AI科學家：邁向完全自動化的開放式科學探索

The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery

August 12, 2024

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

cs.AI

摘要

人工通用智能的一個重大挑戰是發展能夠進行科學研究並發現新知識的代理人。儘管前沿模型已被用作協助人類科學家，例如用於腦力激盪想法、編寫代碼或預測任務，但它們仍然僅完成科學過程的一小部分。本文提出了第一個全自動科學發現的全面框架，使前沿大型語言模型能夠獨立進行研究並傳達其發現。我們介紹了AI科學家，它能夠生成新穎的研究想法、編寫代碼、執行實驗、視覺化結果、通過撰寫完整的科學論文描述其發現，然後運行一個模擬的審查過程進行評估。原則上，這個過程可以重複進行，以開放式方式迭代地發展想法，就像人類科學社區一樣。我們展示了其多功能性，應用於機器學習的三個不同子領域：擴散建模、基於Transformer的語言建模和學習動態。每個想法的實施和發展成一篇完整的論文的成本不到每篇15美元。為了評估生成的論文，我們設計並驗證了一個自動審稿人，我們展示其在評估論文分數方面達到接近人類的表現。AI科學家可以生成超過我們自動審稿人評估的頂級機器學習會議的接受門檻的論文。這種方法標誌著機器學習科學發現的新時代的開始：將AI代理人的轉型效益帶給AI本身整個研究過程，使我們更接近一個可以釋放對世界上最具挑戰性問題的無限負擔得起的創造力和創新的世界。我們的代碼在https://github.com/SakanaAI/AI-Scientist 上開源。

English

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 https://github.com/SakanaAI/AI-Scientist