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从基础智能到通用智能: 基于大模型的GenAI和AGI之现状与展望

Summary

This survey and position paper, led by researchers at the CAS Institute of Automation, reviews the state of generative AI (GenAI) worldwide and in China. It asks how China can close its gap with the United States on the road toward artificial general intelligence (AGI).

The authors trace the technical lineage from deep learning and the Transformer to diffusion models. They catalogue leading foreign systems, including GPT-4, Claude 3, Llama 2, Stable Diffusion and Sora. They then map China's crowded field of general models (文心, 通义千问/Qwen, 盘古, Kimi, InternLM) and domain models in medicine, finance, education, industry and AI4S. A side-by-side comparison of compute, data, algorithms and ecosystem shows a clear US lead. The paper also contrasts Western and Chinese approaches to AI safety governance across the model lifecycle.

As remedies, it proposes:

  • energy-efficient compute powered by western China's renewables;
  • federated data;
  • specialised domain models;
  • a blockchain/DAO-based federated ecosystem called TAO.

It ends with a speculative vision of a society shared by digital humans, robots and people. Its main value is as a map of China's LLM landscape and policy thinking.

Key points

  • Hardware gap: Nvidia H100 (SXM) delivers 1,979 TFLOPS FP16 with 80 GB HBM3 and 900 GB/s interconnect. Huawei Ascend 910 delivers 280 TFLOPS with 16 GB. The software stacks also differ: CUDA versus CANN.
  • Ecosystem gap: China has 70 AI unicorns worth about 1.3T, against 315 worth 5.9T in the US. China has 60+ highly cited papers versus 220+, and its share of institutions/talent is about 14%/13.85% versus 55%/56.55%. Sources are State of AI Report 2023 and Aminer.
  • Notable Chinese models:
    • Kimi's context window grew from 200k to 2 million Chinese characters.
    • Pangu weather model, trained on 39 years of global data, reportedly beat numerical forecasting.
    • 紫东太初 was billed as the first vision–text–speech pretrained model.
  • Domestic governance: It follows the 《生成式人工智能服务管理暂行办法》, which introduced a filing ("备案") regime. Its seven content and compliance dimensions are checked through red-team tests, sandboxes and third-party evaluation such as OpenCompass, C-Eval and SuperCLUE.
  • Federated data and TAO: The proposed federated data architecture has six components: real data, virtual data, federated experiments, fusion, security and trusted federated intelligence. TAO combines blockchain "TRUE" with DAO. The envisioned future workforce is over 80% digital humans, under 15% robots and under 5% biological humans.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages · Natural language processing

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.16383/j.aas.c240156

@inproceedings{2024,
  title = {{从基础智能到通用智能: 基于大模型的GenAI和AGI之现状与展望}},
  author = {缪青海 and 王兴霞 and 杨静 and 赵勇 and 王雨桐 and 陈圆圆 and 田永林 and 俞怡 and 林懿伦 and 鄢然 and 马嘉琪 and 那晓翔 and 王飞跃},
  booktitle = {自动化学报 (Acta Automatica Sinica)},
  year = {2024},
  doi = {10.16383/j.aas.c240156},
  url = {https://doi.org/10.16383/j.aas.c240156}
}

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