【中文版在后面】

The Industrial AI Framework, developed in 2022, is a useful guide for integrating artificial intelligence (AI) within industrial Internet of Things (IIoT) systems. The framework provides organizations with valuable insights into how AI can improve efficiency, safety, decision-making, and business outcomes in areas like manufacturing, robotics, and predictive maintenance.  It helps businesses adopt AI technology to uncover insights from complex data, enable digital transformation, and prepare for future challenges by leveraging AI's potential to optimize operations and create new business models.

My favorite part about this framework is it describes four different viewpoints that help frame concerns and guide AI adoption in industrial systems:

• Business Viewpoint: This focuses on maximizing value and improving return on investment (ROI) by leveraging AI to extract insights from large data sets, enabling digital transformation, and future-proofing organizations. AI can enhance production efficiency, lower costs, and provide new capabilities for businesses.
• Usage Viewpoint: Here, the focus is on how AI systems are used, with attention to trustworthiness, security, privacy, and ethical concerns. It also covers societal impacts and use cases in the industrial AI market.
• Functional Viewpoint: This covers the technical structure and interaction of AI components within an IIoT system. It focuses on how AI models are built and deployed to solve industrial problems, including learning techniques and data management.
• Implementation Viewpoint: This addresses practical considerations for deploying AI in industrial environments, such as reliability, response time, bandwidth, and security. It ensures that AI systems can be efficiently integrated and maintained.

Using the Framework in 2024

A lot can happen in just two years, and in the world of AI, those years feel like a leap forward.  While this Industrial AI Framework remains extremely valuable as a foundation, the rapid advancements in AI have created new opportunities and challenges that weren't fully addressed when this document was first created.  The framework was, and still is, a solid guide for integrating analytical AI within Industrial IoT (IIoT) systems, helping organizations optimize operations, improve decision-making, and unlock hidden value in their data.  It touches on key concerns like data management, system architecture, security, ethics, and deployment strategies.  But in 2024, to stay relevant and future-proof, it needs to be broadened to reflect the realities of today's AI landscape.

One of the most significant changes since 2022 has been the explosive growth of Generative AI.  Unlike the AI discussed in this framework, which focuses on analyzing historical data to predict outcomes or optimize existing processes, Generative AI can create new content, designs, and solutions.  This ability shifts AI from being purely analytical to becoming an engine for innovation.  For example, Generative AI can not only predict when a machine might fail (as analytical AI would) but also generate multiple solutions for preventing that failure or even suggest new designs for more efficient machinery.

Much of the core architecture, data governance, and ethical considerations still apply.  But updating the framework to reflect Generative AI would broaden its scope significantly.  Here's how AI leaders can leverage what's already built while enhancing it for today's capabilities:

• Broaden the Scope: The original framework focuses specifically on IIoT systems.  Updating it to include all operational systems across industries, not just industrial settings, would make it more flexible.  Generative AI can be applied to areas like supply chain management, healthcare, and even retail, where its ability to create new solutions can drive transformative change.
• Integrate Generative AI: Leaders should build upon the analytical AI models described in the document by incorporating Generative AI for tasks like product design, system optimization, and scenario planning.  For instance, in manufacturing, Generative AI can simulate various production processes and generate designs for machinery or products that are more efficient or cost-effective.
• Update Ethical and Trustworthiness Guidelines: With Generative AI's ability to produce new content autonomously, the framework's sections on trustworthiness and ethics will need to evolve.  Leaders should expand on the original guidelines, ensuring that issues like bias, explainability, and misuse of AI-generated content are addressed.  Transparency and user control will become even more critical as AI moves from making decisions to creating solutions that could significantly impact operations.
• Data and Compute Infrastructure: Generative AI often requires far more computational power and data than traditional models.  AI leaders should enhance the Implementation Viewpoint to include guidance on the increased infrastructure demands for deploying Generative AI.  Considerations for storage, processing, and real-time data generation will be crucial in industries where AI is integrated directly into physical operations, like autonomous vehicles or robotics.
• Use Cases and New Applications: The framework already discusses AI use cases like predictive maintenance and smart manufacturing.  Leaders can take this further by adding Generative AI applications, such as creating simulations for future scenarios, generating optimized supply chain routes, or even producing creative marketing content based on business data.  These new use cases will highlight the expanding role of AI beyond traditional optimization.

【中文版】

工业人工智能框架于2022年开发，是将人工智能(AI)集成到工业物联网(IIoT)系统中的有用指南。该框架为组织提供了关于人工智能如何在制造、机器人和预测性维护等领域提高效率、安全性、决策和业务成果的宝贵见解。它帮助企业采用人工智能技术，从复杂数据中发现洞察力，实现数字化转型，并通过利用人工智能的潜力来优化运营和创建新的商业模式，为未来的挑战做好准备。

关于这个框架，我最喜欢的部分是它描述了四种不同的观点，这些观点有助于构建关注点，并指导工业系统中人工智能的采用:

• 商业观点:这侧重于通过利用人工智能从大型数据集中提取见解，实现价值最大化和提高投资回报率(ROI)，从而实现数字化转型，并使组织经得起未来考验。人工智能可以提高生产效率，降低成本，并为企业提供新的能力。
• 使用观点:在这里，重点是如何使用人工智能系统，关注可信度、安全性、隐私和道德问题。它还涵盖了工业人工智能市场的社会影响和用例。
• 功能观点:这涵盖了工业物联网系统中人工智能组件的技术结构和交互。它侧重于如何构建和部署人工智能模型来解决工业问题，包括学习技术和数据管理。
• 实现观点:这解决了在工业环境中部署AI的实际考虑，例如可靠性、响应时间、带宽和安全性。它确保人工智能系统可以有效地集成和维护。

2024年使用框架

短短两年可以发生很多事情，在人工智能的世界里，这几年感觉像是一个飞跃。虽然这个工业人工智能框架作为一个基础仍然非常有价值，但人工智能的快速发展创造了新的机遇和挑战，这些在本文件最初创建时没有得到充分解决。该框架过去是，现在仍然是在工业物联网(IIoT)系统中集成分析人工智能的坚实指南，帮助组织优化运营，改进决策，并释放数据中的隐藏价值。它涉及数据管理、系统架构、安全性、道德规范和部署策略等关键问题。但在2024年，为了保持相关性和面向未来，它需要扩大规模，以反映当今人工智能领域的现实。

自2022年以来最重要的变化之一是生成式人工智能的爆炸式增长。与本框架中讨论的专注于分析历史数据以预测结果或优化现有流程的人工智能不同，生成式人工智能可以创建新的内容、设计和解决方案。这种能力将人工智能从纯粹的分析变成了创新的引擎。例如，生成式人工智能不仅可以预测机器何时可能出现故障(就像分析式人工智能一样)，还可以生成多种解决方案来防止故障发生，甚至可以为更高效的机器提出新的设计建议。

许多核心架构、数据治理和道德考虑仍然适用。但更新框架以反映生成式人工智能将大大扩大其范围。以下是人工智能领导者如何利用已经建立的东西，同时增强它以适应当今的能力:

• 扩大范围:最初的框架专门关注工业物联网系统。将其更新为包括跨行业的所有操作系统，而不仅仅是工业设置，将使其更加灵活。生成式人工智能可以应用于供应链管理、医疗保健甚至零售等领域，在这些领域，它创造新解决方案的能力可以推动变革。

  
  

• 整合生成式人工智能:领导者应该在文件中描述的分析型人工智能模型的基础上，将生成式人工智能纳入产品设计、系统优化和场景规划等任务。例如，在制造业中，生成式人工智能可以模拟各种生产过程，并生成更高效或更具成本效益的机械或产品设计。

  
  

• 更新道德和可信度准则:随着生成式人工智能自主生成新内容的能力，框架中关于可信度和道德的部分将需要发展。领导者应该在最初的指导方针的基础上进行扩展，确保诸如偏见、可解释性和滥用人工智能生成的内容等问题得到解决。随着人工智能从决策转向创造可能对运营产生重大影响的解决方案，透明度和用户控制将变得更加重要。

  
  

• 数据和计算基础设施:生成式人工智能通常比传统模型需要更多的计算能力和数据。人工智能领导者应该加强实施观点，包括对部署生成式人工智能的基础设施需求增加的指导。在自动驾驶汽车或机器人等人工智能直接集成到物理操作中的行业，对存储、处理和实时数据生成的考虑将至关重要。

  
  

• 用例和新应用:该框架已经讨论了预测性维护和智能制造等人工智能用例。领导者可以通过添加生成式人工智能应用程序来进一步实现这一点，例如为未来场景创建模拟，生成优化的供应链路线，甚至根据业务数据生成创造性的营销内容。这些新的用例将突出人工智能在传统优化之外的扩展作用