  Recent progress in artificial intelligence (AI) using deep learning
techniques has triggered its wide-scale use across a broad range of
applications. These systems can already perform tasks such as natural language
processing of voice and text, visual recognition, question-answering,
recommendations and decision support. However, at the current level of
maturity, the use of an AI component in mission-critical or safety-critical
applications can have unexpected consequences. Consequently, serious concerns
about reliability, repeatability, trust, and maintainability of AI applications
remain. As AI becomes pervasive despite its shortcomings, more systematic ways
of approaching AI software development and certification are needed. These
fundamental aspects establish the need for a discipline on "AI Engineering".
This paper presents the current perspective of relevant AI engineering concepts
and some key challenges that need to be overcome to make significant progress
in this important area.
