Towards artificial general intelligence via a multimodal foundation model

Nanyi Fei, Zhiwu Lu (), Yizhao Gao, Guoxing Yang, Yuqi Huo, Jingyuan Wen, Haoyu Lu, Ruihua Song, Xin Gao, Tao Xiang, Hao Sun () and Ji-Rong Wen ()
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Nanyi Fei: Renmin University of China
Zhiwu Lu: Renmin University of China
Yizhao Gao: Renmin University of China
Guoxing Yang: Renmin University of China
Yuqi Huo: Beijing Key Laboratory of Big Data Management and Analysis Methods
Jingyuan Wen: Renmin University of China
Haoyu Lu: Renmin University of China
Ruihua Song: Renmin University of China
Xin Gao: King Abdullah University of Science and Technology
Tao Xiang: University of Surrey
Hao Sun: Renmin University of China
Ji-Rong Wen: Renmin University of China

Nature Communications, 2022, vol. 13, issue 1, 1-13

Abstract: 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”.

Date: 2022
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DOI: 10.1038/s41467-022-30761-2

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