Expeditions: Collaborative Research: Understanding the World Through Code

在几乎每一个科学领域，现在都有可能捕获大量的数据。这使得机器学习在科学发现中发挥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。但现代机器学习技术不太适合设计与数据一致的假设或设想新的实验来测试这些假设的关键任务。这个探险项目的目标是开发新的学习技术，帮助从数据中生成科学理论的过程自动化。为了使这项研究在实际应用中落地，该项目将重点放在四个领域，这些技术可以产生最显著的影响：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展示了价值，包括预测有机化合物的性质，识别复杂的社会活动，以及为计算机系统的性能建模。然而，提出的技术可以通过帮助科学家更深入地了解产生数据的过程，在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基于对认知的更好理解而改进的教学方法。为了实现这一愿景，该项目将开发学习神经符号模型的新方法，将能够识别数据中复杂模式的神经元素与能够表示更高层次概念的符号结构结合起来。该方法基于这样一种观察，即编程语言提供了一种独特的表达形式来描述复杂的模型。因此，其目的是开发学习技术，使模型看起来更像科学家已经用代码手工编写的模型。这些神经符号技术将更容易地结合关于被建模现象的先验知识，并产生可解释的模型，这些模型可以通过分析来设计新的实验或推断因果关系。通过开发这些技术，并将其构建为各种领域的科学家可以使用的工具，该项目有可能彻底改变从数据中获取科学知识的方式。更广泛地说，这些新技术将在任何需要学习对其期望行为有强烈要求的更多可解释模型的环境中发挥作用。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.