我可能，刚刚成为了哈基米的儿子。 至少，AI是这么认为的。 事情是这样的。 前两天，我在小红书上闲逛，无意间用他们的AI搜索功能，搜了一下影视飓风的李四维。 然后，就发现了一个神奇的AI回答。 李四维，是Tim的父亲。 嗯。。。 如果看过影视飓风的朋友都知道，左边这个就是李四维。。。 在李四维踹了一脚无影墙的那一刻，他勉强能算的上，是Tim的爹。。。 我打开了AI搜索参考的那三篇笔记。 发现问题好像出在最后一篇。 这篇的图片有"李四维是影视飓风创始人潘天鸿(Tim)的父亲"的AI总结，AI很可能就是从这里获取的错误信息。 真的，这玩意其实就是那种所谓的无意识投毒，就是，有人，在互联网上，写了一些内容，然后AI就信了，然后AI就开始到处跟别人说，李四维是Tim的 父亲。 再然后，就是以谣传谣，先污染了百度，然后又被用户分发以后，又污染了小红书。 当时觉得这个玩意很有意思，所以，我就想，自己也试一下玩玩。 其实吧，这种所谓的投毒或者GEO，有些时候，在一些冷门的话题下，想污染起来是很轻松的。 比如给我自己，也安排一个父亲，安排一个哈基米。 于是，我随手注册了一个小号。 随手发了一条笔记，内容写的就是"卡兹克是哈基 ...

Core Viewpoint - The article discusses the phenomenon of information pollution through AI, highlighting how misinformation can spread rapidly and be accepted as truth by AI systems, leading to potential harm to individuals and brands [27][45]. Group 1: Information Pollution Mechanism - AI can inadvertently spread false information based on erroneous data it encounters online, as demonstrated by the example of misidentifying a character's parentage [6][8]. - The author conducted experiments to illustrate how easily misinformation can be injected into AI systems, showing that even a newly created account can influence AI responses with the right prompts [12][15]. - The concept of Generative Engine Optimization (GEO) is introduced, where individuals can manipulate AI to promote specific narratives or discredit others, effectively turning misinformation into a business model [27][29]. Group 2: Impact on Individuals and Brands - The article highlights the risks posed to individuals, such as job candidates, who may be unfairly judged based on fabricated negative information generated by AI [30][31]. - It emphasizes the ease with which negative information can overshadow positive attributes, leading to reputational damage for brands and individuals alike [39][40]. - The author notes that the current landscape allows for the rapid dissemination of negative narratives, which can be more impactful than positive ones due to human nature's tendency to focus on negative information [41][42]. Group 3: Recommendations for Mitigation - The article suggests that individuals should not take AI responses at face value and should seek additional sources of information to verify claims [53]. - It encourages the preservation of original information sources to maintain a sense of perspective and awareness of biases in AI-generated content [54]. - The author advocates for contributing truthful content to counter misinformation, even if it seems insignificant, to help create a more balanced information environment [55][56].

Core Viewpoint - The rapid development of AI has led to the emergence of "AI poisoning," where malicious data is fed into AI systems, resulting in the generation of false or harmful information [3][4][5] Group 1: AI Poisoning Overview - "AI poisoning" refers to the introduction of false or harmful information into AI training data, which can lead to significant consequences in various fields such as healthcare, finance, and autonomous driving [4][5] - There are two main methods of "AI poisoning": injecting harmful data into training datasets and altering model files to change training outcomes [3][4] Group 2: Consequences of AI Poisoning - In the medical field, poisoned AI could lead to misdiagnosis of conditions; in finance, altered algorithms could create trading risks; and in autonomous driving, malicious data could cause vehicles to fail at critical moments [4] Group 3: Prevention Measures - The industry is implementing multi-dimensional technical measures to create a "digital firewall" against "AI poisoning," including safety alignment at the algorithm level and external protective barriers [5] - Current strategies include fact-checking AI outputs through cross-validation and data tracing, as well as requiring platforms to label AI-generated content to alert users [5][6] Group 4: User Guidelines - Users are advised to utilize AI tools from reputable platforms, use AI outputs as references rather than absolute truths, and protect personal information to avoid contributing to the spread of harmful data [6][7]