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Towards artificial general intelligence via a multimodal foundation model

Author

Listed:
  • Nanyi Fei

    (Renmin University of China
    Beijing Key Laboratory of Big Data Management and Analysis Methods
    Renmin University of China)

  • Zhiwu Lu

    (Renmin University of China
    Beijing Key Laboratory of Big Data Management and Analysis Methods)

  • Yizhao Gao

    (Renmin University of China
    Beijing Key Laboratory of Big Data Management and Analysis Methods)

  • Guoxing Yang

    (Renmin University of China
    Beijing Key Laboratory of Big Data Management and Analysis Methods)

  • Yuqi Huo

    (Beijing Key Laboratory of Big Data Management and Analysis Methods
    Renmin University of China)

  • Jingyuan Wen

    (Renmin University of China
    Beijing Key Laboratory of Big Data Management and Analysis Methods)

  • Haoyu Lu

    (Renmin University of China
    Beijing Key Laboratory of Big Data Management and Analysis Methods)

  • Ruihua Song

    (Renmin University of China
    Beijing Key Laboratory of Big Data Management and Analysis Methods)

  • Xin Gao

    (King Abdullah University of Science and Technology)

  • Tao Xiang

    (University of Surrey)

  • Hao Sun

    (Renmin University of China
    Beijing Key Laboratory of Big Data Management and Analysis Methods)

  • Ji-Rong Wen

    (Renmin University of China
    Beijing Key Laboratory of Big Data Management and Analysis Methods
    Renmin University of China)

Abstract

The fundamental goal of artificial intelligence (AI) is to mimic the core cognitive activities of human. Despite tremendous success in the AI research, most of existing methods have only single-cognitive ability. To overcome this limitation and take a solid step towards artificial general intelligence (AGI), we develop a foundation model pre-trained with huge multimodal data, which can be quickly adapted for various downstream cognitive tasks. To achieve this goal, we propose to pre-train our foundation model by self-supervised learning with weak semantic correlation data crawled from the Internet and show that promising results can be obtained on a wide range of downstream tasks. Particularly, with the developed model-interpretability tools, we demonstrate that strong imagination ability is now possessed by our foundation model. We believe that our work makes a transformative stride towards AGI, from our common practice of “weak or narrow AI” to that of “strong or generalized AI”.

Suggested Citation

  • Nanyi Fei & Zhiwu Lu & Yizhao Gao & Guoxing Yang & Yuqi Huo & Jingyuan Wen & Haoyu Lu & Ruihua Song & Xin Gao & Tao Xiang & Hao Sun & Ji-Rong Wen, 2022. "Towards artificial general intelligence via a multimodal foundation model," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-30761-2
    DOI: 10.1038/s41467-022-30761-2
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    References listed on IDEAS

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    1. R. Quian Quiroga & L. Reddy & G. Kreiman & C. Koch & I. Fried, 2005. "Invariant visual representation by single neurons in the human brain," Nature, Nature, vol. 435(7045), pages 1102-1107, June.
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    Cited by:

    1. Hermann, Erik & Puntoni, Stefano, 2024. "Artificial intelligence and consumer behavior: From predictive to generative AI," Journal of Business Research, Elsevier, vol. 180(C).

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