年AI赛道竞争解析 欢迎扫码预约，来直播间互动赢好礼 直播亮点： 1、 DeepSeek横空出世，AI原生App竞争格局发生了哪些转变？ 2、 面对新的机遇和挑战，传统App如何开展AI转型？ 3、 哪些赛道被重点押注，不同App布局方式上又有哪些异同？ QuestMobile 研究总监 DeepSeek横空出世,AI原生App竞争格局发生了 哪些转变? 面对新的机遇和挑战，传统App如何开展AI转型? QuestMobile 解决方案经理 直播亮点 哪些赛道被重点押注,不同App布局方式上又有哪 些异同? 扫码预约 》》》》》》》》》 ...

AGI可能使先行者获得显著优势，通过突然出现的决定性"奇迹武器"改变军事力量平衡。例如，想象一种具备极高网络攻击能力的AGI系统，它 能够识别并利用敌方网络防御中的漏洞，实施一种"辉煌的首次网络打击"，彻底瘫痪对方的反击能力。这种首发优势可能扰乱关键战区的军事力 量平衡，带来各种扩散风险，并加速技术竞赛动态。 这一场景并非纯粹的科幻想象。随着大型语言模型和AI系统能力的不断增强，我们已经看到这些系统在软件开发、漏洞发现和攻击向量识别方面 表现出令人惊叹的能力。如果某个国家或组织首先掌握了这种技术，它可能在短时间内获得显著的战略优势，类似于早期核武器发展带来的地缘 政治震荡。 系统性力量转变 AGI可能引发国家力量工具的系统性转变，从而改变全球力量平衡。军事创新历史表明，能够采用新技术往往比率先实现科学或技术突破更为重 要。当美国、盟国和竞争对手的军事力量获得AGI并大规模采用时，它可能通过影响军事竞争的关键构成要素而颠覆军事平衡，如"隐藏者与发 现者"、"精确与大规模"或"集中与分散指挥控制"之间的关系。那些更好地准备好利用和管理AGI引起的系统性变化的国家可能获得极大的影响力 扩展。 " 欧米伽未来研究所 ...

Core Viewpoint - The article discusses the evolution of artificial intelligence (AI) from its inception to the current era of large models, highlighting key milestones, technological advancements, and the impact on various industries. Group 1: Birth of Artificial Intelligence (Mid-20th Century) - In 1950, Alan Turing proposed the "Turing Test," defining the philosophical goal of AI [3] - The term "Artificial Intelligence" was first used in 1956 at Dartmouth College, marking the transition from philosophical speculation to applied technology [3] - Early AI systems, like the IBM701, had limited computational power, executing only 16,000 operations per second, which is significantly less than modern devices [3] Group 2: Symbolism and Its Failures (1960-1970) - The 1960s saw the rise of "symbolism," where researchers attempted to simulate human reasoning through rule-based expert systems [4] - The MYCIN system developed in 1976 achieved near-expert accuracy in diagnosing blood infections, demonstrating the commercial value of expert systems [4][5] - The "Fifth Generation Computer Systems" project in Japan, launched in 1982 with an investment of $850 million, aimed to create intelligent computers but ultimately failed due to over-reliance on symbolic methods and hardware limitations [8] Group 3: Rise of Machine Learning (1990s-2000s) - The 1990s marked a shift to machine learning, moving from rule-based systems to data-driven approaches, allowing machines to learn from data rather than relying solely on hard-coded rules [10] - IBM's DeepBlue defeated a chess champion in 1997, showcasing the potential of machine learning in closed tasks [12] - The introduction of Google's PageRank algorithm in 1998 demonstrated the commercial value of data correlation, transforming search engines into profitable ventures [12] Group 4: Deep Learning Revolution (2010s-2020) - The 21st century saw the emergence of deep learning, enabling AI to automatically extract features through multi-layer neural networks [13] - AlphaGo's victory over a world champion in 2016 highlighted the capabilities of deep reinforcement learning [13] - The rapid increase in model parameters from 60,000 in LeNet-5 to 600 million in AlexNet illustrated the exponential growth in AI's capacity to handle complex tasks [14] Group 5: Era of Large Models (2021-Present) - The introduction of large pre-trained models like GPT-3 in 2020 has propelled AI towards general intelligence, showcasing advanced language understanding and generation capabilities [15] - Applications of generative AI have expanded across various fields, including content creation, programming assistance, and image generation, significantly enhancing productivity [16] - The competition between open-source and closed-source models has intensified, with companies like HuggingFace promoting open-source development while others like OpenAI focus on proprietary advancements [17] Group 6: Future Directions and Challenges - The future of AI is expected to focus on specialized models for high-value sectors such as healthcare and finance, emphasizing efficiency and cost-effectiveness [38] - The relationship between AI and human employees is anticipated to evolve into deeper integration, enhancing decision-making and innovation within organizations [38] - Ethical challenges and societal risks associated with AI, such as job displacement and privacy concerns, remain critical issues that need addressing [39]

Core Insights - OpenAI's user base has surpassed 800 million, representing approximately 10% of the global population, driven by the recent launch of GPT-4.5 and a new text-to-image feature inspired by Studio Ghibli [1][4] - The introduction of GPT-4.5 has significantly enhanced OpenAI's capabilities in language understanding, creativity, and multimodal processing, leading to a rapid increase in users [1][4] - OpenAI's valuation has reached $300 billion, nearly doubling since October 2024, making it the most valuable startup globally, surpassing ByteDance [3] User Growth and Engagement - The ChatGPT application saw millions of new users within five days, with a peak of one million users gained in just one hour, largely due to the popularity of the Ghibli-inspired features [2] - OpenAI is exploring a payment mechanism for artists whose styles are mimicked by the AI, indicating a shift towards monetizing artistic styles in AI-generated content [2] Technological Advancements - GPT-4.5 boasts 1 trillion activation parameters and a training dataset of 120 trillion tokens, with an extended context window of 256K, making it the most knowledge-rich model to date [4] - The model is designed to exhibit high emotional intelligence and reduced AI hallucinations, enhancing its ability to understand and respond to human intentions [4] Future Developments - OpenAI is accelerating the development of AI agents, which are expected to transform AI from a mere information tool to a highly intelligent productivity tool by 2030 [3] - The company plans to increase its GPU resources significantly to support the growing user base and demand for AI applications [5][6] Market Position - ChatGPT ranks first in both web and mobile platforms, with 400 million active users weekly and 175 million mobile users, according to a recent report [6] - The rapid user acquisition of DeepSeek, a competing AI startup, highlights the competitive landscape, with DeepSeek achieving 10 million users in just 20 days [7]

吴翼： RL 是机器学习这个大概念下一类比较特殊的问题。 曲凯： 今天我们请来了国内强化学习 (RL) 领域的专家吴翼，吴翼目前是清华大学交叉信息研究院 助理教授，他曾经在 OpenAI 工作过，算是国内最早研究强化学习的人之一，我们今天就争取一 起把 RL 这个话题给大家聊透。 首先吴翼能不能简单解释一下，到底什么是 RL？ 传统机器学习的本质是记住大量标注过正确答案的数据对。 举个例子，如果你想让机器学习能分辨一张图片是猫还是狗，就要先收集 10000 张猫的照片和 10000 张狗的照片，并且给每一张都做好标注，让模型背下来。 上一波人工智能四小龙的浪潮其实都以这套框架为基础，主要应用就是人脸识别、指纹识别、图 像识别等分类问题。 这类问题有两个特点，一是单一步骤，比如只要完成图片分辨就结束了；二是有明确的标准答 案。 但 RL 很不一样。 RL 最早是用来打游戏的，而游戏的特点和分类问题有两大区别。 第一，游戏过程中有非常多的动作和决策。比如我们玩一个打乒乓球的游戏，发球、接球、回 球，每一个动作都是非标的，而且不同的选择会直接影响最终的结果。 第二，赢得一场游戏的方式可能有上万种，并没有唯一的标准答 ...

曲凯： 今天我们请来了国内强化学习 (RL) 领域的专家吴翼，吴翼目前是清华大学交叉信息研究院助理教授，他曾经在 OpenAI 工作过，算是国内最早研究强化学 习的人之一，我们今天就争取一起把 RL 这个话题给大家聊透。 首先吴翼能不能简单解释一下，到底什么是 RL？ 因此，RL 其实更通用一些，它的逻辑和我们在真实生活中解决问题的逻辑非常接近。比如我要去美国出差，只要最后能顺利往返，中间怎么去机场、选什么航 司、具体坐哪个航班都是开放的。 但 RL 很不一样。 RL 最早是用来打游戏的，而游戏的特点和分类问题有两大区别。 第一，游戏过程中有非常多的动作和决策。比如我们玩一个打乒乓球的游戏，发球、接球、回球，每一个动作都是非标的，而且不同的选择会直接影响最终的结 果。 第二，赢得一场游戏的方式可能有上万种，并没有唯一的标准答案。 所以 RL 是一套用于解决多步决策问题的算法框架。它要解决的问题没有标准答案，每一步的具体决策也不受约束，但当完成所有决策后，会有一个反馈机制来评 判它最终做得好还是不好。 吴翼： RL 是机器学习这个大概念下一类比较特殊的问题。 传统机器学习的本质是记住大量标注过正确答案的数据对。 ...

丽娜认为，印尼和中国在清洁能源领域的合作非常重要。"印尼在新能源发展方面潜力巨大，而中 国在新能源技术和产业化方面处于世界领先地位。以镍为例，印尼可以通过与中国合作，推动镍深加工 产业的发展，进而支持全球新能源汽车供应链的发展。而在太阳能发电领域，印尼光照资源丰富，中国 是全球最大的太阳能设备生产国。印尼可以借助中国的技术经验，实现能源的可持续发展。" 今年是中国与印度尼西亚建交75周年。75年来，两国关系在互信互利的基础上不断深化，成为全球 南方国家合作的典范。近年来，双方在基础设施建设、科技合作、绿色发展等领域取得诸多突破，展现 了构建人类命运共同体理念的生动实践。 "我对中国的快速发展深感震撼。"印尼战略与国际研究中心国际关系部主任丽娜·亚历桑德拉在接 受记者采访时表示，她曾多次访问中国，走访过北京、上海、深圳、广州等多个城市，对中国的现代化 进程、基础设施建设和科技进步印象深刻。 "中国已成为世界上发展最快的经济体之一。从日常消费品到高端技术产品，中国制造随处可 见。"丽娜表示，中国不仅是全球制造中心，更是全球供应链的关键枢纽，与世界各国的经济联系日益 紧密。 丽娜对未来印尼与中国的合作充满期待。她认 ...

Core Viewpoint - Nvidia's market position has been challenged globally, but it has unexpectedly benefited from a surge in H20 chip sales in China, with major tech companies placing significant orders [1][3]. Group 1: Sales Surge - In Q1 2025, Chinese tech giants like ByteDance, Alibaba, and Tencent collectively ordered at least $16 billion worth of H20 chips, nearly matching Nvidia's total sales in China for 2024, which was $17 billion [1][4]. - In 2024, Nvidia shipped over 1 million H20 chips to China, generating approximately $12 billion in revenue, while in just three months of 2025, the orders approached the previous year's total [4]. Group 2: Market Dynamics - Initially, Chinese companies were cautious about H20 chips due to performance issues and high prices, leading to a wait-and-see attitude [5]. - By the second half of 2024, market confidence rebounded, evidenced by a surge in orders at the beginning of 2025 [6]. Group 3: Supply and Demand - As of early April 2025, H20 chip inventory was nearly depleted, with new shipments expected only by mid-April, indicating a supply-demand imbalance [7]. Group 4: Drivers of Demand - The rapid growth of China's AI industry and the unique positioning of H20 chips have driven demand, particularly for low-cost AI models [8]. - Major companies are investing in AI servers based on H20 chips to support cloud services and the deployment of DeepSeek-R1 models [8]. Group 5: Competitive Landscape - Despite the current success, H20 chips face significant challenges from U.S. export restrictions and domestic regulatory pressures [10][11]. - The potential tightening of U.S. export controls poses a risk to H20's supply chain, while new energy efficiency standards in China could limit sales if H20 chips do not comply [13]. - Local competitors like Huawei are rapidly advancing, with their chips offering similar pricing and localized advantages, which could threaten H20's market position [14].

Core Insights - The rise of AI-native organizations represents a dual revolution in technology and organizational structure, posing significant challenges to traditional internet giants [1][2] - The competition is not only about technological capabilities but also about organizational forms, cultural genes, and talent strategies [2][3] Group 1: Characteristics of AI-native Organizations - AI-native organizations integrate AI as a core driver of products, services, and business processes, rather than as an added feature [2] - They possess self-developed core technologies, with rapid iteration speeds that outpace traditional companies, exemplified by OpenAI's swift transition from GPT-3 to GPT-4 within two years [2] - Product design inherently relies on AI capabilities, making it impossible for products to exist independently of AI [3] - The focus has shifted from "data and computing power" to "algorithms and community," emphasizing algorithm breakthroughs and scenario innovations as keys to market recognition [4] - Organizational structures are fluid, with flat, self-organizing teams that enable rapid decision-making and resource responsiveness [5] - A geek culture and strong founder cohesion drive these organizations, emphasizing technical idealism and long-term value [6] Group 2: Challenges for Traditional Internet Giants - Traditional tech giants face a core issue: how to evolve their organizations to maintain competitiveness in the AI-native wave [2][9] - Despite having significantly more resources, traditional companies struggle to replicate the technical sharpness of AI-native organizations like DeepSeek [1][9] - The lack of visionary leadership and a clear pursuit of algorithmic efficiency hampers traditional firms' ability to compete effectively [9] - The user engagement battle is intensifying, with AI-native applications rapidly gaining traction and threatening traditional applications' user time [10] Group 3: Strategic Responses from Major Companies - Major companies are attempting to integrate AI-native capabilities into their core businesses, recognizing the potential for scalable applications [11][21] - ByteDance is restructuring its AI organization to enhance agility and innovation, with a focus on AI-native talent [19][20] - Tencent is migrating its AI product lines to a more integrated structure, emphasizing collaboration with AI-native models [21] - Alibaba plans to invest over 380 billion yuan in AI infrastructure and aims for a comprehensive transformation across its core businesses [22] Group 4: Future Directions and Organizational Evolution - The evolution of organizational forms will be crucial as companies transition from traditional data-algorithm-traffic models to a model-data-agent framework [27] - Companies must focus on enhancing their organizational learning speed to convert technological breakthroughs into business cycles effectively [27] - The historical challenges of organizational inertia must be addressed to facilitate meaningful transformation in response to AI-native competition [25][26]

原标题：以科技创新增强抵御外部冲击的底气 我们已与美国打了8年贸易战，而这8年正是中国经济发展"含新量"不断上升的8年。无论是所谓"小 院高墙"，还是"脱钩断链"，都没能阻挡中国经济发展和科技进步。中国科技创新实力正在从量的积累 迈向质的飞跃，从点的突破迈向系统能力提升，在集成电路、人工智能、人形机器人等领域创新成果密 集井喷，带动了产业发展，提振了市场信心，增强了抵御风浪的能力。 如今，面对美政府变本加厉的霸凌行径，我们更要以破釜沉舟的决心和勇气坚定不移做"困难而正 确"的事，闯出科技创新"华山一条道"。从某种意义上来说，外部的遏制打压是一种"反向激励"，它倒 逼我们走出"舒适区"，摆脱创新惰性，激发内在潜能与活力。从华为的绝地反击到DeepSeek的横空出 世，无不验证了"哪里有封锁，哪里就有突围"。在攻关核心技术的实验室里，在产教融合的课堂上，在 产业升级的车间中，每一份创新的努力，都积蓄起中国勇开顶风船的更大能量。 外部环境越是严峻复杂，我们就越要扎实推动科技创新和产业创新深度融合，加快现代化产业体系 建设，让中国全产业链优势无可替代。我们可以依托持续科技创新不断提升产品附加值，增强出口产品 的国际竞 ...