DeepMind 分析 AGI 到 ASI 的四种路线
Google DeepMind 发布论文《From AGI to ASI》，探讨从通用人工智能（AGI）到超级人工智能（ASI）的四种可能路径：持续扩展计算与模型规模、算法范式突破（超越 Transformer）、递归自我改进（AI 加速 AI 研发）、多智能体集体智能。论文指出，扩展路径可能受限于数据、计算和能源瓶颈；递归改进最不确定，因需真实世界测试和稀缺硬件；多智能体集体智能最被低估，通过专业化与协调可超越单一模型。ASI 可能不是单一事件，而是 AI 辅助创造更好 AI 的加速链。
Beautiful paper from Google DeepMind. Explains th…
Beautiful paper from Google DeepMind.
Explains the pathways from AGI to ASI, and why that jump could happen through several routes.
The authors frame the AGI-to-ASI transition around 4 technical pathways:
- continued scaling of compute, model size, data, and test-time inference;
- algorithmic paradigm shifts beyond today’s transformer-based foundation-model stack;
- recursive self-improvement, where AI accelerates AI R&D and improves future systems; and
- multi-agent collective intelligence, where large populations of specialized agents coordinate into a superhuman group agent.
Scaling may work for a while, but it could hit limits in data, compute, energy, or weaker returns from making systems larger.
Recursive improvement is the most uncertain path, because AI could speed up AI research, but that loop may also slow if hard research problems need real-world testing, scarce hardware, or new ideas.
Multi-agent collectives may be the most underappreciated path, because a society of competent digital workers could outperform a brilliant individual model through specialization, speed, and coordination.
The big point is that ASI may not arrive as 1 sudden event, but as a chain of faster changes as AI helps create better AI and stronger scientific tools.
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Link – arxiv. org/abs/2606.12683
Title: "From AGI to ASI"