Abstract
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.
Abstract (translated)
URL
https://arxiv.org/abs/2103.15551
