Group 1 - The article discusses the phenomenon of AI hallucinations, where AI models provide seemingly accurate but fabricated information due to issues with training data quality and completeness [2][3] - Google's official explanation attributes AI hallucinations to two main reasons: the quality of training data and the model's difficulty in accurately understanding real-world knowledge [2] - A study by Vectara in March 2025 found that leading AI models have low hallucination rates, with Gemini-2.0-Flash-001 achieving a 0.7% hallucination rate, indicating high accuracy in document processing [3] Group 2 - The article compares the hallucination rates of AI models to human error rates, noting that top AI models outperform human experts in knowledge-intensive tasks but still lag in open-ended creative tasks [7] - In the medical field, the World Health Organization reported an average misdiagnosis rate of 30%, highlighting that human cognitive biases lead to more significant errors than AI hallucinations [8] - Human cognitive biases, such as confirmation bias and anchoring effects, contribute to a higher incidence of misjudgment compared to AI, as illustrated by historical examples like the Titanic disaster and the Chernobyl accident [9][10]

2025.04. 11 本文字数：1807，阅读时长大约3分钟 在法律层面，早在2023年8月，由网信办等七部门发布的《生成式人工智能服务管理暂行办法》（下 称《办法》）正式施行，《办法》明确要求生成式AI服务提供者需建立数据合规、算法透明、生成 内容管理等六大机制，随着《办法》的实施，中国AI产业的治理与规范化水平日益发展和成熟。 导读 ： 金融领域因其数据密度高、专业性强，暴露出大模型垂直行业数据供给不足的问题。 作者 | 第一财经 齐琦 2025年是AI应用元年，金融行业正经历一场以"垂直化AI"为核心的深度变革。安永最新报告显示， 中国金融科技市场规模已突破4.59万亿美元，预计2030年将达9.97万亿美元，年复合增长率达 13.8%。 当前，包括银行、保险、基金等金融机构已完成多类通用大模型的本地化部署。行业人士对记者称， 大模型与专业知识库的结合是AI落地的未来趋势。 金融AI的知识基建：从通用到专属 具体看来，AI正逐步渗透金融领域，从风险管理到客户服务、从投资决策再到支付安全。 易方达投顾金融科技负责人刘玮对第一财经分析称，DeepSeek的出现令金融机构以更具成本效益的 方式运用AI技术， ...

Core Viewpoint - DeepSeek is emerging as a new consumption decision-making tool for young consumers, providing personalized recommendations that challenge traditional influencer-led shopping methods [3][5][46]. Group 1: DeepSeek's Functionality and Impact - DeepSeek offers personalized recommendations based on user-specific queries, such as skin type or reading preferences, providing detailed reports that include product features and suitability [4][12]. - The platform is seen as a more comprehensive alternative to traditional live-streaming influencers, as it utilizes a deep thinking model to deliver tailored suggestions [5][6]. - As of February 9, DeepSeek's app has surpassed 110 million downloads, with weekly active users reaching nearly 97 million, indicating its growing popularity among young consumers [9]. Group 2: Comparison with Traditional E-commerce Platforms - Traditional e-commerce platforms like Taobao and JD have attempted to integrate AI for personalized shopping but have not prioritized these features in their main app interfaces, limiting user engagement [7][8][22]. - DeepSeek's recommendations are based on a broader range of sources compared to existing e-commerce AI assistants, which often rely on fewer references, leading to less comprehensive suggestions [29][30]. - Despite the advantages of DeepSeek, traditional influencers still hold a significant role in the market due to their established trust and the ability to provide curated selections backed by professional institutions [19][20]. Group 3: Challenges and Limitations - DeepSeek faces challenges such as "AI hallucination," where the AI may produce inaccurate or biased recommendations based on its training data, necessitating human oversight for quality control [17][18]. - The platform's current model requires users to transition to e-commerce sites for purchases, which contrasts with the seamless shopping experience offered by influencers [20][21]. - E-commerce platforms are cautious about integrating DeepSeek due to concerns over data sensitivity and the potential disruption of existing business models [40][41][42]. Group 4: Future Prospects - The shift towards AI-driven recommendations is seen as a significant trend in e-commerce, with DeepSeek positioned to capture the preferences of younger consumers [46]. - There is a need for e-commerce platforms to adapt and potentially collaborate with AI technologies like DeepSeek to enhance their offerings and maintain competitiveness in the evolving market landscape [47][48].

字母榜 . 让未来不止于大 以下文章来源于字母榜 ，作者薛亚萍 从事产品运营工作的陈鹏，今年26岁，他想要通过阅读提高自己的眼界，便向DeepSeek提问"最应 该读的十本书是什么？"DeepSeek同样给他列出了书单，并且分类附上了理由。陈鹏选择了购入其 中几本书。 这曾是李佳琦和董宇辉们在直播间的工作： 导购。 过去几年，头部主播们通过建立和用户之间的信任，构建了以主播为核心的商品分发机制，将他们认 为最好的、最适合的东西推荐给粉丝朋友们。 但现在这套推荐体系，正在被DeepSeek解构。DeepSeek深度思考模式的长思维链优势，能为用户 提供更全面、精准的优质解答，进而形成一对一的个性化推荐，应用到购物领域，俨然已经成为D选 ——DeepSeek优选。 "D选"的本质是"AI导购"，辅助用户高效进行消费决策，"AI导购"这个场景并不陌生。 早在多年前，一些电商平台就试图借助大模型实现"种草+购物"的交易闭环。 譬如淘宝的AI助手"淘宝问问"早已接入通义千问，功能包含个性化推荐，并生成选购建议。京东的言 犀大模型，也接入消费导购场景，"京东京言"也被明确定位于"专属AI购物助手"。抖音APP的AI搜索 ...