本文介绍了一个能让前沿大语言模型独立进行科学研究并交流发现的综合框架——AI科学家，它能生成研究想法、写代码、做实验、展示结果等，还通过应用于机器学习的三个子领域来展示其多功能性，且生成的论文经评估可达到较高水平。

🎯AI科学家是一个全面的框架，能够使大语言模型独立完成科学研究的全过程，包括生成新的研究想法、编写代码、执行实验、可视化结果、撰写完整科学论文以及进行模拟评审过程。

💻该框架在机器学习的三个不同子领域，即扩散建模、基于Transformer的语言建模和学习动力学中得到应用，每个想法都被实施并发展成完整的论文，且成本较低。

📋为评估生成的论文，设计并验证了一个自动评审员，其在评估论文分数方面表现出接近人类的性能，AI科学家生成的论文经此评审可达到顶级机器学习会议的接受阈值。

Published on August 15, 2024 9:32 PM GMT

Authors: Chris Lu, Cong Lu, Robert Tjarko Lange, Jakob Foerster, Jeff Clune, David Ha.

Blogpost: https://sakana.ai/ai-scientist/. 

Abstract:

One of the grand challenges of artificial general intelligence is developing agents capable of conducting scientific research and discovering new knowledge. While frontier models have already been used as aids to human scientists, e.g. for brainstorming ideas, writing code, or prediction tasks, they still conduct only a small part of the scientific process. This paper presents the first comprehensive framework for fully automatic scientific discovery, enabling frontier large language models to perform research independently and communicate their findings. We introduce The AI Scientist, which generates novel research ideas, writes code, executes experiments, visualizes results, describes its findings by writing a full scientific paper, and then runs a simulated review process for evaluation. In principle, this process can be repeated to iteratively develop ideas in an open-ended fashion, acting like the human scientific community. We demonstrate its versatility by applying it to three distinct subfields of machine learning: diffusion modeling, transformer-based language modeling, and learning dynamics. Each idea is implemented and developed into a full paper at a cost of less than $15 per paper. To evaluate the generated papers, we design and validate an automated reviewer, which we show achieves near-human performance in evaluating paper scores. The AI Scientist can produce papers that exceed the acceptance threshold at a top machine learning conference as judged by our automated reviewer. This approach signifies the beginning of a new era in scientific discovery in machine learning: bringing the transformative benefits of AI agents to the entire research process of AI itself, and taking us closer to a world where endless affordable creativity and innovation can be unleashed on the world's most challenging problems. Our code is open-sourced at this https URL.

I think this is important as a proof of concept for the feasibility of and for what automated ML research (including e.g. prosaic alignment research) could look like in the near future. 

I plan to write a separate post with thoughts on the paper and its implications.

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