蔡磊介绍道，渐冻症"AI科研大脑"已经在高效运转，将科研速度提升了数十倍乃至上百倍。"目前我们 已经完成了近4万篇渐冻症核心文献的系统性梳理，并基于此，对数百个有潜力的候选药物进行排序， 正联合全球的科学家们加速验证。" 日前，渐冻症患者、京东前副总裁蔡磊进行了本年度首次直播，他在直播中用眼控打字，文字随后被语 音播放出来，"身体在渐冻症的侵蚀下不断失去功能。"11月25日，蔡磊用文字回复南都N视频记者称， 他希望通过这次直播向大家传递一种韧性精神，告诉大家他还在坚持战斗，"只要身体状况还有一丝允 许，我都会雷打不动投入科研探索和各方沟通。" 蔡磊直播视频截图。 "很遗憾由于病情持续发展，我现在已经很难亲自参与直播，甚至连简单的连线互动都变得十分艰 难。"蔡磊在他今年的首次直播中表示，他的身体在渐冻症的侵蚀下不断失去功能。 蔡磊用文字回复南都记者称，尽管现在完全无法说话，全身动弹不得，但他始终坚持用眼睛和耳朵工 作。他表示，接下来仍然会将全部精力集中在科研工作。"与渐冻症抗争这6年来，只要我的身体状况还 有一丝允许，我都会雷打不动地投入科研探索和各方沟通，推动各个研究管线的进展。" 采写：南都N视频记者 林诗 ...

2024 年 8 月，Transformer 论文作者之一 Llion Jones 创立的 Sakana AI 推出全球首位「AI 科学家」，可自主提出研究问题、设计实验并撰写论文，引发全 球科研界震动。从自动化实验到自主发现，AI 正从科研助手跃升为「共同研究者」。当 AI 走进实验室，科学的未来将被如何改写？ 2024 年 8 月，由 Transformer 论文作者之一 Llion Jones 创立的 Sakana AI 公司宣布推出全球首位「AI 科学家（AI Scientist）」，通过自主生成研究想法、 设计实验、编写代码、执行实验乃至撰写论文，并借助「AI 审稿人」对结果进行评审与改进，形成了完整闭环的科研生态系统。今年 3 月，该系统产出 的一篇计算机科学论文通过了 ICLR 2025 研讨会的双盲评审；同期，Autoscience 研究所也表示其 AI 系统 Carl 撰写的论文也被 ICLR 的 Tiny Papers 赛道 接收。 从某种程度上看，这些 AI 科学家已经走出实验室，正在一步一步攀向与人类研究人员比肩的高度。 然而，当 AI 将瞄准镜指向科学发现时，对人类而言或是喜忧参半— ...

Core Viewpoint - The article discusses Kosmos, an AI scientist capable of conducting extensive research autonomously, achieving results equivalent to six months of human work in just one day, and demonstrating high reproducibility in scientific findings [2][24]. Group 1: Kosmos Capabilities - Kosmos can work continuously for up to 12 hours, reading 1,500 papers and writing 42,000 lines of code in a single research session [2][6]. - It has successfully made seven genuine discoveries across various fields, including metabolomics and neuroscience, some of which were previously unpublished by humans [4][6]. - The AI has a reproducibility rate of 79% for its research results, indicating a high level of reliability [2]. Group 2: Research Process - Kosmos operates through a structured world model that allows for real-time information sharing between data analysis and literature search modules [20]. - The research process involves a "cyclic iteration + information sharing" model, where Kosmos can run up to 200 iterations to refine its findings [21]. - Each research cycle produces results that are automatically compiled into a report, with all data and sources clearly cited [21]. Group 3: Research Findings - Kosmos has replicated an unpublished finding regarding the metabolic mechanisms of brain protection at low temperatures, achieving a correlation of R²=0.998 with human research [13][15]. - It has also discovered new patterns, such as the environmental factors affecting perovskite solar cell efficiency and protective proteins in myocardial fibrosis [26]. Group 4: Team Background - The Kosmos project is led by Ludovico Mitchener and Michaela Hinks from Edison Scientific, both of whom have strong academic backgrounds in AI and biological engineering [27][29]. - Edison Scientific is a non-profit organization focused on automating research in biology and other complex scientific fields [30].

AI科学家时代正在到来，哈佛MIT最新推出的ToolUniverse，通过一个统一平台，让AI用自然语言操作600+科学工具，推动科研自动化的全面 升级，迎接科学发现新范式。 科学史的每一次飞跃，往往伴随着工具的革新。随着近期大模型和智能体的飞速发展，这条路径正在通向一种全新的阶段：「AI科学家」。 在AI赋能科研的前沿，我们正见证一个重要的里程碑：从证明AI智能体「能否」解决特定科学问题，转向思考如何让它「高效、可靠、规模化」地参与 整个研究过程。 Nature近期发布的新闻解析， 报道了由哈佛大学Marinka Zitnik和高尚华团队与MIT发布的首款大规模工具开源框架ToolUniverse 新闻链接：https://www.nature.com/articles/d41586-025-03246-7 ToolUniverse开放的在线环境让研究人员能够用自然语言将各类大模型和智能体 连接到不同科学领域常用的工具，为打造AI科学家奠定了基础。 项目主页：https://aiscientist.tools 论文详解：https://arxiv.org/abs/2509.23426 代码开源：https: ...

Core Insights - OpenAI is transitioning from a research lab to a vertically integrated "AI empire" with significant infrastructure investments requiring industry-wide collaboration [2][3][8] - The company's strategy is driven by confidence in future model capabilities, anticipating substantial economic value creation in the next one to two years [3][15] - OpenAI's partnerships with major tech companies like NVIDIA, Oracle, and AMD are part of a broader effort to leverage the entire AI industry chain [3][8] Group 1: Infrastructure Investment - OpenAI's CEO Sam Altman announced a "very aggressive infrastructure bet" that necessitates support from the entire industry [8][15] - This investment is based on the expectation of future model capabilities rather than current models, indicating a proactive approach to meet anticipated demand [15][68] - Altman hinted at more collaborations to be announced in the coming months, emphasizing the scale of this initiative [8][15] Group 2: Energy and AI - Altman linked the future of AI directly to energy availability, stating that AI's exponential growth will depend on cheaper and more abundant energy sources [6][9] - He predicts that the future energy landscape will be dominated by a combination of solar energy with storage and advanced nuclear energy [9][16] - The cost of nuclear energy will be a critical factor in its adoption and ability to support AI development [9][16] Group 3: Strategic Positioning of Sora - The recently released video generation model Sora is seen as a strategic tool for building "world models" to advance AGI and help society adapt to AI developments [10][17] - Sora also presents new commercialization challenges, as users engage with it for both professional and entertainment purposes [17] Group 4: Emergence of AI Scientists - Altman expressed excitement about the potential for AI models to make significant scientific discoveries within the next two years, marking a transformative moment for the world [12][20] - The capabilities of GPT-5 are already showing promise in making small, novel scientific discoveries [12][20] Group 5: Shift to Vertical Integration - Altman acknowledged a change in perspective regarding vertical integration, now viewing it as essential for OpenAI to achieve its mission [13][22] - He compared this shift to the success of Apple's iPhone, highlighting the need for OpenAI to control its entire stack from foundational computing to application [22][36]

OpenAI正在从一家研究实验室向一个垂直整合的"AI帝国"转型。 10月8日，OpenAI首席执行官Sam Altman在与知名风投公司a16z联合创始人Ben Horowitz的一场最新对话中透露，OpenAI已决定进行"非常激进的 基础设施押注"，其规模之大需要整个行业参与。 他解释说，这一决策基于对未来一到两年内模型能力的强大信心，因为他们预见到即将到来的模型将创造巨大的经济价值，而当前的扩张速度已 无法满足未来的需求。 这一战略直接解释了OpenAI近期与英伟达、甲骨文、AMD等科技巨头达成的一系列合作。Altman预告，未来数月将有更多此类合作公布，显示 其正试图撬动"从电子到模型分发"的整个产业链。 这或也意味着AI竞赛正从算法转向一场关乎算力、资本和能源的全方位斗争。 Altman同时将AI的未来与能源的未来直接挂钩，指出AI的指数级增长将需要更廉价、更丰富的能源。他预测，长期的解决方案将是太阳能加储能 与先进核能的结合，并断言核能的成本将是决定其能否快速普及、进而支撑AI发展的关键变量。 Altman谈到公司愿景时表示，OpenAI不仅仅是研究实验室，更是集消费者AI订阅服务、超大规模基础设 ...

Core Insights - The emergence of AI scientists represents a significant advancement in scientific research, enabling faster hypothesis generation and experimental design, as demonstrated by the recent validation of a new bacterial gene transmission mechanism by Google's AI in just 48 hours [1][2] Group 1: AI Scientist Development - AI scientists are not physical robots but intelligent agents powered by large language models, capable of generating scientific hypotheses and research plans autonomously [1] - The global competition among research institutions to develop AI scientist systems is intensifying, with two main categories: AI as research assistants and fully autonomous scientific discovery systems [2][3] Group 2: Research Assistant Systems - The first category focuses on creating AI systems that assist human scientists, providing interdisciplinary knowledge and research ideas, exemplified by Stanford University's "Virtual Laboratory" which successfully designed 92 antiviral nanobodies [2] Group 3: Autonomous Discovery Systems - The second category aims to develop fully autonomous systems capable of scientific discovery, with examples including Japan's "Fish AI" which produced a computer science paper and the "Future Home" AI system that discovered a drug for dry macular degeneration [3] Group 4: China's AI Scientist Initiatives - China is accelerating the development of AI scientist systems, with initiatives like the "Virtual Scientist" system and the "Feng Deng Gene Scientist" system, which has identified previously unreported gene functions in staple crops [4] Group 5: Future Prospects - The future may see more physical AI scientists assisting in complex research environments, such as "AI crop geneticists" and "AI soil scientists," transforming previously fictional scenarios into reality [5]

Core Viewpoint - The article discusses the emergence of SciMaster, an AI scientific assistant developed by Shanghai Jiao Tong University, DeepMind Technology, and Shanghai Algorithm Innovation Institute, which is claimed to be the world's first truly general-purpose scientific AI agent [5][10]. Group 1: Introduction to SciMaster - SciMaster has gained significant attention in the research community, with its invitation codes being sold for nearly a thousand yuan, indicating high demand [5]. - It integrates advanced capabilities such as literature search, theoretical calculations, experimental design, paper writing, and collaboration, significantly enhancing research efficiency [7][11]. Group 2: Macro Trends in AI - The AI field is transitioning from data and computing power reliance to practical applications, as noted by mathematician Terence Tao [9]. - The concept of an "AI scientist" is at the forefront of this trend, with SciMaster filling a gap in the availability of practical AI research assistants [10]. Group 3: Functional Capabilities of SciMaster - SciMaster covers the entire research process, including reading, calculating, conducting experiments, and writing reports [11]. - It utilizes a vast database of 170 million research documents to provide reliable information and can trace every assertion back to its source [11][14]. - The system can perform calculations and execute experiments through integration with automated laboratory systems [14][15]. Group 4: Performance and Testing - SciMaster has demonstrated its capabilities by achieving a new state-of-the-art score of 32.1% on the Humanity's Last Exam benchmark, surpassing competitors like OpenAI and Google [28]. - The assistant can handle general queries and conduct deep research, providing comprehensive reports based on extensive data collection and analysis [30][31]. Group 5: Future Prospects - The development of SciMaster represents a significant step towards a new era of collaborative scientific exploration between humans and AI [16][49]. - The company aims to expand SciMaster's capabilities to cover a broader range of scientific knowledge, indicating a commitment to advancing AI in research [50].