Google DeepMind这篇论文把AGI到ASI的四种路径拆解得明明白白，递归改进和多智能体集体两条线特别值得关注。
Google DeepMind发布论文《From AGI to ASI》，提出从AGI到ASI的四种技术路径：持续扩展算力/模型规模/数据/测试时推理、算法范式转变、递归自我改进、多智能体集体智能。论文指出递归改进是最不确定的路径，因为AI加速AI研究可能遇到真实测试、稀缺硬件或新想法瓶颈。多智能体集体被作者视为最被低估的路径，通过专业化、速度和协调可超越单个模型。ASI可能不是单一事件，而是AI帮助创造更好AI和科学工具带来的加速链。
完整报告： https://t.co/qbqnwTAbCF 源：https://t.co/NKZoyOa4ip
完整报告： arxiv.org/pdf/2606.12683 源： x.com/rohanpaul_ai/s… Rohan Paul @rohanpaul_ai 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. ---- – arxiv. org/abs/2606.12683 Title: "From AGI to ASI" 🔗 View Quoted Tweet 💬 0 🔄 0 ❤️ 0 👀 491 ⚡