Published: 11 January 2024
人工智能（AI）快速发展出结合文本、图像、视频、音频等类型数据的多种模态。为提升数据价值，负责AI的IT领导者应探索多模态AI并将其纳入企业能力。
AI基础模型会逐渐实现多模态化，即用不同类型的数据（模态）进行训练，并且能够在输入和输出中处理多种模态。这既得益于技术进步，也得益于多模态对于性能、鲁棒性和用户体验的改善。
多模态出现之后，涌现出很多以前无法实现的新型AI用例，包括：回答有关视频和图像的问题，利用文本创建音频和视频，以及构建可通过文本、图像、音频和视频进行动态交互的智能体。多模态AI可以应用于整个企业机构，尤其是在销售和营销、媒体和娱乐、产品设计和客户服务等领域。
各类用例的可行性差异很大。多模态AI在图像生成以及需要结合文本和图像的其他用例中是最先进的技术，但对于同时结合两种以上模态的模型而言，该技术仍处于起步阶段。
多模态对于数据管理、治理和集成会形成挑战，在数据隐私方面会带来新的风险，而且技术使用和定制非常复杂。
向AI团队介绍多模态的概念、优势和风险。鼓励AI专家在其技术专业之外开展AI项目，例如自然语言处理和计算机视觉，打破AI技术孤岛。
明确企业机构中的哪些AI用例在结合多个数据模态的情况下，能比目前使用单模态AI基础模型创造更多的业务价值，例如结合音频、文本和视频信号进行情感分析。为此应开展试点，展示模型可创造的业务价值。
在多模态表现优于单模态的用例中启用多模态AI，例如生成具有复杂文本描述的图像。追踪技术发展下各类新兴用例的可行性，例如视频生成。
尝试多模态AI所需的新技术管道和工作流。通过强大的数据工程和数据治理来识别、降低数据隐私和安全方面的风险。
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