The dream of building machines that can do science has inspired scientists for decades. Remarkable advances have been made recently; however, we are still far from achieving this goal. In this paper, we focus on the scientific discovery process where a high level of reasoning and remarkable problem-solving ability are required. We review different machine learning techniques used in scientific discovery with their limitations. We survey and discuss the main principles driving the scientific discovery process. These principles are used in different fields and by different scientists to solve problems and discover new knowledge. We provide many examples of the use of these principles in different fields such as physics, mathematics, and biology. We also review AI systems that attempt to implement some of these principles. We argue that building science discovery machines should be guided by these principles as an alternative to the dominant approach of current AI systems that focuses on narrow objectives. Building machines that fully incorporate these principles in an automated way might open the doors for many advancements.
翻译:数十年来,建筑能够进行科学的机器的梦想激励了科学家们。最近取得了显著的进步;然而,我们仍远未实现这一目标。在本文件中,我们侧重于科学发现过程,需要高度的推理和出色的解决问题能力。我们审查了科学发现中所使用的不同机器学习技术及其局限性。我们调查并讨论了驱动科学发现过程的主要原则。这些原则在不同领域被不同的科学家用来解决问题和发现新知识。我们提供了许多例子,说明这些原则在不同领域,例如物理、数学和生物学等领域的运用情况。我们还审查了试图执行这些原则的AI系统。我们主张,建设科学发现机器应该以这些原则为指导,以这些原则替代当前侧重于狭隘目标的AI系统的主要方法。建立以自动化方式充分纳入这些原则的机器可能会为许多进步打开大门。