Toward Building Science Discovery Machines

TL;DR

This paper reviews the principles and AI systems involved in scientific discovery, emphasizing the need for machines that incorporate reasoning and problem-solving principles to advance scientific knowledge automation.

Contribution

It provides a comprehensive survey of scientific discovery principles, analyzes current AI systems, and advocates for integrating these principles into future discovery machines.

Findings

Current AI systems have limitations in scientific discovery.

Principles of scientific reasoning are used across multiple fields.

Automating these principles could lead to significant advancements.

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 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.