AI與機器人科學家在科學發現中的規模化定律

Scaling Laws in Scientific Discovery with AI and Robot Scientists

March 28, 2025

作者: Pengsong Zhang, Heng Zhang, Huazhe Xu, Renjun Xu, Zhenting Wang, Cong Wang, Animesh Garg, Zhibin Li, Arash Ajoudani, Xinyu Liu

cs.AI

摘要

科學發現正通過先進的機器人技術和人工智慧迎來快速發展。當前的科學實踐面臨著重大限制，因為手動實驗既耗時又耗費資源，而跨學科研究則需要整合超出個別研究者專業範圍的知識。在此，我們設想了一種自主通用科學家（AGS）的概念，它結合了代理式人工智慧與具身機器人技術，以自動化整個研究生命週期。該系統能夠動態地與物理和虛擬環境互動，同時促進跨多學科知識的整合。通過在研究的各個階段——包括文獻綜述、假設生成、實驗和論文撰寫——部署這些技術，並結合內部反思與外部反饋，該系統旨在顯著減少科學發現所需的時間和資源。基於從虛擬人工智慧科學家到多功能通用人工智慧機器人科學家的演進，AGS展現了突破性的潛力。隨著這些自主系統日益融入研究過程，我們推測科學發現可能會遵循新的規模定律，這些定律可能由這些自主系統的數量和能力所塑造，從而為知識的生成與演變提供新視角。具身機器人對極端環境的適應性，加上科學知識積累的飛輪效應，有望持續突破物理與智力的邊界。

English

Scientific discovery is poised for rapid advancement through advanced robotics and artificial intelligence. Current scientific practices face substantial limitations as manual experimentation remains time-consuming and resource-intensive, while multidisciplinary research demands knowledge integration beyond individual researchers' expertise boundaries. Here, we envision an autonomous generalist scientist (AGS) concept combines agentic AI and embodied robotics to automate the entire research lifecycle. This system could dynamically interact with both physical and virtual environments while facilitating the integration of knowledge across diverse scientific disciplines. By deploying these technologies throughout every research stage -- spanning literature review, hypothesis generation, experimentation, and manuscript writing -- and incorporating internal reflection alongside external feedback, this system aims to significantly reduce the time and resources needed for scientific discovery. Building on the evolution from virtual AI scientists to versatile generalist AI-based robot scientists, AGS promises groundbreaking potential. As these autonomous systems become increasingly integrated into the research process, we hypothesize that scientific discovery might adhere to new scaling laws, potentially shaped by the number and capabilities of these autonomous systems, offering novel perspectives on how knowledge is generated and evolves. The adaptability of embodied robots to extreme environments, paired with the flywheel effect of accumulating scientific knowledge, holds the promise of continually pushing beyond both physical and intellectual frontiers.