SoD-HCER: Learning Based Programming

今天编写的大量软件与自然发生的（传感器）数据（诸如文本、语音、图像和视频、金融数据流和生物序列）交互，并且需要相对于复杂并且通常难以根据观察到的原始数据明确定义的概念进行推理（例如，确定图像中的人的性别，确定文章的主题，确定当前是否有超过三个人在某人的办公室中开会，以适应资源和链接的多种属性的方式在网格中调度计算。 需要这种能力的应用程序预计将在未来几年迅速增长，甚至更加重要。 虽然传统的编程语言依赖于程序员来明确地定义所涉及的所有概念和关系，但是利用在测量级别上高度可变和模糊的自然发生的数据进行编程需要编程模型，其中一些变量、概念和关系在编程时可能是未知的，可以仅以数据驱动的方式来定义，或者在不依赖于以这种方式获得的其它概念的情况下可能不会被明确地定义。 它必须能够对不依赖于对测量环境的严格假设的变量进行推理，并且需要围绕语义级交互模型进行推理，该模型通过依赖于数据的组件实现，并支持对真实世界观察的抽象。 今天的编程范式和相应的编程语言都不利于实现这一目标。 因此，尽管机器学习已经取得了二十年的进步，并且明确需要具有重要可训练（数据依赖）组件的系统，但今天很少有系统包含重要的机器学习组件，甚至更少使用单个分类器。 在这个基于学习的编程（LBP）的项目中，PI将探索一种新的软件工程范式，允许程序员将可训练变量无缝地整合到程序中，从而能够使用高级概念进行推理，而无需根据它们可能依赖的所有变量或它们之间的函数依赖关系明确定义它们;这些可以以数据驱动的方式通过学习操作符来确定，所述操作符的细节从程序员那里抽象出来。 在这项工作中，PI将充实他所设想的LBP范式的细节，并实现LBP语言，并通过开发普适计算和自然语言处理两个领域的应用程序来研究它。更广泛的影响：该项目将导致软件工程和机器学习领域的交叉施肥和相互振兴。 使计算机系统的发展，互动和科普自然发生的（传感器）数据的变化将需要在编译和软件工程问题的根本进步。 相反，LBP工具的可用性将激励机器学习领域的研究人员探索依赖于大量相互依赖的学习者进行推理的过程，作为为程序员提供更好的抽象的手段，以便他们能够更有效地解决涉及此类数据的越来越复杂的应用程序。

A significant amount of the software written today interacts with naturally occurring (sensor) data such as text, speech, images and video, streams of financial data, and biological sequences, and needs to reason with respect to concepts that are complex and often difficult to define explicitly in terms of the raw data observed (e.g., determining the gender of a person in an image, determining the topic of an article, determining whether more than three people are currently meeting in someone's office, scheduling a computation in a grid in a way that adapts to a multitude of properties of the resources and links. Applications that require such abilities are expected to rapidly grow even more important in future years. While conventional programming languages rely on a programmer to explicitly define all the concepts and relations involved, programming with naturally occurring data that is highly variable and ambiguous at the measurement level necessitates a programming model in which some of the variables, concepts and relations may not be known at programming time, may be defined only in a data driven way, or may not be unambiguously defined without relying on other concepts acquired this way. It must be possible to reason with respect to variables that do not depend on tight assumptions on the environment in which the measurements are taken, and needs to center around a semantic level interaction model made possible via components that are data-dependent and support abstractions over real-world observations. Today's programming paradigms, and the corresponding programming languages, are not conducive to that goal. Consequently, despite two decades of progress in machine learning, and a clear need for systems with significant trainable (data dependent) components, few systems today incorporate significant machine learning components, and even fewer use more than a single classifier. In this project on Learning Based Programming (LBP), the PI will explore a novel software engineering paradigm that allows a programmer seamless incorporation of trainable variables into the program and, consequently, the ability to reason using high-level concepts without the need to explicitly define them in terms of all the variables they might depend on, or the functional dependencies among them; these may be determined in a data-driven way, via learning operators whose details are abstracted away from the programmer. In this work, the PI will flesh out the details of the LBP paradigm he envisages, and implement an LBP language and study it via the development of applications in two areas: ubiquitous computing and natural language processing.Broader Impacts: This project will lead to cross-fertilization and mutual reinvigoration of the software engineering and machine learning fields. Enabling the development of computer systems that interact and cope with the variability of naturally occurring (sensor) data will require fundamental advances in compilation and software engineering issues. Conversely, availability of the LBP vehicle will motivate researchers in machine learning to explore the process of making inferences that rely on a large number of mutually dependent learners as a means to providing programmers with better abstractions so that they can more effectively tackle a broad range of increasingly complex applications involving such data.