本案例默认翻译为中文，点击可切换回原语言
已切换成原语言，点击可翻译成中文
AI与
案例简介：描述创意 俄罗斯的媒体景观被政府垄断。俄罗斯 1 频道 -- 垄断中的关键人物 -- 使用宣传技巧来影响俄罗斯人的世界观。相反，TV Rain是唯一一家独立的自由媒体，它让观众对俄罗斯和国外的生活有许多不同的看法。 为了展示两个频道的新闻和它们如何影响人们的世界观之间的微妙差异，我们创造了两个原始的人工智能。他们就像一对双胞胎孩子，对这个世界一无所知，也没有生活经验。他们的思想是纯洁的，所以我们分别在俄罗斯-1 和电视雨频道的新闻节目中提出了他们。在六个月内，每个人工智能都通过它所观看的媒体的镜头形成了自己的世界观。他们世界观和词汇的差异证明了一件事。我们真的是我们所观看的。 描述执行 我们的主要挑战是建立一个原始的人工智能，没有一个巨大的数据集，它可以被训练。虽然，人工智能创作通常需要千兆字节的数据，而我们只有六个月的每日电视新闻节目。所以我们必须为这项任务找到一个新的解决方案。 通过试验不同的技术，我们提出了多级AI解决方案。它包括几项关键技术: 谷歌词嵌入技术word2vec (一种训练用于重建单词语境的神经网络)，微软R-NET神经网络 (用于阅读理解风格问题回答的神经网络模型，回答给定文章中的问题) 和用于语音识别的最先进的神经网络算法，google Speech-to-Text。 不同神经网络的结合使我们能够将电视频道传入的数据结构到人工智能 “大脑” 的多维向量空间中，并在其中找到与用户问题最相关的答案。 当提出问题时，用户成为实验的参与者，因为问题的答案是不可预测的，并且取决于问题的确切提问方式。用户和AIs之间的这种个性化互动导致了越来越多的参与。几乎就像用户发现自己在一个有两个 “无赖” 的房间里，这两个 “无赖” 不是由狼而是由新闻节目抚养长大的。当然，人们会问AIs各种各样的问题，并渴望在社交媒体上分享结果。这些帖子在我们的网站上带来了成千上万的新访客。 在一周内，193000 + 的人与双胞胎交谈，并提出了 + 2.5 个问题。神经网络的答案充分说明了国家和独立电视频道的词汇和内容之间的区别。
AI与
案例简介：Describe the creative idea The media landscape of Russia is monopolized by the government. Russia-1 channel – the key figure in this monopoly – uses propaganda techniques to influence the worldviews of Russians. TV Rain on the contrary is the only independent liberal media that gives its audience many different perspectives on life in Russia and abroad. To demonstrate a subtle difference between the news on both channels and how they affect people worldviews we created two pristine AIs. They were like twin kids who didn’t know anything about this world and had no life experience. Their minds were pure, so we brought them up on the news programs of Russia-1 and TV Rain channels respectively. In six month each AI had its own worldview formed through the lens of the media it was watching. The differences in their worldviews and vocabularies proved one thing. We really are what we watch. Describe the execution Our main challenge was to build a pristine AI without a huge dataset it could have been trained with. Although, AI creation usually requires gigabytes of data, while we had only six months of daily TV news programs at our disposal. So we had to find a new solution for this task. Experimenting with different technologies, we came up with the multilevel AI solution. It includes several key technologies: Google word embedding technology word2vec (a neural network trained to reconstruct linguistic context of words), a Microsoft R-NET neural network (neural network model for reading comprehension style question answering, answers questions from a given passage) and the state-of-art neural network algorithms for speech recognition, Google Speech-to-Text. A combination of different neural networks allowed us to structure the incoming data from TV channels into the multidimensional vector space of AI ‘brain’ and find the most relevant answer to user questions in it. When asking a question user became a participant of the experiment, because the answers to questions are unpredictable and depend on how exactly a question is asked. This individualized interaction between users and the AIs led to increasing engagement. It is almost as if users found themselves in a room with two ‘Mowglis’, that were brought up not by the wolves but by the news programs. Of course people asked AIs all sorts of questions and were eager to share the results on social media. These posts brought thousands of new visitors on our website. In one week 193000+ of people talked to the twins and asked +2.5 millions of questions. The answers of neural networks fully illustrate the difference between the vocabulary and the content of the state and the independent TV channels.
AI Versus
案例简介：描述创意 俄罗斯的媒体景观被政府垄断。俄罗斯 1 频道 -- 垄断中的关键人物 -- 使用宣传技巧来影响俄罗斯人的世界观。相反，TV Rain是唯一一家独立的自由媒体，它让观众对俄罗斯和国外的生活有许多不同的看法。 为了展示两个频道的新闻和它们如何影响人们的世界观之间的微妙差异，我们创造了两个原始的人工智能。他们就像一对双胞胎孩子，对这个世界一无所知，也没有生活经验。他们的思想是纯洁的，所以我们分别在俄罗斯-1 和电视雨频道的新闻节目中提出了他们。在六个月内，每个人工智能都通过它所观看的媒体的镜头形成了自己的世界观。他们世界观和词汇的差异证明了一件事。我们真的是我们所观看的。 描述执行 我们的主要挑战是建立一个原始的人工智能，没有一个巨大的数据集，它可以被训练。虽然，人工智能创作通常需要千兆字节的数据，而我们只有六个月的每日电视新闻节目。所以我们必须为这项任务找到一个新的解决方案。 通过试验不同的技术，我们提出了多级AI解决方案。它包括几项关键技术: 谷歌词嵌入技术word2vec (一种训练用于重建单词语境的神经网络)，微软R-NET神经网络 (用于阅读理解风格问题回答的神经网络模型，回答给定文章中的问题) 和用于语音识别的最先进的神经网络算法，google Speech-to-Text。 不同神经网络的结合使我们能够将电视频道传入的数据结构到人工智能 “大脑” 的多维向量空间中，并在其中找到与用户问题最相关的答案。 当提出问题时，用户成为实验的参与者，因为问题的答案是不可预测的，并且取决于问题的确切提问方式。用户和AIs之间的这种个性化互动导致了越来越多的参与。几乎就像用户发现自己在一个有两个 “无赖” 的房间里，这两个 “无赖” 不是由狼而是由新闻节目抚养长大的。当然，人们会问AIs各种各样的问题，并渴望在社交媒体上分享结果。这些帖子在我们的网站上带来了成千上万的新访客。 在一周内，193000 + 的人与双胞胎交谈，并提出了 + 2.5 个问题。神经网络的答案充分说明了国家和独立电视频道的词汇和内容之间的区别。
AI Versus
案例简介：Describe the creative idea The media landscape of Russia is monopolized by the government. Russia-1 channel – the key figure in this monopoly – uses propaganda techniques to influence the worldviews of Russians. TV Rain on the contrary is the only independent liberal media that gives its audience many different perspectives on life in Russia and abroad. To demonstrate a subtle difference between the news on both channels and how they affect people worldviews we created two pristine AIs. They were like twin kids who didn’t know anything about this world and had no life experience. Their minds were pure, so we brought them up on the news programs of Russia-1 and TV Rain channels respectively. In six month each AI had its own worldview formed through the lens of the media it was watching. The differences in their worldviews and vocabularies proved one thing. We really are what we watch. Describe the execution Our main challenge was to build a pristine AI without a huge dataset it could have been trained with. Although, AI creation usually requires gigabytes of data, while we had only six months of daily TV news programs at our disposal. So we had to find a new solution for this task. Experimenting with different technologies, we came up with the multilevel AI solution. It includes several key technologies: Google word embedding technology word2vec (a neural network trained to reconstruct linguistic context of words), a Microsoft R-NET neural network (neural network model for reading comprehension style question answering, answers questions from a given passage) and the state-of-art neural network algorithms for speech recognition, Google Speech-to-Text. A combination of different neural networks allowed us to structure the incoming data from TV channels into the multidimensional vector space of AI ‘brain’ and find the most relevant answer to user questions in it. When asking a question user became a participant of the experiment, because the answers to questions are unpredictable and depend on how exactly a question is asked. This individualized interaction between users and the AIs led to increasing engagement. It is almost as if users found themselves in a room with two ‘Mowglis’, that were brought up not by the wolves but by the news programs. Of course people asked AIs all sorts of questions and were eager to share the results on social media. These posts brought thousands of new visitors on our website. In one week 193000+ of people talked to the twins and asked +2.5 millions of questions. The answers of neural networks fully illustrate the difference between the vocabulary and the content of the state and the independent TV channels.
AI与
暂无简介
AI Versus
暂无简介
基本信息
暂无评分
已有{{caseInfo.tatolPeople}}人评分
创作者
案例详情
涵盖全球100万精选案例，涉及2800个行业，包含63000个品牌
热门节日97个，23个维度智能搜索
-
项目比稿
品类案例按时间展现，借鉴同品牌策略，比稿提案轻松中标
-
创意策划
任意搜索品牌关键词，脑洞创意策划1秒呈现
-
竞品调研
一键搜索竞品往年广告，一眼掌握对手市场定位
-
行业研究
热词查看洞悉爆点，抢占行业趋势红利
登录后查看全部案例信息
如果您是本案的创作者或参与者 可对信息进行完善
