# Google DeepMind论文《From AGI to ASI》提出四条技术路径

- 来源：Rohan Paul (@rohanpaul_ai)
- 发布时间：2026-07-06 06:26
- AIHOT 分数：46
- AIHOT 链接：https://aihot.virxact.com/items/cmr8e143901cgsl0d2ht13h0l
- 原文链接：https://x.com/rohanpaul_ai/status/2073896201876017487

## AI 摘要

Google DeepMind新论文《From AGI to ASI》提出AGI向ASI过渡的四条技术路径：持续扩展计算、模型规模、数据和测试时推理；超越Transformer的算法范式转变；递归自我改进（AI加速AI研发）；多智能体集体智能（专业化智能体协调成超人群体）。扩展可能遇到数据、算力、能源瓶颈或边际收益递减；递归改进最不确定（需真实世界测试、稀缺硬件或新想法）；多智能体集体智能可能被低估——数字工作者社会通过专业化、速度和协调可超越单个卓越模型。ASI可能不是单一突发事件，而是AI助力的加速变化链。

## 正文

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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- arxiv. org/abs/2606.12683

Title： "From AGI to ASI"