Core Insights - The article emphasizes the transition from merely adopting AI to effectively utilizing it, predicting that by 2026, 5 billion people will use AI daily, highlighting its evolution into a core productivity driver [1][12]. Group 1: AI Adoption Challenges - Companies face significant challenges in AI implementation, including doubts about AI's value, with 37% of enterprises expressing skepticism despite projected spending growth [2]. - Key bottlenecks hindering AI's transition from experimentation to large-scale application include uncontrollable model outputs, unreliable data sources, and inadequate security mechanisms [2][3]. Group 2: Trustworthy AI Framework - Trustworthy AI is defined as the ability to meet stakeholder expectations in a verifiable manner, with a formula proposed: Trustworthy Productivity = Trustworthy Models + Trustworthy Data [4]. - Trustworthy models require not only technical capabilities but also the ability to systematically solve complex problems through trustworthy task planning [4][5]. Group 3: Importance of Trustworthy Data - Trustworthy data is crucial for achieving trustworthy AI, with its reliability ensured through identifying credible data sources and efficiently extracting necessary information [6][7]. - The authority of data sources and the reliability of data acquisition methods are often overlooked factors that significantly impact decision quality [8]. Group 4: Data Source System - The company has established a multi-tiered, high-standard trustworthy data source system, including access to over 1,000 authoritative institutions for macroeconomic and industry data [9]. - It also integrates professional third-party data and enterprise-specific data to provide a comprehensive view of business operations [10]. Group 5: Security and Collaboration Mechanisms - The company prioritizes architecture design over functional promises, ensuring that AI systems can be deployed in a controlled environment to maintain data security [11]. - In critical business decision scenarios, human experts retain final decision-making authority, with AI serving as an efficient execution assistant [11]. Group 6: Practical Applications and Future Outlook - Successful applications of AI have been demonstrated, such as a marketing agency increasing creative material effectiveness from 30% to 70% through predictive testing [12]. - As AI technology continues to permeate industries, the competition will shift from merely having AI to possessing superior AI capabilities, making trustworthy AI a critical component of digital transformation [12].

21世纪经济报道记者 方海平 上海报道 当下，人人都对快速迭代的各类技术的巨大能量有了初体验，也深信其终将渗透并重塑经济和社会生活 的各个领域，在金融行业亦不例外。作为"金融五篇大文章"之一，金融机构尤其是银行对数字金融的重 视和投入似乎怎么强调都不为过。 如今银行业的数字化进程到了哪一步？大模型等新兴技术强势来袭，银行业有怎样的思考和顾虑？新的 数字化地基更新建成，下一步在技术应用上会有怎样的突破？对此，21世纪经济报道《对话数字金融30 人》高端访谈栏目近期专访了上海银行（601229）副行长、首席信息官胡德斌。 胡德斌拥有十分深厚的银行业数字化经历，拥有吉林大学软件工程博士学位。其职业生涯深度贯穿中国 银行业信息化与数字化历程，曾历任中国工商银行软件开发中心副总经理、数据中心（上海）副总经理 等关键职务。自2016年出任上海银行副行长，并于2021年兼任首席信息官以来，他主导推动了该行一系 列重大科技战略工程。 近期，上海银行历时27个月的"智芯工程"圆满收官，新一代全栈信创核心系统成功投产。该工程不仅实 现了从底层硬件到应用软件的全面自主可控，更依托腾讯云TDSQL数据库与TCE专有云平台，完成了 核 ...

2025年的最后两个月，混沌Black创新企业联盟的五家企业接连迎来 两场IPO 。 在对赌压顶、前路迷茫的时刻，两位创始人先后走进了混沌Black创新企业联盟。一年半后，他们各自带着一家上市公司走了出来。 一个月后，51WORLD的钟声在港交所响起，成为"Physical AI第一股"，技术闭环+生态协同叩响万亿新赛道，市值超150亿港元。创始人李熠（混沌学园5期学 员）的"克隆地球"梦想，第一次在全球资本市场的目光中，变得如此真切。 按照"克隆地球计划"，51WORLD通过16年（2015-2030）克隆地球5.1亿平方公里，以此不断解决真实世界中关于交通拥堵、AI训练、时空沉浸、安全预警、气候 预测、能源工业等应用领域的各种现实问题。到目前，公司已经集齐了地球克隆最基本的要素——"人与建筑"、"车与道路"、"水与城市"，在19个国家拥有超过 1000家客户。 资本市场在2025年格外冷静，这两声钟响因此显得尤为珍贵。而只有真正了解内情的人才知道，这两家公司有一个更重要的共同点： 2024年初，他们都遇到巨大的危机。 先是11月，明略科技登陆港交所，成为全球Agentic AI（自主智能体）第一股。创始人 ...

近日，冀北电科院发布"智序"电力智能体测评体系，为人工智能在电力行业的有序落地筑牢根基，推动 新型电力系统与智能电网建设迈向新高度。 随着国家"人工智能+"行动持续推进，新型电力系统加快建设，智能体作为人工智能技术的重要应用形 态，正逐步从实验探索走向电网核心业务场景。电力行业的AI应用具有高安全性、高可靠性特点，智 能体一旦参与运行和生产，其行为是否可控、决策是否稳定、结果是否可信，成为必须正面回答的现实 问题。 围绕智能体应用落地前的这一重要关口，冀北电科院立足电力行业实际，打造"智序"电力智能体测评体 系，面向智能体全生命周期构建系统化、工程化的测评方法，致力于以"可度量、可解释、可复现"的专 业评估手段，为电力智能体实现"可用、好用、放心用"提供支撑。 从行业视角看，"智序"电力智能体测评体系为智能体应用提供了一套可复用、可推广的测评范式，有助 于统一能力认知、降低应用风险、提升人工智能应用的可控性和规范性，为后续开展规模化应用和行业 协同奠定基础。 面向未来，冀北电科院将持续深化"智序"测评体系建设，推动其在更多电力业务场景中的实践应用，并 加强与国家人工智能测评与标准体系的协同衔接，不断提升电力智 ...

这些在真实世界中无处不在的视觉退化，足以让最先进的GPT-4V、Qwen-VL等模型产生荒谬输出，成为其在自动驾驶、医疗影像、安防监控等关键领域 落地的「阿喀琉斯之踵」。 现有方法的根本困境在于「隐式适应」：通过对抗训练、数据增强等手段，试图让模型「硬扛」干扰。 这如同给模型戴上更厚的滤镜——治标不治本，且不可解释。模型在特定退化上表现提升，却无法理解退化本身，更无法泛化到未知干扰，其决策过程仍 是黑箱。 【导读】多模态大语言模型（MLLMs）已成为AI视觉理解的核心引擎，但其在真实世界视觉退化（模糊、噪声、遮挡等）下的性能崩溃，始终是制约产 业落地的致命瓶颈。近日，一篇被AAAI 2026接收为Oral的重磅论文Robust-R1，给出了革命性解法：来自香港科技大学、西北工业大学等团队首次跳出 「隐式适应」的思维定式，将视觉退化问题重构为显式结构化推理任务，让模型不仅「抗干扰」，更能「诊干扰」，在多项权威评测中实现质量与鲁棒性 的双重突破。 当多模态大模型（MLLMs）从实验室走向真实世界，它们遇到了一个致命瓶颈：视觉退化。 雨滴斑驳的车窗、年代久远的监控录像、网络压缩的低质图片、医疗影像的固有噪声…… 今 ...

以下文章来源于快鲤鱼 ，作者杨婧雪 快鲤鱼 . 创业邦旗下AGI矩阵号，寻找海内外创新性的AGI高成长公司，记录AGI商业领袖的成长轨迹。 作者丨杨婧雪 编辑丨刘恒涛 | | | 北京彩智科技有限公司-融资历程 | | | --- | --- | --- | --- | | 融资轮次 | 事件时间 | 融资金额 | 投资方 | | 天使轮 | 2024年11月 | 数千万人民币 | 智谱Al领投 | | | | | 盛景嘉成 | | A轮 | 2025年12月 | 数千万人民币 | 致远互联独家领投 | | | | | 数据来源： 睿兽分析 | 随着 AI 的不断发展，大模型在一些垂直的严肃工作场景落地越来越普遍。比如政务服务大模型、企 业客服大模型，这些严肃的场景，需要大模型对制度、章程的严谨、准确输出，对幻觉"零容忍"。但 目前市面上常见的大模型都是概率模型，尤其遇上章程知识， AI 幻觉更加严重。 但对政企办公来说，规章制度是基本守则。 AI 如果不能解决对规章制度的幻觉，就很难真正进入严 肃办公场景。 彩智科技 正在推进的深知可信知识模型， 针对的就是这一市场痛点。 瞄准规章领域痛点 打造零幻觉 AI ...

人工智能正以前所未有的速度重塑我们的世界：它让个体担忧职业的未来，让产业面临转型的阵痛，也 让全球格局步入新一轮科技竞争与协作的十字路口。当乐观成为迎接未来的必要选择，我们能否正视技 术回报"缓慢起飞"的发展周期，以人类命运共同体的愿景迈向AI技术跃迁的新纪元？本期《两说》邀请 美国《连线》杂志创始主编、未来学家凯文·凯利（Kevin Kelly）进行了一场深度对话，聆听其前瞻思 考。作为一位以洞察技术演化规律著称的科技预言家，凯文·凯利在节目中勾勒了一幅关乎趋势、周期 与文明的AI未来全景图。 乐观主义与聆听技术 塑造未来的理性工具 面对普遍的社会焦虑，凯文·凯利重新校准了"乐观"的含义。在他看来，乐观并非天真的期待或盲目的 信心，而是一种基于历史经验的、蓄意选择的理性工具。他指出，人类所有复杂的文明造物，从城市到 技术，最初都源于乐观者脑海中的蓝图。正是因为他们先"相信"其可能，才最终得以实现。 将此应用于AI，他认为讨论的起点应是客观证据。目前，数据显示AI直接导致的大规模失业并未发 生；长远来看，技术演进会如同计算机催生出"网页设计师"一样，创造大量前所未有的新职业。因此， 他的乐观主义哲学最终导向一 ...

人工智能正以前所未有的速度重塑我们的世界：它让个体担忧职业的未来，让产业面临转型的 阵痛，也让全球格局步入新一轮科技竞争与协作的十字路口。当乐观成为迎接未来的必要选择，我 们能否正视技术回报"缓慢起飞"的发展周期，以人类命运共同体的愿景迈向AI技术跃迁的新纪元？ 本期《两说》邀请美国《连线》杂志创始主编、未来学家凯文·凯利（Kevin Kelly）进行了一场深度 对话，聆听其前瞻思考。作为一位以洞察技术演化规律著称的科技预言家，凯文·凯利在节目中勾勒 了一幅关乎趋势、周期与文明的AI未来全景图。 乐观主义与聆听技术 塑造未来的理性工具 面对普遍的社会焦虑，凯文·凯利重新校准了"乐观"的含义。在他看来，乐观并非天真的期待或盲目的 信心，而是一种基于历史经验的、蓄意选择的理性工具。他指出，人类所有复杂的文明造物，从城市到技 术，最初都源于乐观者脑海中的蓝图。正是因为他们先"相信"其可能，才最终得以实现。 将此应用于AI，他认为讨论的起点应是客观证据。目前，数据显示AI直接导致的大规模失业并未发 生；长远来看，技术演进会如同计算机催生出"网页设计师"一样，创造大量前所未有的新职业。因此，他 的乐观主义哲学最终导向一 ...

Core Insights - The global infrastructure industry is at a transformative crossroads, with projected construction spending reaching $10 trillion by 2025, yet productivity has seen little improvement over decades. AI is viewed as a key opportunity to bridge the supply-demand gap in infrastructure [1][4] - AI is increasingly integrated into various stages of infrastructure projects, enhancing efficiency and decision-making, but its adoption faces significant challenges due to the industry's complexity and high stakes [1][8] Group 1: AI Integration and Impact - Approximately half of the respondents in a global survey have piloted or implemented AI in infrastructure, with one-third predicting AI will be applied to over half of their design and engineering projects within three years [4] - AI has demonstrated substantial efficiency improvements, with examples including a Chinese engineering company achieving over 60% operational efficiency in substations and a Turkish project reducing development time from five years to one year while cutting costs by over 75% [4][9] - Bentley's AI strategy emphasizes "trustworthy AI," focusing on specialized intelligence rooted in infrastructure scenarios, utilizing real project data and geographic information [7][8] Group 2: Challenges in AI Adoption - Data silos present a significant challenge, as infrastructure projects involve multiple phases and data formats, necessitating a unified data foundation to facilitate seamless data flow [8][9] - The rigorous engineering logic must be embedded in AI to ensure compliance with safety and construction standards, as any deviation could lead to unsafe outcomes [8][9] - The complexity of adapting AI to various geographical and climatic conditions poses a third challenge, requiring tailored solutions for different project environments [9][10] Group 3: Future Directions - Bentley's "Infrastructure AI Co-Creation Program" aims to involve users in the design of AI workflows, enhancing software optimization through user feedback [10] - The vision for AI in the infrastructure sector is not to replace engineers but to empower them, fostering a collaborative human-machine process [11]

Core Viewpoint - The article highlights the journey of Wu Minghui, the founder of Minglue Technology, emphasizing his technical background, entrepreneurial challenges, and the evolution of his company towards AI-driven solutions, particularly focusing on trust and data credibility in business decision-making. Group 1: Entrepreneurial Journey - Wu Minghui is portrayed as a typical "scholar-type" entrepreneur with a strong technical background, having excelled in mathematics and computer science [1][7] - The company experienced significant ups and downs, including a dramatic downturn where it struggled to pay severance to employees, leading to negative public perception [1][39][46] - After nearly two decades of exploration in the business world, Wu has focused on the core question of what constitutes trustworthy data [3][24] Group 2: Product Development and Innovation - Minglue Technology recently launched the multi-modal foundational model web GUI intelligent agent, Mano, which achieved state-of-the-art performance in international benchmarks [1][2] - The proprietary large model product line, DeepMiner, aims to address the challenge of making AI agents trustworthy, explainable, and traceable in enterprise decision-making [2][68] - DeepMiner is designed to connect credible data sources, enabling businesses to make informed decisions based on reliable data analysis [68][69] Group 3: Strategic Insights and Reflections - Wu reflects on the importance of trust in data and the need for AI to act as a gatekeeper in business decisions [4][66] - The article discusses the strategic errors made during the company's rapid expansion, emphasizing the need for a controlled strategic pace [50][51] - Wu acknowledges the lessons learned from past failures, particularly the necessity of aligning team goals and maintaining trust within the organization [54][57]