Can AI Understand Our Universe? Test of Fine-Tuning GPT by Astrophysical Data
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866917640934522880 |
|---|---|
| author | Wang, Yu Zhang, Shu-Rui Momtaz, Aidin Moradi, Rahim Rastegarnia, Fatemeh Sahakyan, Narek Shakeri, Soroush Li, Liang |
| author_facet | Wang, Yu Zhang, Shu-Rui Momtaz, Aidin Moradi, Rahim Rastegarnia, Fatemeh Sahakyan, Narek Shakeri, Soroush Li, Liang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_10019 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Can AI Understand Our Universe? Test of Fine-Tuning GPT by Astrophysical Data Wang, Yu Zhang, Shu-Rui Momtaz, Aidin Moradi, Rahim Rastegarnia, Fatemeh Sahakyan, Narek Shakeri, Soroush Li, Liang Instrumentation and Methods for Astrophysics Astrophysics of Galaxies High Energy Astrophysical Phenomena Artificial Intelligence Machine Learning Data Analysis, Statistics and Probability 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. |
| title | Can AI Understand Our Universe? Test of Fine-Tuning GPT by Astrophysical Data |
| topic | Instrumentation and Methods for Astrophysics Astrophysics of Galaxies High Energy Astrophysical Phenomena Artificial Intelligence Machine Learning Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2404.10019 |