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WAIC 2025大黑马,一个「谢耳朵AI」如何用分子式超越Grok-4
机器之心· 2025-07-29 10:31
Core Insights - The article highlights the launch of the Intern-S1 multimodal model by Shanghai AI Laboratory, which is positioned as a leading open-source model in the field of scientific research, showcasing significant advancements in AI for science [5][12][17]. Group 1: Model Capabilities - Intern-S1 is recognized for its superior performance in scientific reasoning tasks, outperforming leading closed-source models like Grok-4, particularly in fields such as chemistry, materials science, and biology [12][17]. - The model integrates a 235 billion parameter MoE language model and a 6 billion vision encoder, trained on 5 trillion tokens, with over 2.5 trillion tokens specifically from scientific domains [25][21]. - Intern-S1 demonstrates a 70% improvement in compression rates for chemical formulas compared to previous models, indicating enhanced efficiency in processing complex scientific data [26]. Group 2: Technological Innovations - The model employs a dynamic tokenizer and temporal signal encoder to effectively handle various complex scientific modalities, addressing challenges posed by data heterogeneity and semantic understanding [26]. - Intern-S1's training costs for reinforcement learning have been reduced by tenfold due to collaborative breakthroughs in system and algorithm optimization [30]. - The model's architecture allows for a unique "cross-modal scientific analysis engine," enabling it to interpret complex scientific data such as chemical structures and seismic signals accurately [16][17]. Group 3: Open Source and Community Engagement - Since its initial release in 2023, the "ShuSheng" model family has been continuously upgraded and expanded, fostering an active open-source community with participation from hundreds of thousands of developers [32][33]. - The Shanghai AI Laboratory has launched a comprehensive open-source toolchain that includes frameworks for data processing, pre-training, fine-tuning, deployment, and evaluation, aimed at lowering barriers for research and application [32]. - The Intern-Discovery platform, based on Intern-S1, has been introduced to enhance collaboration among researchers, tools, and research subjects, promoting a new phase of scientific discovery [6][33].
低渗透+高增长,品牌扎堆入局美妆最后一条黄金赛道
Ge Long Hui· 2025-07-26 18:18
Core Insights - The beauty industry is experiencing a resurgence in the fragrance sector, with major brands and local companies expanding their offerings [2][3] - Interparfums has signed a fragrance licensing agreement with Longchamp, with the first fragrance expected to launch in 2027 [2] - The market is witnessing a trend of cross-industry brands entering the fragrance space, indicating a shift in consumer preferences towards emotional and everyday use of fragrances [8][10] Industry Developments - Interparfums is set to fully manage the Longchamp fragrance line, which will include the creation, development, production, and sales of the brand's perfumes [2] - Coty has launched a new mass-market fragrance brand, Origen, targeting the U.S. market with a focus on storytelling through scents [5] - TSG Consumer has acquired the independent fragrance brand Phlur, which emphasizes emotional resonance and affordability [7] Market Trends - The global fragrance market is projected to grow steadily, with estimates suggesting it will exceed $79.3 billion by 2027, driven by the demand for self-care and emotional healing [8][9] - The fragrance market is expanding at a compound annual growth rate of over 3%, with the Chinese market showing significant growth potential despite low penetration rates [9] - Fragrances are increasingly seen as everyday emotional consumption items rather than luxury goods, with younger consumers seeking emotional connections through scent [9][10] Financial Performance - Puig's latest half-year report indicates that its fragrance and fashion division generated €1.685 billion in revenue, accounting for over 70% of total revenue, with an 8.6% year-on-year growth [8] - The fragrance industry boasts a gross margin of approximately 70%, with low raw material costs and high product turnover rates contributing to its profitability [9]
从医学到农业,上海AI实验室发布十项“AI+科学”成果
第一财经· 2025-07-26 12:09
2025.07. 26 本文字数:2630,阅读时长大约5分钟 作者 | 第一财经 刘晓洁 成果2:多智能体虚拟疾病学家系统"元生"(OriGene) 联合临港实验室、上海交通大学、复旦大学、MIT等研究机构,发布国际首个专注于靶标发现与临床 转化价值评估的多智能体虚拟疾病学家系统——"元生"(OriGene)。OriGene可自动发现并验证创 新治疗靶点,实现从数据到机制、从假说到验证的全流程智能化,推动AI驱动靶点及药物发现"新范 式"。目前,OriGene已在肝癌和结直肠癌治疗上分别提出新靶点GPR160和ARG2,被真实临床样本 和动物实验验证,形成科学闭环。 成果3:全球首个单细胞DNA甲基化基础模型scDNAm-GPT 2025年,大模型从实验室走向落地,正在各行各业"开花结果"。 7月26日,在WAIC 2025科学前沿全体会议上,上海人工智能实验室(上海AI实验室)联合多家科研 机构及企业一次性发布了十项突破性科学智能联合创新成果,包括量子计算、生命科学、材料科学、 地球科学、深空天文等多个领域。 这些重量级成果包括全球首个基于人工智能的量子计算中性原子排布算法,能在60毫秒完成2024个 量 ...
AI生物学家诞生!我国学者开发元生智能体,自主发现抗癌新靶点并设计验证实验,能力超越人类专家和主流大模型
生物世界· 2025-06-11 09:22
Core Viewpoint - The discovery and identification of therapeutic targets remain a critical bottleneck in drug development, with over 90% of candidate drugs failing in clinical development due to flawed initial hypotheses regarding biological function, disease relevance, or druggability [2][3]. Group 1: Target Discovery Challenges - Traditional target discovery relies on disease biologists integrating various independent biomedical data to form testable hypotheses, which is a slow and costly process, often exceeding $2 million per target [2][3]. - The failure rate in clinical development is largely attributed to issues with the selected targets rather than the compounds themselves [2]. Group 2: Introduction of OriGene - A new multi-agent virtual disease biologist system named "OriGene" has been developed, focusing on target discovery and clinical translation value assessment, outperforming human experts and leading AI models in target discovery capabilities [2][3][9]. - OriGene autonomously discovered new targets for liver cancer and colorectal cancer, demonstrating its ability to generate original targets validated through experiments [3][27]. Group 3: System Features and Functionality - OriGene integrates over 500 expert tools and organized biomedical databases, supporting multi-modal reasoning across genomics, transcriptomics, proteomics, phenomics, and pharmacology [11][12]. - The system features a multi-agent collaborative decision-making architecture, including a Coordinator Agent, Planning Agent, Reasoning Agent, Critic Agent, and Reporting Agent, enabling a closed-loop autonomous scientific decision-making process [12][13]. Group 4: Performance Evaluation - A specialized benchmark test set for target discovery, TRQA, was created, covering 1,921 multi-dimensional validation questions, demonstrating OriGene's superior performance in accuracy, recall, and robustness compared to human experts and other AI models [18][21]. - The system's self-evolving capabilities allow it to improve its reasoning ability over time through iterative learning and feedback from experiments [14][16]. Group 5: Practical Validation - In liver cancer, OriGene identified G protein-coupled receptor GPR160 as a key target, showing significant expression in cancer tissues and potential as a new immune checkpoint [23]. - For colorectal cancer, the system selected arginase ARG2 as a target, confirming its high expression in cancer tissues and demonstrating effective tumor suppression in patient-derived organoid models [25][27]. Group 6: Implications for Drug Development - The research signifies a major advancement in using AI to accelerate therapeutic target discovery, providing a scalable and adaptable platform for identifying mechanism-based treatment targets [27]. - As generative AI models and biomedical data resources mature, frameworks like OriGene are expected to facilitate AI-driven end-to-end drug discovery, enhancing the potential for precision medicine [27].