Expeditions: Collaborative Research: Understanding the World Through Code

探险：合作研究：通过代码了解世界

基本信息

批准号：
1918889
负责人：
Isil Dillig
金额：
$ 77.68万
依托单位：
University of Texas at Austin
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-04-01 至 2025-03-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1918889&HistoricalAwards=false
关键词：
Expeditions Collaborative Research Understanding World

项目摘要

In almost every field of science, it is now possible to capture large amounts of data. This has led machine learning to play an increasingly important role in scientific discovery, for example sifting through large amounts of data to identify interesting events. But modern machine learning techniques are less well suited for the critical tasks of devising hypotheses consistent with the data or imagining new experiments to test those hypotheses. The goal of this Expeditions project is to develop new learning techniques that can help automate this process of generating scientific theories from data. In order to ground this research in real applications, the project focuses on four domains where these techniques can have the most significant impact: organic chemistry, RNA splicing, cognitive and behavioral science, and computing systems. Machine learning is already demonstrating value in all of these domains, including predicting properties of organic compounds, recognizing complex social activities, and modeling the performance of computer systems. However, the proposed techniques could have a transformative impact in all of these domains by helping scientists gain a deeper understanding of the processes that give rise to their data. This deeper understanding could lead to important contributions ranging from more efficient drug discovery to improved teaching methods grounded on a better understanding of cognition. To realize this vision, the project will develop new methods for learning neurosymbolic models that combine neural elements capable of identifying complex patterns in data with symbolic constructs that are able to represent higher level concepts. The approach is based on the observation that programming languages provide a uniquely expressive formalism to describe complex models. The aim is therefore to develop learning techniques that can produce models that look more like the models that scientists already write by hand in code. These neurosymbolic techniques will more easily incorporate prior knowledge about the phenomena being modeled, and produce interpretable models that can be analyzed to devise new experiments or to infer causal relations. By developing these techniques and building them into tools that can be used by scientists in a variety of fields, this project has the potential to revolutionize the way scientific knowledge is derived from data. More broadly, these new techniques will be useful in any setting that requires learning more interpretable models with strong requirements on their desired behavior.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在几乎每个科学领域，现在都可以捕获大量数据。这使机器学习在科学发现中起着越来越重要的作用，例如筛选大量数据以识别有趣的事件。但是，现代的机器学习技术不太适合设计与数据或想象新实验以检验这些假设一致的假设的关键任务。该探险项目的目标是开发新的学习技术，以帮助自动化从数据中生成科学理论的过程。为了在实际应用中进行这项研究，该项目着重于四个领域，这些领域可能会产生最大的影响：有机化学，RNA剪接，认知和行为科学以及计算系统。机器学习已经在所有这些领域中都证明了价值，包括预测有机化合物的特性，识别复杂的社交活动以及对计算机系统的性能进行建模。但是，提出的技术可以通过帮助科学家对产生数据的过程有更深入的了解，从而对所有这些领域产生变革性的影响。这种更深入的理解可能会带来重要的贡献，从更有效的药物发现到改进的教学方法，这些方法基于对认知的更好理解。为了实现这一愿景，该项目将开发新的方法来学习神经符号模型，这些方法结合了能够识别数据中的复杂模式的神经元素与能够代表更高级别概念的符号结构的复杂模式。该方法基于这样的观察，即编程语言提供了一种独特的表达形式主义来描述复杂的模型。因此，目的是开发学习技术，这些技术可以产生模型，看起来更像科学家已经手工编写的模型。这些神经成像技术将更容易地纳入有关所建模现象的先验知识，并产生可解释的模型，这些模型可以分析以设计新的实验或推断因果关系。通过开发这些技术并将它们构建成各个领域的科学家可以使用的工具，该项目有可能彻底改变科学知识的方式。更广泛地说，这些新技术将在任何需要学习对其所需行为有强烈要求的可解释模型的环境中都有用。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估评估来通过评估来支持的。