01 问：自ChatGPT发布以来，生成式AI技术发展迅速。这些技术对社会、经济和科学研究的主要影响是 什么？ Joanna Bryson： 对于科学研究，人工智能在某种程度上加速了科研进程，但它本质上只是一个工具。 换句话说，它和我们做其他工作的工具没有太大区别，不必过于强调它的特殊性。 在社会层面，经济和政治是两大重要方面。从经济学角度来看，引入自动化可能带来两种效应：一种是 替代效应，即减少对劳动力的需求；另一种是增强效应，即通过提高生产力来创造更多就业。我认为关 于这个主题最好的论文来自詹姆斯·贝森 （ Jam es B essen）。Oxford的研究显示，英国目前并未显现出 明显的替代效应，反而在高生产力领域看到了更多的就业机会。然而，考虑到创建大型语言模型的高成 本，其经济效益是否能覆盖这些投入仍然不明确。 在政治方面，经济衰退及其引发的不安全感可能导致政治极化，尤其是在社交媒体和人工智能的作用 下，针对性信息传播成为可能。经济安全感的丧失，尤其是意外的经济下滑 （如失业、负债） ，常常 会导致个体的身份认同危机，进而引发群体认同需求，甚至有可能转向极端行为。在经济不稳定时期， 这种易受操控的心 ...

被访谈人：Joanna Bryson 柏林赫尔蒂学院伦理与技术教授 整理： 曹建峰 腾讯研究院高级研究员 本文根据柏林赫尔蒂学院伦理与技术教授 乔安娜·布赖森 （ Joanna Bryson） 在腾讯研究院 AI&Society 海外专家面对面系列对话中的分享整理而成，分享主题 为" AI只是可控工具 "。 本文为 腾讯研究院 AI&society 海外名家对话 系列第三篇 问：自ChatGPT发布以来，生成式AI技术发展迅速。这些技术对社会、经济和科学研究的主要影响是什么？ Joanna Bryson： 对于科学研究，人工智能在某种程度上加速了科研进程，但它本质上只是一个工具。换句话说，它和我们做其他工作的工具没有太大区 别，不必过于强调它的特殊性。 在社会层面，经济和政治是两大重要方面。从经济学角度来看，引入自动化可能带来两种效应：一种是替代效应，即减少对劳动力的需求；另一种是增强效 应，即通过提高生产力来创造更多就业。我认为关于这个主题最好的论文来自詹姆斯·贝森 （ Jam es B essen） 。Oxford的研究显示，英国目前并未显现出明显 的替代效应，反而在高生产力领域看到了更多的就业机会。然而 ...

henry 发自 凹非寺 量子位 | 公众号 QbitAI 压缩即智能，又有新进展！ | Method | Trained on: | Neural | Acc. | Dataset split | | --- | --- | --- | --- | --- | | Random guessing | Nothing | × | 0% | All | | Brute force rule search (Kamradt, 2024) | Nothing | × | 40% | Private Eval | | U-Net baseline | Target puzzle | V | 0.75% | Public Eval | | CompressARC (ours) | Target puzzle | V | 20% | Public Eval | | HRM ablation (ARC Prize Team, 2025) | Test puzzles | V | 31% | Public Eval | | HRM (Wang et al., 2025) | Train+test puzzles | V | ...

而83岁的谢诺夫斯基，依然在实验室里追问那个问题。 也许没有人比他更适合回答今天AI缺失的那些碎片。他见证了神经网络从"异端"到"改变世界"的全过 程；他既懂物理学的简洁优雅，也懂生物学的复杂混沌；他和辛顿一起打开了AI的大门，又眼看着这 扇门后的世界变得越来越陌生。 1984年的一天，物理学家特伦斯·谢诺夫斯基和心理学家杰弗里·辛顿坐在实验室里，盯着黑板上的方程 发呆。那是AI的第二个寒冬，神经网络陷入僵局。人们都知道多层网络更强大，但没人知道怎么训练 它。 "如果我们把神经网络想象成一团气体呢？"谢诺夫斯基突然说。 这个疯狂的想法最终变成了玻尔兹曼机，这是一个用统计物理学重新定义"学习"的数学模型。它证明了 只要找到合适的能量函数，神经网络就能像气体从高温降到低温一样，自发地调整到最优状态。 这成为现代深度学习的理论基石之一。 但两人后续的志趣却互相有所偏离。辛顿发现了更实用的反向传播算法，带领深度学习走出寒冬，最终 迎来ChatGPT主导的AI时代。而谢诺夫斯基选择了回到神经科学实验室，用几十年时间解剖大脑的每一 个回路，试图回答那个最初的问题：大脑究竟是如何工作的？ 40年后，辛顿因玻尔兹曼机获得20 ...

Core Viewpoint - The article warns that while the U.S. is heavily investing in AI, it may win the AI race but lose broader economic dominance, as the approach is overly focused on AI at the expense of diversifying investments in other critical technologies [1][2]. Investment Trends - U.S. tech companies have invested over $350 billion in AI-related infrastructure in the past year, with projections to exceed $400 billion by 2026, significantly outpacing China's investment of nearly $100 billion [2]. - The article highlights that while the U.S. is betting heavily on AI, China is taking a more diversified and pragmatic approach, investing in various sectors such as electric vehicles, batteries, and renewable energy [3][7]. Strategic Differences - The U.S. tech industry is characterized by a high concentration of investment in AI, which may lead to collective blind spots and increased risks due to the monopolistic structure [3][8]. - In contrast, China's strategy involves a broader investment in multiple future technologies, with significant capital expenditures projected to reach $940 billion in clean energy by 2024, overshadowing AI investments [7]. Cultural and Economic Factors - The article suggests that Silicon Valley's obsession with AI may stem from cultural factors, where there is a tendency to over-invest in new ideas, and from an economic perspective, spending on projects is preferred over stock buybacks [8][9]. - There is a concern that the substantial investment in AI by U.S. tech giants may serve to reinforce their monopolistic positions rather than genuinely advance human welfare [9].

"真正迷恋AI的是美国。"作者写道，并指出，美国的投资动机既商业化，又带着某种神秘色彩，尤其体 现在对通用人工智能（AGI）和"奇点"（singularity）的追求上；人们强烈相信技术会持续呈指数级进 步，却忽视了这种情况在技术史上极为罕见。且越是深入探究，就越会发现，无论在AI的支持者中， 还是在末日论者中，相关观点都显得越来越脱离现实。 与此同时，美国科技行业高度集中，其近乎垄断的结构进一步放大了风险：当巨额资金掌握在极少数公 司手中时，集体盲从的可能性也随之上升。 【文/观察者网 王恺雯】"美国会赢得AI，却输掉战争吗？"英国《金融时报》12月13日以此为题刊登文 章，给美国当前的人工智能（AI）热潮泼下一盆冷水。 文章作者、拜登政府时期担任白宫科技与竞争政策特别助理的吴修铭（Tim Wu），对美国全面押注AI 发出警告，指出与中国就这一未来关键技术展开"末日之战"的想法，既是幻觉，也是硅谷的游说套路。 相较之下，中国虽然也在大力推动AI发展，但态度却克制、务实得多，也更注重多元布局。 文章指出，在过去一年时间里，美国主要科技公司在与AI相关的基础设施上投入了超过3500亿美元， 预计到2026年将超 ...

Core Insights - The event serves as a "high-pressure test" for the commercialization capabilities of humanoid robots, with significant developments expected in 2025, which is viewed as a critical year for the industry [1][4] - The competition features various scenarios that encompass the integration of robots into human life, highlighting the need for practical applications rather than mere demonstrations [4][5] Group 1: Event Overview - The Global Developer Pioneer Summit and International Embodied Intelligence Skills Competition (GDPS 2025) opened in Shanghai, showcasing collaborations between companies like Zhiyuan and Yushu [1] - The event includes six thematic tracks and multiple scenarios, focusing on the practical application of humanoid robots in various fields [4] Group 2: Technological Developments - In 2023, over ten new humanoid robot products were launched, with breakthroughs in key technologies such as edge-side chips and intelligent modules [7] - The first humanoid robot mass production factory in Shanghai, operated by Zhiyuan, aims for an annual production capacity of around 10,000 units, marking a significant milestone in the industry [8] Group 3: Industry Applications - The competition features various applications, including industrial assembly, emergency rescue, and home service tasks, emphasizing the need for robots to perform in complex environments [5][4] - Shanghai's strategic focus on humanoid robots has led to the establishment of a robust ecosystem, with major international industrial robot manufacturers present in the region [5][7] Group 4: Future Goals and Plans - Shanghai aims to achieve breakthroughs in core algorithms and technologies related to embodied intelligence by 2027, with a target of over 20 significant advancements [10] - The city plans to create high-quality incubators and promote innovative application scenarios, with a goal of exceeding 50 billion yuan in the core industry scale [10][11]

Core Insights - The article highlights the differing paths of AI development between China and the United States, emphasizing that China's approach is driven by practical applications rather than the pursuit of "superintelligence" [1][2] - China's AI growth is rooted in its status as a manufacturing powerhouse, focusing on real-world applications that enhance efficiency and reduce costs in various industries [1][2] Group 1: China's AI Development - China's AI development is characterized by an "application-driven" strategy, where specific needs in industries guide the creation of AI models, such as Baidu's Wenxin model tailored for targeted applications [1] - The core motivation for AI in China stems from tangible demands, such as reducing waste in production lines and optimizing supply chains, which provide clear value creation [1][2] - Baidu's initiatives, like the "Famu Intelligent Body," exemplify the focus on finding optimal solutions in real industrial scenarios, showcasing the practical application of AI technology [1] Group 2: Comparison with U.S. AI Development - In contrast, the U.S. AI development emphasizes foundational scientific exploration and the pursuit of general artificial intelligence (AGI), investing heavily in chip architecture and algorithm breakthroughs [2] - The U.S. approach aims for a "one model fits all" strategy, which, while pushing the boundaries of technology, differs significantly from China's pragmatic focus on industry-specific solutions [2] - The article suggests that the winner in the global AI competition will be determined by who can integrate AI as an "inherent capability" within enterprises to solve real problems [2]

Core Viewpoint - OpenAI is facing significant challenges, including a quarterly loss of $12 billion and losing market share to competitors like Anthropic, raising concerns about its sustainability and the narrative of being "too big to fail" [1][34]. Group 1: Financial Performance and Market Position - OpenAI reported a staggering loss of $12 billion in the last quarter, which has raised alarms about its financial health and sustainability [34]. - The company has seen its share of the enterprise large language model (LLM) market drop from 50% to 25% within two years, while Anthropic has surged to a 32% market share [18][24]. - OpenAI's revenue projections show a rapid increase from $1 billion in 2023 to an expected $100 billion by 2029, but the feasibility of achieving such growth remains uncertain [30]. Group 2: Competitive Landscape - Google and DeepMind are identified as the most formidable competitors to OpenAI, with Google significantly improving its AI offerings and market position [9][10][14]. - Anthropic has become the leader in the enterprise AI market, with its model Claude rapidly gaining traction among developers, outperforming OpenAI's offerings [18][24]. - The competition is intensifying, with Google expected to further challenge OpenAI's market share by 2026 [17]. Group 3: Strategic Partnerships and Government Contracts - OpenAI has secured a $200 million contract with the U.S. Department of Defense, which is viewed as more valuable than its consumer subscription revenue, highlighting the importance of large contracts in the AI sector [24]. - The company is diversifying its revenue streams by exploring B2B opportunities, as reliance on consumer subscriptions may not be sustainable in the long term [25]. Group 4: Leadership and Governance Issues - Concerns have been raised about CEO Sam Altman's leadership style and transparency, with allegations of dishonesty affecting the board's confidence in his ability to lead [26][31]. - Internal conflicts within OpenAI's leadership have been documented, indicating potential governance issues that could impact the company's future [27][31].

公开资料显示，苏箐曾担任华为车 BU 智能驾驶产品部部长，负责华为自动驾驶系统方案。2022 年 1 月，苏箐正式从华为离职。同年 10 月，苏箐加入地平 线。 苏菁称，关于特斯拉 FSD V12 到底是不是最强的问题，业内争议很大，但这个问题不重要，重要的是 FSD V12 证明了一段式端到端技术的可行性，推动智 驾技术范式从规则驱动转向数据驱动。他认为，一段式端到端在智驾行业的普及将带来两大趋势的行业演进。 一是智驾系统会在未来几年内越来越"类人"，这将使 L2 级辅助驾驶迎来巨大的发展红利期，城区辅助驾驶将逐步普及到 10 万元级别车型。 二是 L2 和 L4 级别的智驾方法论统一，同样的开发范式，不仅能提升 L2 辅助驾驶体验，同时也能以更低的部署成本和几乎无限制部署区域扩张，落 地一个 L4 系统（Robotaxi）。 2024年,以FSD V12成熟为标准, 智能驾驶迎来内在底层技术 范式与外部用户感知体验的一次 重构。其意义,堪比核能从理论 迈入工程。 苏 等 地平线副总裁兼首席架构师 2025 地平线技术生态大会 HORIZON 75 TOGETHER Z IT之家 12 月 12 日消息，据 ...