机器之心报道 编辑：泽南、+0 现在的世界模型，值得批判。 我们知道，大语言模型（LLM）是通过预测对话的下一个单词的形式产生输出的。由此产生的对话、推理甚至创作能力已经接近人类智力水平。 但目前看起来，ChatGPT 等大模型与真正的 AGI 还有肉眼可见的差距。如果我们能够完美地模拟环境中每一个可能的未来，是否就可以创造出强大的 AI 了？回想 一下人类：与 ChatGPT 不同，人类的能力组成有具体技能、深度复杂能力的区分。 模拟推理的案例：一个人（可能是自私的）通过心理模拟多个可能结果来帮助一个哭泣的人。 人类可以执行广泛的复杂任务，所有这些任务都基于相同的人类大脑认知架构。是否存在一个人工智能系统也能完成所有这些任务呢？ 论文：Critiques of World Models 论文链接：https://arxiv.org/abs/2507.05169 研究人员指出了构建、训练世界模型的五个重点方面：1）识别并准备包含目标世界信息的训练数据；2）采用一种通用表征空间来表示潜在世界状态，其含义可 能比直接观察到的数据更为丰富；3）设计能够有效对表征进行推理的架构；4）选择能正确指导模型训练的目标函数； ...

近年来，大模型发展主要由大型科技公司主导，其领先的核心在于规模庞大且高质量的数据资源。然而，这些公司通常并不公开其原始数据及数据处理工具，使 得学术界在大模型训练数据的构建与优化方面难以追赶，受制甚深。 尽管近年来开源了大量数据集，学术界在大模型数据准备方面仍面临诸多挑战。目前，大模型训练数据的清洗与构建仍主要依赖各个研究团队 "闭门造车"，缺乏 系统化、高效的工具支持 。现有的数据处理工具如 Hadoop 和 Spark 等， 支持的操作算子大多偏向传统方法，尚未有效集成基于最新大语言模型（LLMs）的智能 算子，对于构建先进大模型的训练数据支持有限。 为此，张文涛和鄂维南院士团队提出了以数据为中心的 AI 系统 DataFlow 。它系统实现了 100 余个基于规则、本地大模型或大模型 API 的数据治理算子 （Operators），并在此基础上构建 8 条预设数据处理流水线（Pipeline），包括：大规模嘈杂数据（如 PDF 文档、纯文本、低质量问答数据、爬虫数据等）的清 洗、扩增与评估；带有思维链的强推理数据合成；RAG 数据提取与合成等等主流数据治理需求。该系统可供用户灵活组织现有算子，开发新算子 ...

Core Insights - OpenAI signed a $200 million contract with the U.S. Department of Defense to provide AI tools for addressing critical national security challenges [2] - The competition for government contracts in the AI and cloud computing sectors has intensified, with major tech companies vying for lucrative deals [2][3] - The U.S. government is increasingly integrating AI into military operations, with significant investments planned for the coming years [10][12] Government Contracts and Collaborations - OpenAI's contract with the Department of Defense is part of a broader trend where tech companies like Palantir and Snowflake are securing government contracts to enhance their AI capabilities [2][3] - Palantir has seen substantial revenue growth, with 60% of its income derived from government contracts, including a significant contract for Project Maven [2] - Snowflake obtained a $1 billion temporary authorization from the Department of Defense, allowing all military branches to utilize its enhanced data capabilities [3] Major Cloud Providers and AI Integration - The Department of Defense awarded a $9 billion Joint Warfighting Cloud Capability (JWCC) contract to major cloud providers including Amazon, Google, Microsoft, and Oracle [4] - Microsoft has been a key partner for the government, integrating OpenAI's GPT-4 model into various government agencies [4] - Oracle is also involved in providing cloud services to the military, aiming to simplify cloud management and reduce costs [10] Economic Implications of AI - The economic benefits of AI are under scrutiny, with predictions suggesting that generative AI could contribute $7 trillion to global GDP over the next decade [7] - However, some experts argue that the immediate economic impact of AI may be overstated, with many tasks requiring human intervention and expertise [8][9] Shifts in Corporate Policies - Major tech companies are shifting their policies regarding military applications of AI, with OpenAI and Google removing restrictions on the use of their technologies for military purposes [11][12] - This shift indicates a deeper involvement of tech companies in military operations, reflecting the growing importance of AI in national security [12]

Group 1 - The article discusses the philosophical implications of creating Artificial General Intelligence (AGI) that can understand human emotions such as "regret" and "forgiveness," prompting a reflection on human limitations and desires [2][4][8] - Liu Jia, a professor at Tsinghua University, emphasizes that AGI is not merely a tool but a mirror reflecting human aspirations and fears, suggesting that it could amplify human intelligence or threaten cognitive freedom [7][12][14] - The article highlights the unique challenges posed by AGI, particularly in the context of human skills that are difficult for AI to replicate, such as basic physical tasks, which may become more valuable in the future [6][21] Group 2 - Liu Jia's new book explores the intersection of cognitive science and AI, breaking down the technical logic of large models while incorporating perspectives from psychology and philosophy [5][41] - The article mentions that since the advent of GPT-3.5, many AI experts have likened the risks of AGI to nuclear disasters, indicating a serious ethical dilemma that humanity must confront [12][36] - The discussion includes the potential for AGI to evolve into a new species with self-awareness, drawing parallels to human brain evolution and the emergence of intelligence [17][29][68] Group 3 - The article suggests that the current educational paradigm must shift to focus on "relearning how to learn," as knowledge becomes less scarce due to AI's capabilities [41][50] - Liu Jia argues that AI can enhance educational equity by providing access to knowledge and resources that were previously unavailable to underprivileged students [46][49] - The need for a new educational framework that emphasizes creativity and critical thinking over rote memorization is highlighted, as AI can handle knowledge retrieval [42][50] Group 4 - The article discusses the challenges of "follow-up innovation" in China's AI industry, suggesting that true breakthroughs require a shift in investment culture and strategic focus [61][64] - Liu Jia emphasizes the importance of interdisciplinary approaches, particularly the integration of brain science and AI, to foster original innovations and maintain competitive advantages [60][68] - The potential for AI to evolve beyond current models is explored, with a call for new architectures that mimic biological brain functions to achieve more human-like intelligence [67][68]

Core Insights - The article discusses the development of Energy-Based Transformers (EBTs) that can learn to think independently through unsupervised learning, enhancing the model's reasoning capabilities akin to human System 2 thinking [9][10]. Group 1: System 2 Thinking and Model Development - Human thinking is categorized into System 1 (fast thinking) and System 2 (slow thinking), with the latter being crucial for complex tasks [3][4]. - Current large language models excel in System 1 tasks but struggle with System 2 tasks, prompting researchers to explore methods to enhance System 2 reasoning [4][5]. - EBTs are designed to assign energy values to input and candidate predictions, optimizing through gradient descent to simulate a thinking process [9][10]. Group 2: Performance and Scalability - EBTs demonstrate a 35% faster scalability in training compared to mainstream Transformer++ methods across various metrics such as data volume and model depth [11]. - In reasoning tasks, EBTs outperform Transformer++ by 29% in language tasks, indicating superior performance with increased computational effort [12]. - EBTs also excel in image denoising tasks, requiring fewer forward passes than diffusion Transformers while achieving better results [13]. Group 3: Generalization and Robustness - EBTs show enhanced generalization capabilities, particularly when handling out-of-distribution data, outperforming existing models even with similar or worse pre-training performance [14]. - The model's ability to learn and express uncertainty in predictions is highlighted, with EBTs effectively capturing the difficulty of token predictions [62][65]. - EBTs exhibit a linear trend in performance improvement as the distribution shift increases, emphasizing their critical role in cross-distribution generalization tasks [68][69]. Group 4: Experimental Results and Comparisons - EBTs outperform Transformer++ in various scalability metrics, including data efficiency and computational efficiency, suggesting they will excel in large-scale training scenarios [46][72]. - Despite slightly higher pre-training perplexity, EBTs achieve lower perplexity in downstream tasks, indicating stronger generalization capabilities [74]. - In image denoising tasks, EBTs significantly outperform DiT models, achieving better peak signal-to-noise ratios (PSNR) with 99% fewer forward passes [81][92].

Group 1: Cloud Intelligence Technology - Yunzhisheng officially listed on the Hong Kong Stock Exchange on June 30, 2025, with the stock code "9678," becoming the first AGI stock in Hong Kong [2][5] - The company launched its first large language model, UniCore, based on BERT, and later developed the Shanhai model with 60 billion parameters, achieving significant performance in various evaluations [3][4] - Yunzhisheng's revenue from 2022 to 2024 was 601 million, 727 million, and 939 million CNY, with a compound annual growth rate (CAGR) of 25% [4] Group 2: Ophthalmic Biotechnology - Bokan Shiyun officially listed on the Hong Kong Stock Exchange on July 3, 2025, with the stock code "2592" [6] - The company focuses on developing differentiated drugs for major eye diseases using proprietary technology platforms [6] - Bokan Shiyun's core product CBT-001 is undergoing Phase III clinical trials in the US and China, aiming to provide non-invasive treatment for pterygium [6][7] Group 3: GPU Technology - Moore Threads submitted its prospectus for the Sci-Tech Innovation Board on June 30, 2025, focusing on self-developed GPUs for high-performance computing [8][9] - The company has achieved significant breakthroughs in GPU technology, with products nearing international advanced levels [10] - Revenue from 2022 to 2024 was 46 million, 124 million, and 438 million CNY, with a CAGR exceeding 200% [11] Group 4: Healthcare Payment Solutions - Meixin Health submitted its prospectus for the Hong Kong Stock Exchange on June 30, 2025, becoming the largest multi-payment platform in China [14][15] - The company has saved patients approximately 6.7 billion CNY in out-of-pocket expenses by the end of 2024 [14] - Revenue from 2022 to 2024 was 1.069 billion, 1.255 billion, and 2.035 billion CNY, with a gross profit margin of 31.1%, 36.8%, and 35.8% respectively [16] Group 5: Industrial Robotics - Yifei Technology submitted its prospectus for the Hong Kong Stock Exchange on June 30, 2025, focusing on industrial robots for the light industry [19][20] - The company is ranked fifth among domestic suppliers of industrial robots and related solutions in China [20] - As of June 21, 2025, Yifei Technology has over 400 million CNY in hand orders [22] Group 6: AI in Medical Imaging - Deshi Biotechnology submitted its prospectus for the Hong Kong Stock Exchange on June 29, 2025, focusing on AI in medical imaging [42] - The company's iMedImageTM model supports 19 types of medical imaging modalities, covering over 90% of clinical scenarios [43] - Revenue for 2023 and 2024 was 52.84 million and 70.35 million CNY, with gross profit margins of 71.0% and 65.5% respectively [48] Group 7: Antibody-Drug Conjugates - BlissBio Inc. submitted its prospectus for the Hong Kong Stock Exchange on June 29, 2025, focusing on next-generation ADCs for cancer treatment [50][51] - The company has four ADC candidates in clinical stages, with BB-1701 being the leading candidate for treating HER2-positive breast cancer [51][53] Group 8: Integrated Elderly Care Services - Puxiang Health submitted its prospectus for the Hong Kong Stock Exchange on June 30, 2025, focusing on integrated medical and elderly care services [55] - The company is ranked second among integrated elderly care service providers in North China by revenue [56] - Revenue from 2022 to 2024 was 255 million, 422 million, and 500 million CNY [57]

Core Insights - The video modeling sector has recently gained attention with several companies launching new products, including 生数科技's Vidu, MiniMax's Hailuo-02, and 百度's MuseSteamer, targeting professional video content creators [1] - Despite the excitement in AI, the competition in video modeling is expected to be less intense than in large language models due to limitations in training data [1] - The market is seeing a mix of large tech companies and startups like 爱诗科技 and MiniMax, which are accelerating product iterations and commercialization efforts [1] Company Developments - MiniMax's founder highlighted the complexities of video processing, which requires significant infrastructure and patience due to the scarcity of open-source video content [2] - Investment interest in video models is shifting from team quality to technical and commercialization capabilities as the market matures [2] - Some platforms are attempting to position themselves as the "TikTok of video models," but market response has been lukewarm due to high cost pressures and challenges in monetization [2] Commercialization Strategies - Video models are being commercialized through two main models: To C (consumer) and To B (business), with pricing varying significantly [4] - 快手可灵 has reported an annual recurring revenue (ARR) exceeding $100 million, while other companies' revenue data remains opaque [4] - 生数科技 and MiniMax are actively expanding their commercial applications, with MiniMax's Hailuo generating over 370 million videos since its launch [4] Market Outlook - The global AI video generator market is projected to grow from $614.8 million in 2024 to $2.5629 billion by 2032, with a compound annual growth rate (CAGR) of 20.0% [4] - 生数科技's founder anticipates accelerated commercialization of video models this year, with a diverse market landscape expected to emerge [4] - Overcoming the gap between costs and monetization remains a critical challenge for participants in the video modeling sector [4]

人工智能正在引领新一轮技术革命和产业变革，香港在这波人工智能浪潮中没有缺席。今年2月，由香 港特区政府重点创科项目"InnoHK创新香港研发平台"资助的香港生成式人工智能研发中心发布HKGAI V1大模型，揭开了香港人工智能发展的新篇章。 从政策资金和战略性举措安排看，香港特区政府对人工智能和科技创新表现出强有力的支持。根据2025 至2026财政年度财政预算案，香港特区政府计划拨款10亿港元，专门用来建立香港人工智能研发院。除 此之外，还有更多的拨款用于相应领域的研究。2024年12月，香港目前规模最大的人工智能超算中心投 入使用，数码港人工智能实验室也同步启用。人工智能是未来新质生产力最关键的技术，香港特区政府 推出的多项政策措施将有序完善香港人工智能生态圈的发展和推动人工智能的"数智"应用，超算中心将 会成为香港人工智能发展中不可或缺的重要支柱。超算中心将汇聚算力、数据及算法技术的人才，加上 数码港人工智能实验室，数码港将会为人工智能生态伙伴和企业提供一个创新平台，联结相关的应用场 景。 香港具有人才和技术储备方面的优势。香港有世界一流的教育和研究机构，教学质量高、基础研究强、 国际联系广、与内地合作实 ...

Group 1 - The Hong Kong Digital Port launched the "Artificial Intelligence Funding Program" to promote innovation through the use of supercomputing resources [1] - The program is backed by a government allocation of HKD 30 billion, aimed at supporting local institutions and enterprises in AI research and application [1] - The initiative is part of a broader strategy to establish Hong Kong as an international AI and innovation center, enhancing research efficiency and accelerating the application of results [1] Group 2 - The "Artificial Intelligence Funding Program" has received approximately 20 applications since its launch, with around 10 projects approved by the end of June, totaling a funding amount of HKD 300 million [2] - The projects cover various research areas, including local large language models, new materials, synthetic biology, and medical models [2] - The program aims to integrate government, industry, academia, and research forces to drive high-quality development in Hong Kong and beyond [2] Group 3 - The Hong Kong Polytechnic University reported a 28% increase in the accuracy of information generated by their large model, achieved through the use of the Digital Port's supercomputing resources [2] - The team has made breakthroughs in medical applications, reducing the number of tests required during cancer treatment, thus saving time and resources [2] - The collaboration with top cancer treatment hospitals in Hong Kong and mainland China enhances localized data analysis in cancer treatment [2] Group 4 - The Hong Kong University of Science and Technology successfully developed the first large model, HKGAI V1, which improved research efficiency by over 50% and reduced the relative error rate in speech recognition by 20% [3] - The use of supercomputing resources allowed the team to shorten the experimental time from 6 weeks to 3 weeks [3] - The center aims to further optimize models and expand applications to enhance smart governance and daily life in Hong Kong [3]

7月2日，第45届国际预测大会（ISF 2025）在北京圆满闭幕。 国际预测大会是该领域最具权威性的国际学术会议。自1981年创办以来，今年首次在中国大陆举办，吸 引了来自全球35个国家和地区的580位顶尖预测科学学者、行业领袖及政策制定者注册参会，规模创历 史新高，充分展现了预测科学在全球的重要性日益增长及中国在该领域的影响力日益提升。 大会围绕"预测科学的前沿与创新"主题，聚焦人工智能、大数据、经济管理、能源环境、气候变化等关 键领域，设置了13场主旨报告、5场深度工作坊、12个平行论坛共计106个专题分论坛，累计开展348场 学术报告。专家学者们就贝叶斯预测、机器学习、大语言模型、预测不确定性、预测组合等热点议题， 以及预测在宏观经济、金融、供应链、能源、医疗、灾害防控等领域的应用展开了广泛而深入的交流。 据了解，下一届国际预测大会（ISF 2026）将于明年在加拿大举行。 ISF 2025大会报告人。主办方供图 本届大会不仅促进了全球预测科学前沿成果的分享与碰撞，也为深化该领域的国际科研合作与交流搭建 了重要平台，对推动预测科学的发展及其在应对全球挑战中的应用具有重要意义。 国际预测者协会主席Laur ...