This is an outdated version published on 2019-11-11. Read the most recent version.Preprint / Version 4
Artificial General Intelligence: A New Perspective, with Application to Scientific Discovery
Keywords:AI, Artificial Intelligence, Science Automation, Scientific Discovery
AbstractThe 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 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.
Download data is not yet available.
2019-11-11 — Updated on 2019-11-11
This work is licensed under a Creative Commons Attribution 4.0 International License.