Google DeepMind新论文提出从通用人工智能到超级智能的四条路径:持续扩展(计算、模型规模、数据、测试时推理)、算法范式革新(超越Transformer架构)、递归自我改进(AI加速自身研发)、多智能体集体智能(众多专业AI智能体协作出超人类智能)。扩展可能遇到数据、算力、能源瓶颈;递归改进最不确定;多智能体路径最易被低估,通过专业化与协调能超越单个强模型。ASI可能不是单次跃迁,而是AI辅助创造更好AI的加速链。
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.