"阿福的问答结果中没有任何广告推荐、不存在商业排名，也不受任何其他商业因素干扰"，针对外界猜 测回答结果中是否会有商业广告，蚂蚁集团的AI健康应用"蚂蚁阿福"日前做出了这样硬气的声明。 不久前，蚂蚁集团宣布旗下AI健康应用AQ升级为"蚂蚁阿福"，并上线健康陪伴、健康问答、健康服务 三大核心功能，将其定位从工具型应用转向"AI健康朋友"。当然，真正让"阿福"进入大众视野的是蚂蚁 集团的"钞能力"，比如知名主持人何炅代言，《长安二十四计》《唐诡奇谭》等爆款剧集，以及户外大 屏、地铁公交上的刷屏让它在线上线下都混了个脸熟。 当AI工具遇上医疗健康，用户要的无疑是信任感，这也是蚂蚁阿福要强调自己将保持中立的原因。从 某种意义上来说，这一声明恰恰是蚂蚁阿福这一连串营销操作中最高明的一手。从"AQ"到"蚂蚁阿 福"，蚂蚁集团试图将冰冷的AI工具转化为温情的健康管家，从而试图在C端打开局面。 如果头疼脑热等小毛病也要上医院，钱包和精神可能都经不起考验，由此"丁香医生"、"好大夫"等数字 医疗产品也就应运而生，并通过连接医生、医院、药企来为用户提供专业服务。事实上，"丁香医 生"、"好大夫"这类产品只是将问诊场景从线下搬到线 ...

Core Viewpoint - Meta's acquisition of a 49% stake in Scale AI for $14.8 billion aims to strengthen its position in the AI sector amidst competition, following a previous successful acquisition of WhatsApp for $19 billion 11 years ago [1] Group 1: Acquisition and Market Reaction - Meta's investment in Scale AI comes as a strategic move to catch up in the AI race, especially after signs of lagging in its AI business [1] - Following the deal, OpenAI confirmed it would gradually cease collaboration with Scale AI, indicating a significant shift in partnerships within the AI data supply chain [3] - Google, another major client of Scale AI, plans to terminate its $200 million contract for AI training data, opting to work with Scale AI's competitors instead [3][5] Group 2: Implications for Data Supply and Trust - Scale AI's integration into Meta raises concerns among competitors like OpenAI and Google about data confidentiality and the potential for data poisoning attacks, which could compromise AI model performance [10][14] - The transition of Scale AI from a neutral data provider to a Meta subsidiary could erode trust with its former clients, leading to fears of data leaks and competitive disadvantages [10][16] - The AI industry heavily relies on high-quality data for model training, and the acquisition of Scale AI provides Meta with a stable data source, crucial for the evolution of its Llama model [8][10] Group 3: Data Poisoning Risks - Data poisoning attacks pose a significant threat to AI models, where even a small percentage of contaminated data can severely impact model accuracy [13] - The difficulty in detecting such attacks makes them particularly concerning, especially if a data supplier like Scale AI were to provide tainted data [14][16] - The urgency of maintaining efficient AI model training processes means that companies like OpenAI and Google cannot afford delays caused by potential data integrity issues stemming from Scale AI's new ownership [16]