Artificial intelligence technologies are improving the marketing toolkit, making it possible to process large amounts of data faster and more efficiently than ever before. Machine learning, a subset of AI, uses algorithms that can predict which ads will be most effective in specific situations, allowing for optimized ad targeting. This research explores the issues of coevolution and distribution of machine and human intelligence in various social practices, including marketing and advertising. The authors describe the key approaches to studying the visual component of advertising and suggest revising traditional methods of analyzing advertising messages. The tracking of biometric data combined with AI-based methods that capture human emotions while viewing video content is proposed as a promising direction for such analysis. This paper presents the results of a pilot study based on analytical face-tracking technology using AI, where the subject of the experiment was the analysis of video fragments that may have an impact on the emotional state of the viewer. The AI software platform used was Amazon Rekognition, and the results show that AI analytics provide the ability to track the level of audience engagement in perceiving video content, which helps to improve communication effectiveness. This allows the use of AI to make recommendations for the development of more directed and engaging advertising messages.

人工智能技术正在改进营销工具包，使比以往更快、更高效地处理大量数据成为可能。机器学习是人工智能的一个子集，它使用的算法可以预测哪些广告在特定情况下最有效，从而优化广告定位。这项研究探讨了机器和人类智能在各种社会实践（包括营销和广告）中的共同进化和分配问题。作者描述了研究广告视觉成分的关键方法，并建议修改分析广告信息的传统方法。生物识别数据的跟踪与基于人工智能的方法相结合，在观看视频内容时捕捉人类情绪，被认为是此类分析的一个有前途的方向。本文介绍了基于人工智能分析面部跟踪技术的试点研究的结果，其中实验的主题是分析可能对观看者情绪状态产生影响的视频片段。使用的人工智能软件平台是Amazon Rekognition，结果表明人工智能分析提供了跟踪观众感知视频内容的参与程度的能力，这有助于提高沟通效率。这使得人工智能能够为开发更具针对性和吸引力的广告信息提出建议。使用的人工智能软件平台是Amazon Rekognition，结果表明人工智能分析提供了跟踪观众感知视频内容的参与程度的能力，这有助于提高沟通效率。这使得人工智能能够为开发更具针对性和吸引力的广告信息提出建议。使用的人工智能软件平台是Amazon Rekognition，结果表明人工智能分析提供了跟踪观众感知视频内容的参与程度的能力，这有助于提高沟通效率。这使得人工智能能够为开发更具针对性和吸引力的广告信息提出建议。