RI: Medium: Learned Dynamic Prioritization

这个项目使用机器学习来加速一类与人工智能相关的计算机程序的执行。在给定程序和一类输入的情况下，新方法自动寻找快速的执行策略，同时仍获得高水平的精度。该项目重点研究了统计人工智能背后的主要推理算法：动态规划、信任传播、马尔科夫链蒙特卡罗和回溯搜索。这些推理算法中的每一个都面临着一个巨大的搜索空间，迭代地扩展或改进其对该空间的描述。每种算法都必须不断地选择下一步的计算步骤。机会是学习做出这些选择的策略。有些选择位于“关键路径”上，可以帮助系统找到准确的输出，而另一些选择则主要导致工作浪费。在上下文中评估选择的学习策略本身可能是计算密集型的，所以该方法也学会了在相同的框架内加速这一过程。该项目将传播软件，并将在几个领域产生更广泛的影响。这些目标算法是自然语言处理、语音处理、机器视觉、计算生物学、健康信息学和音乐处理的核心。它们对一组观测结果形成连贯的全球分析的能力是智能的标志，并将使人工系统能够帮助人类理解和表现。随着研究人员开发越来越复杂的统计模型，加速它们是至关重要的。此外，开发的学习方法将在其他试图学习计算或行为策略的环境中有用。

This project uses machine learning to accelerate the execution of a class of computer programs relevant to AI. Given a program and a class of inputs, the new methods automatically seek execution strategies that are fast while still achieving a high level of accuracy.The project focuses on the main inference algorithms that underlie statistical AI: dynamic programming, belief propagation, Markov chain Monte Carlo, and backtracking search. Each of these inference algorithms faces an enormous search space, iteratively extending or refining its picture of this space. Each algorithm must continually choose which computational step to take next.The opportunity is to learn a strategy for making these choices. Some choices are on the "critical path" and help the system find an accurate output, while others lead mainly to wasted work. The learned strategy for evaluating choices in context may itself be computationally intensive, so the method learns to speed that up as well, within the same framework.The project will disseminate software and will have broader impact on several fields. The targeted algorithms are central to natural language processing, speech processing, machine vision, computational biology, health informatics and music processing. Their ability to form a coherent global analysis of a set of observations is a hallmark of intelligence, and will enable artificial systems that aid human understanding and performance. Speeding them up is critical as researchers develop increasingly sophisticated statistical models.Furthermore, the learning methodologies developed will be useful in other settings that attempt to learn computational or behavioral strategies.