如果未来五年没有"神来一笔"的算法突破，只把今天这条趋势线按原样拉长，会发生什么？ 答案可能是几串足以改写行业版图的数字：10^29 FLOP 的单次训练量、≈1000× 的算力放大、≈2000 亿美元的硬件投入、2%+ 的全球用电占比，最前沿训练的峰值 功率直逼吉瓦级。 这是Google DeepMind委托Epoch AI 完成的百余页量化研究《AI in 2030》给出的答案。 这些数据共同定义了 2030 年的 AI——一条保守基线；也回答了行业关注的诸多问 题——算力如何涨、电力要到什么级别、钱花在哪儿、哪些能力会先兑现。 本周，OpenAI 与 Anthropic 也发布了自己的用户数据报告，回答了另一半问题：沿途该怎么管着走。 锦秋基金也做了相关的编译， 感兴趣的朋友可以点击链接查 看。 别走弯路！Anthropic 官方揭秘：大模型哪里有用，哪里有钱 | Jinqiu Select 别押错赛道： OpenAI 的25 亿条消息揭示 AI 的真实需求 | Jinqiu Select 谁在用、用来做什么、在哪儿增长？——OpenAI 与 Anthropic 的两份"用户地图"对比 把这三篇报告叠在 ...

Core Insights - The article highlights the emergence of AI technologies in the pharmaceutical and medical fields, particularly focusing on the advancements made by Chinese AI company DeepSeek and its large model R1, which has gained recognition in the scientific community [2] - The integration of AI in drug discovery and clinical applications is accelerating, with significant investments from major pharmaceutical companies aiming to revolutionize the drug development process [4][5] Group 1: AI in Drug Discovery - Major pharmaceutical companies, including Bristol-Myers Squibb and Sanofi, are investing billions in AI drug discovery, hoping to achieve breakthroughs that will transform the drug development process [4] - Medidata's data indicates that the proportion of clinical trials initiated by Chinese companies has surged from approximately 3% to 30% by 2024, positioning China as the second-largest clinical trial market globally [4] - AI is expected to drive a new wave of drug development, becoming a crucial force in the transformation of new drug research [4] Group 2: AI in Medical Applications - The "Meta-Medical" laboratory, launched by Zhongshan Hospital affiliated with Fudan University, aims to develop AI agents and apply large model technologies to enhance medical knowledge digitization and productization of diagnostic capabilities [6] - AI is changing the paradigm of diagnosis and treatment, with significant advancements in areas such as heart disease risk prediction and real-time monitoring through wearable devices [6] - The successful application of AI in specific medical fields has reached clinical levels, exemplified by the monitoring of intermittent atrial fibrillation using wearable technology [6] Group 3: Challenges and Ethical Considerations - Despite the potential of AI in drug discovery, challenges remain, including a 90% failure rate in clinical trials and the need to address complex biological issues and regulatory hurdles [5] - Ethical considerations are paramount, with the responsibility for medical decisions still resting with physicians, who must ensure that AI technologies are used safely and effectively in clinical settings [7]

Group 1: AI in Drug Development - AI has become a significant focus for multinational pharmaceutical companies, with substantial investments aimed at transforming the drug discovery process and generating breakthroughs in understanding biological data [3][4] - The global proportion of clinical trials initiated by Chinese companies has increased from approximately 3% to 30% by 2024, positioning China as the second-largest clinical trial market [3] - AI is expected to drive a new wave of drug development, becoming a crucial force in the transformation of new drug research and development [3][4] Group 2: AI Applications in Medical Diagnosis - Major medical institutions in China are actively promoting the integration of large models and AI agents in clinical applications, exemplified by the launch of the "Meta-Medical Simulation Laboratory" by Fudan University and technology companies [5] - AI is changing the paradigm of diagnosis and treatment, with significant advancements in areas such as heart rate screening, imaging analysis, and risk assessment [6] - The application of AI in medicine involves three key aspects: data quality, computational power, and algorithm optimization, which are essential for effective clinical application [6] Group 3: Challenges and Considerations - Despite the potential of AI in drug discovery, there are significant challenges, including a 90% failure rate in clinical trials and the need to address complex biological and regulatory issues [4] - Ethical considerations are paramount, with the understanding that physicians remain the primary decision-makers in clinical settings, and the responsibility for medical actions lies with them [6]

这释放了什么信号？AI到底怎样颠覆传统的科研模式？"科学大模型"和我们熟悉的ChatGPT、DeepSeek 有啥不一样？AI怎么打破数学、物理、化学等"学科壁垒"？未来5到10年，科研形式会发生翻天覆地的 变化吗？ 本期嘉宾来自AI for Science（人工智能赋能科学技术）领域的先行者——北京科学智能研究院，我们邀 请到了研究院院长李鑫宇，他将以生动的语言，带我们踏上一次"科研未来之旅"，深入了解这场正在发 生的"科研范式革命"。 本期嘉宾：北京科学智能研究院院长李鑫宇 近期国务院发布的《关于深入实施人工智能+行动的意见》（以下简称《意见》），提出加快实施六大 重点行动，"人工智能+科学技术"排在首位。 对话AI摘编： 主持人：国务院《关于深入实施"人工智能+"行动的意见》将"人工智能+科学技术"放在六大行动首 位，在您看来这传递了什么信号？ 李鑫宇：首先国家敏锐把握到了它对社会发展的重要意义。从历史角度看，"科学技术是第一生产力"一 直是核心，把它放在首位和整体发展逻辑一致，它是"AI+各行业、各领域"的底层支撑。这是重新梳理 二者关系的重要政策——这次是把科学技术重新拉回视野，找回"以科技突破为底 ...

Core Insights - The discussion emphasizes the potential of achieving Artificial General Intelligence (AGI) within the next decade, which could usher in a new scientific renaissance and significant advancements across various fields such as energy and health [2][7][51] - Current AI systems, while advanced, lack true creativity and the ability to generate new hypotheses, which are essential characteristics of AGI [5][34] Group 1: AGI Development - Demis Hassabis predicts that AGI could be realized around 2030, but current AI systems are not yet at a "PhD-level intelligence" due to their limited capabilities in various domains [4][35] - The construction of AGI requires a comprehensive understanding of the physical world, not just abstract concepts like language or mathematics [6][22] - Hassabis believes that the arrival of AGI will lead to a "scientific golden age," providing immense benefits to humanity [7][51] Group 2: DeepMind's Role - DeepMind is viewed as a central engine within Alphabet, integrating various AI teams to develop models like Gemini, which are now embedded in Google's ecosystem [15] - The team at DeepMind consists of approximately 5,000 members, primarily engineers and researchers, focusing on advancing AI technologies [16] Group 3: Innovations in AI Models - The Genie 3 model represents a breakthrough in creating interactive virtual environments based on textual descriptions, showcasing the ability to generate realistic physical interactions [17][20] - The development of mixed models, which combine learning components with established solutions, is seen as crucial for advancing AGI [45][47] Group 4: Future of Robotics - Hassabis envisions a future where robots can understand and interact with the physical world through language commands, enhancing their utility in everyday tasks [23][25] - The design of humanoid robots is considered beneficial for navigating human environments, while specialized robots will still have their unique applications [26][27] Group 5: AI in Drug Development - DeepMind is working on transforming drug development processes, aiming to reduce the timeline from years to weeks or days, leveraging breakthroughs like AlphaFold [41][43] - Collaborations with pharmaceutical companies are underway to advance research in areas such as cancer and immunology [44] Group 6: Energy Efficiency and AI - The conversation highlights the importance of energy efficiency in AI systems, with advancements in model architecture and hardware optimization potentially mitigating energy demands [49][50] - Hassabis believes that the contributions of AI to energy efficiency and climate change will outweigh its energy consumption in the long run [50] Group 7: Creative Tools and User Experience - The future of creative tools like Nano Banana is characterized by their ability to allow users to interact intuitively, enabling rapid iterations and creative processes [38][39] - These tools are designed to democratize creativity, making advanced capabilities accessible to a broader audience while enhancing the productivity of professional creators [39][40]

>> YOU ARE WATCHING BLOOMBERG TECH EUROPE. COMING UP, A. I.DISCOVERY COULD BE THE NEXT FRONTIER OF MEDICINE TO SPEEDING UP DEVELOPMENT. THE TECHNOLOGY PROMISES PLENTY BUT WILL IT DELIVER. WE SPEAK EXCLUSIVELY TO DENNIS HASSABIS THE NOBLE LAUREATE, WHOSE LONDON-BASED ISOMORPHIC LABS HAS THE AMBITION 'TO SOLVE ALL DISEASE.' THE MAN RESPONSIBLE FOR ALL OF ALPHABET’S CORE A. I. WORK AS HEAD OF GOBBLE DEEPMIND IS SETTING THE BAR HIGH FOR ISOMOPHIC.>> THE KIND OF HOLY GRAIL IS TO TACKLE CANCER. BUT WE’VE DONE IT ...

新药研发往往需要数年时间，而且失败率极高。不过，人工智能(AI)有望改变这一情况。谷歌 (GOOGL.US)旗下AI实验室DeepMind的联合创始人兼首席执行官、诺贝尔化学奖获得者Demis Hassabis 表示，AI很快将把新药研发的时间从数年缩短至数月。Demis Hassabis在接受采访时表示："在未来几年 内，我希望能将这一时间缩短至数月，而不是数年。""我认为这是可能的，甚至可能更快。" Isomorphic Labs目前正致力于寻找癌症和免疫系统疾病的治疗方案。该公司的药物设计主管Rebecca Paul表示，这些疾病为将算法模型成果转化为临床结果提供了相对更为简便的路径。Rebecca Paul还曾 表示，AI研发的药物将使许多癌症转变为可治疗的慢性病。她表示："很难给出一个明确的时间表。但 我们现在已经可以开始思考如何解决这个问题。" 自2021年成立以来，Isomorphic Labs已与制药公司礼来(LLY.US)和诺华制药(NVS.US)达成合作。去年， Isomorphic Labs表示正在与诺华制药合作，基于三种靶点研发疗法——这些靶点即药物设计用于作用的 蛋白质或分子。Dem ...

无锡途深智合人工智能科技有限公司（简称"途深智合"），近日宣布完成千万元级人民币天使+轮融 资。 据悉，途深智合此轮融资由上海天使会联合投资，老股东诚美资本持续跟投。途深智合称，将融资资金 进一步投入AI蛋白质平台的研发，持续加速产品创新等。 途深智合宣布完成千万元级人民币天使+轮融资，由上海天使会联合投资，老股东诚美资本 持续跟投。 近两年连融4轮 途深智合成立于2023年12月，是一家专注于人工智能蛋白质设计领域的生物科技公司。该公司以AI超 智能技术推动科学研发，主要应用于高价值蛋白及相关产品的设计与产业化。 目前，途深智合已建立起聚焦生物医药领域的科学智能体平台，能够实现抗体设计的智能化与自动化， 包括ProteinNova蛋白设计AI科学家和蛋白表达测试两大智能体平台。同时，其基于国产芯片超智能技 术，自主研发多款蛋白设计闭源模型，并对多种垂直开源SOTA模型进行了深度优化。 在产业化应用方面，途深智合已取得多项实质性进展。例如，在兔单克隆抗体亲和力改造方面，其成果 已优于欧洲头部医药公司的商用抗体；在酶改造方面，氧化还原酶活性AI首轮设计即提高380%，核酸 酶的活性提高800%。 创投日报记者注 ...

《狐仙崇拜》《至高无上》《通勤梦魇》《与希罗多德一起旅行》。 [加] 康笑菲 著 姚政志 译 读客文化·海南出版社 2025年3月 "你这个狐狸精。"这句台词一度经常出现在影视剧作品中，无论是男女爱情剧还是家庭伦理剧。在大众 印象里，狐狸似乎一直跟魅惑、狡诈、邪恶脱不开干系。狐狸是天性如此，还是被"污名化"了呢？ 华盛顿大学的康笑菲副教授从祖辈早年的狐仙信仰入手，查阅明清及民国时期的笔记小说中大量狐仙故 事，发现在当时的民间视角中，狐仙既可能带来祸害，也可能带来好运和财富。 对于中国人来说，狐狸的形象亦正亦邪，长期就有"模棱两可"的性质：它们漫游在荒野间，无法被驯化 为家畜，却不是人类饲养的牲畜；在人类聚居处造窝，并展现出如人类般的慧黠。正如清代学者纪昀所 言：人物异类，狐则在人物之间；幽明异路，狐则在幽明之间；仙妖殊途，狐则在仙妖之间。正如"狐 狸精"这个称呼，指一个人兼具迷惑人的美貌和毁灭性的色诱力量。而"狐仙"这个称呼，不只是对良狐 的敬称，也带有取悦恶狐的意味。 本书通过一系列生动的故事揭示了"狐仙崇拜"这一中国传统民间信仰的本质：英俊的"胡郎"以狐精之身 迷倒世家小姐，却因出身低微被拒婚，揭示寒门士 ...

Group 1 - The Chinese government has released a strategic document outlining the implementation of "Artificial Intelligence +" across various sectors, aiming for a transition to an intelligent economy and society by 2035 [1][3] - AI is already impacting various fields, including life sciences, legal, and education, with technologies like AlphaFold and ChatGPT reshaping traditional processes and workflows [1][2] - The document emphasizes the need for a systematic approach to integrate AI into education, healthcare, employment, and social support, while also addressing the potential risks associated with job displacement [3][4] Group 2 - The AI revolution is characterized by silent transformations that rewrite processes and change rules, contrasting with the more visible impacts of previous technological advancements like the steam engine [2] - There is a growing concern about the digital divide, where certain populations may be excluded from the benefits of AI due to lack of skills or access, leading to a new form of disenfranchisement [2] - The document highlights the importance of policy guidance and institutional frameworks to ensure that AI technologies serve humanity and promote equitable access, rather than exacerbating existing inequalities [4]