Can AI Understand Our Universe? Test of Fine-Tuning GPT by Astrophysical
  Data
Yu Wang Shu-Rui Zhang Aidin Momtaz Rahim Moradi Fatemeh Rastegarnia Narek Sahakyan Soroush Shakeri Liang Li
ChatGPT has been the most talked-about concept in recent months, captivating
both professionals and the general public alike, and has sparked discussions
about the changes that artificial intelligence (AI) will bring to the world. As
physicists and astrophysicists, we are curious about if scientific data can be
correctly analyzed by large language models (LLMs) and yield accurate physics.
In this article, we fine-tune the generative pre-trained transformer (GPT)
model by the astronomical data from the observations of galaxies, quasars,
stars, gamma-ray bursts (GRBs), and the simulations of black holes (BHs), the
fine-tuned model demonstrates its capability to classify astrophysical
phenomena, distinguish between two types of GRBs, deduce the redshift of
quasars, and estimate BH parameters. We regard this as a successful test,
marking the LLM's proven efficacy in scientific research. With the ever-growing
volume of multidisciplinary data and the advancement of AI technology, we look
forward to the emergence of a more fundamental and comprehensive understanding
of our universe. This article also shares some interesting thoughts on data
collection and AI design. Using the approach of understanding the universe -
looking outward at data and inward for fundamental building blocks - as a
guideline, we propose a method of series expansion for AI, suggesting ways to
train and control AI that is smarter than humans.