Advances in machine learning (ML) algorithms and increasing availability of computational power have resulted in huge investments in systems that aspire to exploit artificial intelligence (AI), in particular ML. AIenabled systems, software-reliant systems that include data and components that implement algorithms mimicking learning and problem solving, have inherently different characteristics than software systems alone.1 However, the development and sustainment of such systems also have many parallels with building, deploying, and sustaining software systems. A common observation is that although software systems are deterministic and you can build and test to a specification, AI-enabled systems, in particular those that include ML components, are generally probabilistic. Systems with ML components can have a high margin of error due to the uncertainty that often follows predictive algorithms. The margin of error can be related to the inability to predict the result in advance or the same result cannot be reproduced. This characteristic makes AI-enabled systems hard to test and verify.2 Consequently, it is easy to assume that what we know about designing and reasoning about software systems does not immediately apply in AI engineering. AI-enabled systems are software systems. The sneaky part about engineering AI systems is they are "just like" conventional software systems we can design and reason about until they?re not.

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工程人工智能系统的真正不同之处是什么？

机器学习 (ML) 算法的进步和计算能力可用性的提高导致对渴望利用人工智能 (AI)，尤其是机器学习的系统进行了大量投资。人工智能系统是一种依赖软件的系统，包括数据和实现模拟学习和解决问题的算法的组件，与单独的软件系统有着本质不同的特征。 1 然而，此类系统的开发和维护也与构建、部署和维护有许多相似之处。持续的软件系统。一个常见的观察结果是，尽管软件系统是确定性的，您可以按照规范进行构建和测试，但支持 AI 的系统，尤其是那些包含 ML 组件的系统，通常是概率性的。由于通常遵循预测算法的不确定性，具有 ML 组件的系统可能具有很高的误差幅度。误差幅度可能与无法提前预测结果或无法重现相同结果有关。这一特性使得支持人工智能的系统难以测试和验证。2 因此，很容易假设我们对软件系统的设计和推理的了解并不能立即应用于人工智能工程。支持人工智能的系统是软件系统。关于工程人工智能系统的狡猾部分是它们“就像”传统的软件系统，我们可以设计和推理直到它们不是。这一特性使得支持人工智能的系统难以测试和验证。2 因此，很容易假设我们对软件系统的设计和推理的了解并不能立即应用于人工智能工程。支持人工智能的系统是软件系统。关于工程人工智能系统的狡猾部分是它们“就像”传统的软件系统，我们可以设计和推理直到它们不是。这一特性使得支持人工智能的系统难以测试和验证。2 因此，很容易假设我们对软件系统的设计和推理的了解并不能立即应用于人工智能工程。支持人工智能的系统是软件系统。关于工程人工智能系统的狡猾部分是它们“就像”传统的软件系统，我们可以设计和推理直到它们不是。