What Is Really Different in Engineering AI-Enabled Systems?
What Is Really Different in Engineering AI-Enabled Systems?
复制标题
工程人工智能系统有什么真正的不同？
DOI：
10.1109/ms.2020.2993662
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Ipek Ozkaya
中科院分区：
文献类型：
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作者：
Ipek Ozkaya
机器学习（ML）算法的进步和计算能力的提高导致了对渴望利用人工智能（AI）的系统的巨大投资，特别是ML。AI支持系统，软件依赖系统，包括实现模拟学习和问题解决算法的数据和组件，具有与单独的软件系统不同的本质特征然而，此类系统的开发和维护也与构建、部署和维护软件系统有许多相似之处。一个常见的观察是，尽管软件系统是确定性的，您可以根据规范构建和测试，但支持ai的系统，特别是那些包含ML组件的系统，通常是概率性的。由于预测算法的不确定性，带有ML组件的系统可能有很高的误差幅度。误差范围可能与无法提前预测结果或无法复制相同的结果有关。这一特点使得人工智能系统难以测试和验证因此，很容易假设我们所知道的关于软件系统的设计和推理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。工程人工智能系统的狡猾之处在于，它们“就像”传统的软件系统一样，我们可以设计和推理，直到它们出现。再保险。
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.