迈向建筑科学发现机器

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 in order to build science discovery machines and speed up the scientific discovery process, we should build theoretical and computational frameworks that encapsulate these principles. Building machines that fully incorporate these principles in an automated way might open the doors for many advancements.

建造可以做科学的机器的梦想激发了数十年来的科学家。最近取得了显着进展。但是，我们离实现这一目标还很遥远。在本文中，我们关注于需要高水平的推理能力和出色的解决问题能力的科学发现过程。我们回顾了科学发现中使用的各种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理。这些原理在不同领域和不同科学家中使用，以解决问题和发现新知识。我们提供了在物理，数学和生物学等不同领域中使用这些原理的许多示例。我们还将回顾尝试实施其中一些原则的AI系统。我们认为，为了构建科学发现机器并加快科学发现过程，我们应该构建包含这些原理的理论和计算框架。以自动化方式完全结合了这些原理的建筑机械可能会为许多进步打开大门。