自主科学发现的智能体经济

中文摘要 AI 辅助

近年来，智能体人工智能（AI）系统的进展标志着AI for Science领域的转变：从使用单个AI系统执行狭窄任务，转向能够编排复杂、端到端研究工作流程并执行（半）自主科学发现的多智能体系统。多智能体AI for Science系统的发展主要集中于提升AI系统的认知能力，特别是使高级推理和假设生成更加可靠。然而，仅关注认知能力的提升可能会忽视对资源的适当管理，而这是科学发现中的一个关键瓶颈。测试和验证科学假设与实验在物理上和经济上都是资源密集型的，且资源有限。这意味着，要在自主科学发现方面取得重大进展，例如提高人类-AI共同科学家的互补性或实现真正闭环的自动化过程，我们必须将推理能力的持续改进与稳健的资源管理相结合。在本文中，我们通过发展科学智能体经济、市场和制度的必要基础，概述了一种AI资源管理基础设施。该基础设施的目标是赋能AI智能体和人类科学家有效（i）协作并确定研究优先级，（ii）分配功劳，（iii）追踪问责与责任，以及（iv）防范恶意使用和信息安全风险。最后，我们探讨（半）自主科学发现的宏观社会影响，以指导治理政策的制定，确保AI驱动的发现及衍生未来技术的公平分配。

英文摘要

Recent advances in agentic Artificial Intelligence (AI) systems have marked a shift in AI for Science: moving away from the use of individual AI systems for narrow task execution, toward multi-agent systems capable of orchestrating complex, end-to-end research workflows and performing (semi-)autonomous scientific discovery. The development of multi-agent AI-for-science systems has primarily focused on improving the cognitive capabilities of AI systems, specifically by making advanced reasoning and hypothesis generation more reliable. However, focusing only on cognitive capability improvement could ignore appropriate management of resources, a key bottleneck in scientific discovery. Testing and validating scientific hypotheses and experiments is, physically and economically, resource-intensive and resources are limited. This means that to make significant advancements in autonomous scientific discovery, such as improving human-AI co-scientist complementarity or reaching a truly closed-loop automated process, we must pair ongoing improvements in reasoning capabilities with robust resource management. In this paper, we outline an infrastructure for AI resource management by developing the necessary foundations of scientific agent economies, markets, and institutions. The aim of this infrastructure is to empower AI agents and human scientists to effectively (i) collaborate and establish research priorities, (ii) assign credit, (iii) track accountability and liability, and (iv) safeguard against malicious use and information security risks. Finally, we engage with the macro-level societal implications. of (semi-)autonomous scientific discovery to inform the development of governance policies ensuring an equitable distribution of AI-driven discoveries and derivative future technologies.

发表机构

• Google DeepMind（谷歌DeepMind）

机构由 AI 辅助整理，请以论文原文为准。