We are entering our tenth year of the current Artificial Intelligence (AI)
spring, and, as with previous AI hype cycles, the threat of an AI winter looms.
AI winters occurred because of ineffective approaches towards navigating the
technology valley of death. The 6-D framework provides an end-to-end framework
to successfully navigate this challenge. The 6-D framework starts with problem
decomposition to identify potential AI solutions, and ends with considerations
for deployment of AI-enabled systems. Each component of the 6-D framework and a
precision medicine use case is described in this paper.

这篇文章介绍了一种名为 6-D 框架的终端到终端方法，该方法包括问题分解，AI 解决方案的识别，以及 AI 系统部署方面的考虑。同时以精准医学为例，讲述了框架的各个部分。

创造 AI-Enabled 系统的 6 个 D

The 6-Ds of Creating AI-Enabled Systems

Since its beginning in the 1950s, the field of artificial intelligence has
cycled several times between periods of optimistic predictions and massive
investment ("AI spring") and periods of disappointment, loss of confidence, and
reduced funding ("AI winter"). Even with today's seemingly fast pace of AI
breakthroughs, the development of long-promised technologies such as
self-driving cars, housekeeping robots, and conversational companions has
turned out to be much harder than many people expected. One reason for these
repeating cycles is our limited understanding of the nature and complexity of
intelligence itself. In this paper I describe four fallacies in common
assumptions made by AI researchers, which can lead to overconfident predictions
about the field. I conclude by discussing the open questions spurred by these
fallacies, including the age-old challenge of imbuing machines with humanlike
common sense.

本文介绍了人工智能领域自 20 世纪 50 年代以来的发展历程，列举了 AI 领域研究人员常见假设中的四个谬误，并讨论了这些谬误引发的问题，包括如何将人类的常识运用于机器智能。