7月3日，一则《演员王一博案，判了》的新闻发酵，文章内容提及，人工智能公司DeepSeek因内容审核疏漏，就关联不实信息向演员王一博道歉，还援引 了刑事判决书。随后，#DeepSeek向王一博道歉#一度冲上热搜。 目前这一新闻已被删除，但仍然引发大量转载。 媒体作为信息"把关人"的严谨性与责任感，在AI时代更为重要。 一条关于辟谣和道歉的新闻，最终却是AI幻觉带来的谣言，这是AI时代的荒诞现实。 上述文章提到"人工智能公司DeepSeek发布正式道歉"，并提及"声明"一词。但第一财经查阅了目前DeepSeek所有官方渠道的账号，包括官网、公众号、知 乎、小红书、海外的X等，都未发现有新的动态。 仅从新闻事实来看，网络流传演员王一博的消息，已被经纪公司澄清，法院判定系谣言，而声称DeepSeek为传谣道歉，则有诸多的不合理之处。此次假新 闻的信息中没有任何一句明确指出DeepSeek的道歉渠道，声明中所提及的法律判决书，在中国裁判文书网上检索显示无数据。 追溯"道歉"新闻源头，或许来自于一则社交媒体中的帖子，但看图片内容可知，道歉的主体是"AI聊天助手DeepSeek"。 | 昨天 博君一肖iPhone客户端 ...

Core Viewpoint - The article discusses the emergence of "intelligent agents" as a significant commercial anchor and the next generation of human-computer interaction, highlighting the shift from "I say AI responds" to "I say AI does" [1] Group 1: Current State and Industry Perspectives - The concept of intelligent agents is currently the hottest topic in the market, with various definitions leading to confusion [3] - A survey indicates that 67.4% of respondents consider the safety and compliance issues of intelligent agents "very important," with an average score of 4.48 out of 5 [9] - The majority of respondents believe that the industry has not adequately addressed safety compliance, with 48.8% stating that there is some awareness but insufficient investment [9] Group 2: Key Challenges and Concerns - The complexity and novelty of risks associated with intelligent agents are seen as the biggest challenges in governance, with 62.8% of respondents agreeing [11] - The most concerning safety compliance issues identified are AI hallucinations and erroneous decisions (72%) and data leaks (72%) [14] - The industry is particularly worried about user data leaks (81.4%) and unauthorized operations leading to business losses (53.49%) [16] Group 3: Collaboration and Security Risks - The interaction of multiple intelligent agents raises new security risks, necessitating specialized security mechanisms [22] - The industry is working on security solutions for intelligent agent collaboration, such as the ASL (Agent Security Link) technology [22] Group 4: Data Responsibility and Transparency - The responsibility for data handling in intelligent agents is often placed on developers, with platforms maintaining a neutral stance [35] - There is a lack of clarity regarding data flow and responsibility, leading to potential blind spots in user data protection [34] - Many developers are unaware of their legal responsibilities regarding user data, which complicates compliance efforts [36]

Core Insights - The year 2025 is referred to as the "Year of Intelligent Agents," marking a paradigm shift in AI development from "I say AI responds" to "I say AI acts" [1] - The report titled "Intelligent Agent Health Check Report - Safety Panorama Scan" aims to assess whether safety and compliance are ready amidst the rapid development of intelligent agents [1] - The core capabilities of intelligent agents, namely autonomy and actionability, are identified as potential risk areas [1] Dimension of Fault Tolerance and Autonomy - The report establishes a model based on two dimensions: fault tolerance and autonomy, which are considered core competitive indicators for the future development of intelligent agents [2] - Fault tolerance is crucial in high-stakes fields like healthcare, where errors can have severe consequences, while low-stakes fields like creative writing allow for more flexibility [2] - Autonomy measures the ability of intelligent agents to make decisions and execute actions without human intervention, with higher autonomy leading to increased efficiency but also greater risks [2] Industry Perspectives on Safety and Compliance - A survey revealed that 67.4% of respondents consider safety and compliance issues "very important," with an average score of 4.48 out of 5 [4] - There is no consensus on whether the industry is adequately addressing safety and compliance, with 48.8% believing there is some attention but insufficient investment [4] - The top three urgent issues identified are stability and quality of task execution (67.4%), exploration of application scenarios (60.5%), and enhancement of foundational model capabilities (51.2%) [5] Concerns Over AI Risks - The most common safety and compliance concerns include AI hallucinations and erroneous decisions (72%) and data leaks (72%) [6] - The industry is particularly worried about user data leaks (81.4%) and unauthorized operations leading to business losses (53.49%) [6] Responsibility and Data Management - The responsibility for data management in intelligent agents is often unclear, with user agreements typically placing the burden on developers [14][15] - Many developers lack awareness of their legal responsibilities regarding user data, which complicates compliance efforts [15] - The report highlights the need for clearer frameworks and standards to ensure responsible data handling and compliance within the intelligent agent ecosystem [15]

Core Viewpoint - The article discusses the importance of iterative processes in Electronic Design Automation (EDA) and highlights the challenges posed by decision-making in logic synthesis, emphasizing the need for integrated tools to manage multi-factor dependencies and improve timing convergence [1]. Group 1: EDA Process and Challenges - Iterative loops have been crucial in the EDA process for decades, especially as gate and line delays have become significant [1]. - The consequences of decisions in the EDA process can be far-reaching, affecting multiple other decisions, which complicates achieving acceptable timing [1]. - Serial tool operation can lead to major issues, and achieving timing convergence in logic synthesis is nearly impossible without a concept of iterative learning [1]. Group 2: Integration of Tools - The integration of decision tools, estimators, and checkers into a single tool addresses the issue of multi-factor dependencies, allowing for quick checks during decision-making [1]. - There is a growing need for such integrated functionalities across various fields, enabling users to guide tool operations based on their expertise [1]. Group 3: AI and Verification in EDA - AI hallucinations are recognized as a characteristic rather than a defect, with models generating plausible but not necessarily factual content [3]. - The use of retrieval-augmented generation (RAG) aims to control these hallucinations by fact-checking generated content, similar to practices in EDA [3]. - The industry has a strong emphasis on verification, which is crucial for ensuring the reliability of AI applications in EDA [5]. Group 4: Future Directions and Innovations - The industry is making progress in identifying necessary abstractions for validating ideas efficiently, with examples like digital twins and reduced-order models [6]. - A model generator capable of producing required abstract concepts for verification is deemed essential for mixed-signal systems [6]. - With proper verification, AI could lead to breakthroughs in performance and power efficiency, suggesting a need for a restructuring phase in the industry [6].

新华财经北京7月2日电 当银行客户经理写一份企业授信尽调报告从耗时7天压缩至3分钟，当政策问答 平均响应时间缩短至20秒，银行与大模型的化学反应正悄然颠覆传统金融作业模式。近日，新华财经独 家对话中国光大银行副行长杨兵兵，深入探讨大模型在银行核心场景的深度实践，用好大模型的关键资 源以及与技术红利如影随形的AI幻觉应对之策等话题。 场景深耕：3分钟生成授信尽调报告，20秒实现精准问答 走进银行的业务一线，大模型技术已不再是遥不可及的概念，而是真切地扎根于多个核心场景，并结出 效率之果。 "大模型不是实验室玩具，而是解决业务痛点的工具。"杨兵兵告诉记者，该行已经推动大模型技术在客 户经理赋能、合规运营、远程坐席、助力分行智能化经营等场景的落地。 在银行客户经理撰写授信尽调报告这一场景中，效率提升尤为显著。 在传统流程下，银行客户经理撰写授信尽调报告需要经历与客户接洽、资料收集、现场尽调、风险评 估、授信方案设计并撰写报告，再提交审批。对于一些中大型企业来说，撰写一份百页授信尽调报告平 均需要7天左右，如今借助大模型技术，短短3分钟即可完成一份报告。 "这极大地节省了客户经理的精力，让他们能更专注于客户关系的深度 ...

Core Viewpoint - The year 2025 is anticipated to be the "Year of Intelligent Agents," marking a paradigm shift in AI development from "I say AI responds" to "I say AI acts," with intelligent agents becoming a crucial commercial anchor and the next generation of human-computer interaction [1] Group 1: Importance of Safety and Compliance - 67.4% of industry respondents consider the safety and compliance issues of intelligent agents to be "very important," but it does not rank in the top three priorities [2][7] - The majority of respondents (70%) express concerns about AI hallucinations, erroneous decisions, and data leakage [3] - 58% of users do not fully understand the permissions and data access capabilities of intelligent agents [4] Group 2: Current State of Safety and Compliance - 60% of respondents deny that their companies have experienced any significant safety compliance incidents related to intelligent agents, while 40% are unwilling to disclose such information [5][19] - The survey indicates that while safety is deemed important, the immediate focus is on enhancing task stability and quality (67.4%), exploring application scenarios (60.5%), and improving foundational model capabilities (51.2%) [11] Group 3: Industry Perspectives on Safety - There is no consensus on whether the industry is adequately addressing safety and compliance, with 48.8% believing there is some attention but insufficient investment, and 34.9% feeling there is a lack of effective focus [9] - The majority of respondents (62.8%) believe that the complexity and novelty of intelligent agent risks pose the greatest challenge to governance [16][19] - 51% of respondents report that their companies lack a clear safety officer for intelligent agents, and only 3% have a dedicated compliance team [23] Group 4: Concerns and Consequences of Safety Incidents - The most significant concerns regarding potential safety incidents include user data leakage (81.4%) and unauthorized operations leading to business losses (53.49%) [15][16] - Different industry roles have varying concerns, with users and service providers primarily worried about data leakage, while developers are more concerned about regulatory investigations [16]

Group 1 - The phenomenon of AI hallucination occurs when AI models generate inaccurate or fabricated information, leading to misleading outputs [1][2] - A survey indicates that 42.2% of users report that the most significant issue with AI applications is the inaccuracy or presence of false information [2] - The rapid growth of generative AI users in China, reaching 249 million, raises concerns about the risks associated with AI hallucinations [2] Group 2 - AI hallucinations stem from the probabilistic nature of large models, which generate content based on learned patterns rather than storing factual information [2][3] - There is a perspective that AI hallucinations can be viewed as a form of divergent thinking and creativity, suggesting a need for a balanced view of their potential benefits and drawbacks [3] - Efforts are being made to mitigate the negative impacts of AI hallucinations, including regulatory actions and improvements in model training to enhance content accuracy [3][4]

Core Viewpoint - The article discusses how a method involving "cat" has been used to improve the accuracy of AI-generated references, particularly in the context of scientific research, highlighting the ongoing challenges of AI hallucinations in generating fictitious literature [1][25][26]. Group 1 - A post on Xiaohongshu claims that using "cat" as a safety threat has successfully corrected AI's tendency to fabricate references [1][5]. - The AI model Gemini reportedly found real literature while ensuring the safety of the "cat" [2][20]. - The post resonated with many researchers, garnering over 4000 likes and 700 comments [5]. Group 2 - Testing the method on DeepSeek revealed that without the "cat" prompt, the AI produced incorrect references, including links to non-existent articles [8][12][14]. - Even when the "cat" prompt was applied, the results were mixed, with some genuine references but still many unverifiable titles [22][24]. - The phenomenon of AI fabricating literature is described as a "hallucination," where the AI generates plausible-sounding but false information [25][26]. Group 3 - The article emphasizes that the core issue of AI generating false references stems from its statistical learning from vast datasets, rather than true understanding of language [27][28]. - Current industry practices to mitigate hallucinations include Retrieval-Augmented Generation (RAG), which enhances model outputs by integrating accurate content [31]. - The integration of AI with search functionalities is becoming standard across major platforms, improving the quality of collected data [32][34].

Core Insights - ChatGPT has demonstrated its potential in outdoor navigation by successfully guiding a group lost in a forest using GPS coordinates, showcasing its ability to provide clear directional information and terrain details [2][3][5] Group 1: AI Navigation Capabilities - A recent study published in Translational Vision Science & Technology indicates that AI can assist in navigation by interpreting outdoor scene images, suggesting that models like ChatGPT can effectively respond to directional queries based on visual inputs [7][9] - Research has shown that large language models can optimize path planning in outdoor navigation by utilizing semantic terrain cost grids and classic pathfinding algorithms, improving efficiency by 66% to 87% [18] Group 2: Limitations and Risks - Despite the promising results, current AI technology relies heavily on extensive training data and pre-existing map databases, which limits its effectiveness in uncharted or data-scarce areas [16] - The phenomenon of "AI hallucination" poses a significant risk, as misjudgments in complex real-world environments could lead to severe consequences [17][19]

Group 1 - Musk, co-founder of OpenAI, is developing an AI assistant named Grok through his company xAI, which is currently involved in a $300 million equity transaction, valuing xAI at $113 billion [1] - Musk expressed frustration on the X platform regarding the presence of "garbage" data in uncorrected foundational models, indicating plans to rewrite the human knowledge corpus using Grok 3.5 or Grok 4 to enhance data accuracy [1][2] - The industry is currently employing various methods, such as RAG frameworks and external knowledge integration, to mitigate AI hallucinations, while Musk's approach aims to create a reliable knowledge base [2][35] Group 2 - A recent evaluation of AI models, including Grok, revealed that some models still exhibit hallucinations, with Grok performing well in tests by providing accurate answers [3][11][21] - The tests highlighted the importance of enabling deep thinking modes and networked searches to improve the accuracy of AI-generated content, as models like Doubao and Tongyi showed improved performance when these features were activated [7][21][37] - The evaluation also indicated that while AI hallucinations persist, they are becoming less frequent, and Grok consistently provided correct answers across multiple tests [33][38] Group 3 - Critics, including Gary Marcus, argue that Musk's plan to rewrite the human knowledge corpus may introduce bias, potentially compromising the objectivity of the AI model [38] - The ongoing development of AI models suggests that integrating new mechanisms for content verification may be more effective in reducing hallucinations than rewriting the knowledge base [38] - Research indicates that retaining some level of AI hallucination can be beneficial in fields like abstract creation and scientific research, as demonstrated by the recent Nobel Prize-winning work utilizing AI's "error folding" [38]