2019/10/14 by P. Santhanam, Santhanam, P., Eitan Farchi +3
Computer Science · #Adversarial Robustness in Machine Learning #Computers and Society (cs.CY) #FOS: Computer and information sciences #Machine Learning and Algorithms #Software Testing and Debugging Techniques
paper · pdf · doi:10.48550/arxiv.1910.12582
openalex publication_date 2019/10/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.