Programming the world of uncertain things (keynote)

Abstract

Computing has entered the era of uncertain data, in which hardware and software generate and reason about estimates. Applications use estimates from sensors, machine learning, big data, humans, and approximate hardware and software. Unfortunately, developers face pervasive correctness, programmability, and optimization problems due to estimates. Most programming languages unfortunately make these problems worse. We propose a new programming abstraction called Uncertain embedded into languages, such as C#, C++, Java, Python, and JavaScript. Applications that consume estimates use familiar discrete operations for their estimates; overloaded conditional operators specify hypothesis tests and applications use them control false positives and negatives; and new compositional operators express domain knowledge. By carefully restricting the expressiveness, the runtime automatically implements correct statistical reasoning at conditionals, relieving developers of the need to implement or deeply understand statistics. We demonstrate substantial programmability, correctness, and efficiency benefits of this programming model for GPS sensor navigation, approximate computing, machine learning, and xBox.