未来突破性AI应由多元智能体协作，而非单一超级智能。
The Future of AI is Many, Not One
- 主张用多元智能体团队替代单一超级智能体
- 多元团队能延缓过早共识，探索非常规解法
- 适合关注AI创新潜力与系统多样性研究者
当前生成式AI的思维模式本质上是孤立的，无论用户交互、模型构建、评测方式，还是商业与科研策略，均体现这一倾向。我们主张，若希望AI推动突破性创新与科学发现，必须摒弃此单一模式。基于复杂系统、组织行为学及科学哲学的研究与理论，我们指出：深邃的知识突破更可能来自认知多样性的智能体协作群体，而非单一超智能体。多元团队拓展解空间，延迟过早共识，支持非主流路径探索。发展多元智能体还能回应批评——现有模型受限于历史数据，缺乏创新所需的创造性洞察。因此，我们认为，变革性基于Transformer的AI未来在于‘众多’，而非‘一个’。
原文摘要 · Abstract (English)
The way we're thinking about generative AI right now is fundamentally individual. We see this not just in how users interact with models but also in how models are built, how they're benchmarked, and how commercial and research strategies using AI are defined. We argue that we should abandon this approach if we're hoping for AI to support groundbreaking innovation and scientific discovery. Drawing on research and formal results in complex systems, organizational behavior, and philosophy of science, we show why we should expect deep intellectual breakthroughs to come from epistemically diverse groups of AI agents working together rather than singular superintelligent agents. Having a diverse team broadens the search for solutions, delays premature consensus, and allows for the pursuit of unconventional approaches. Developing diverse AI teams also addresses AI critics' concerns that current models are constrained by past data and lack the creative insight required for innovation. The upshot, we argue, is that the future of transformative transformer-based AI is fundamentally many, not one.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。