The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Towards artificial general intelligence via a multimodal foundation model
Open accessAbstract
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”.
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Cites
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Cited by
The current and future state of AI interpretation of medical images
Rajpurkar, P., & Lungren, M. P. (2023) · New England Journal of Medicine · doi:10.1056/nejmra2301725 · Cited by 443
The shaky foundations of large language models and foundation models for electronic health records
Wornow, M., Xu, Y., Thapa, R., Patel, B., Steinberg, E., Fleming, S., Pfeffer, M. A., Fries, J., & Shah, N. H. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00879-8 · Cited by 324
Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook.
AlSaad, R., Abd-alrazaq, A., Boughorbel, S., Ahmed, A., Renault, M.-A., Damseh, R., & Sheikh, J. (2024) · Journal of medical Internet research · doi:10.2196/59505 · Cited by 349
“This underscores the importance of developing any-to-any M-LLMs, which are crucial for realizing real artificial general intelligence (AGI) [52,53].”
Review of multimodal machine learning approaches in healthcare
Krones, F., Marikkar, U., Parsons, G., Szmul, A., & Mahdi, A. (2025) · Information Fusion · doi:10.1016/j.inffus.2024.102690 · Cited by 235
McIntosh, T. R., Susnjak, T., Liu, T., Watters, P., Xu, D., Liu, D., & Halgamuge, M. N. (2025) · Technologies · doi:10.3390/technologies13020051 · Cited by 102
“…AI systems that emulate the comprehensive and multifaceted aspects of human cognition, is a sub-domain focused on developing AI with the capability for holistic understanding and complex reasoning that closely aligns with the depth and breadth of human cognitive abilities [282], [283], [32].”
BackgroundInfluential
Metadata
- Authors
- Fei, N., Lu, Z., Gao, Y., Yang, G., Huo, Y., Wen, J., Lu, H., Song, R., Gao, X., Xiang, T., Sun, H., & Wen, J.-R.
- Venue
- Nature Communications
- Published
- 2022-06-02
- Volume
- 13
- Issue
- 1
- Pages
- 3094-3094
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W4281643269
- Cited by
- 280
- References
- 48
- FWCI
- 18.53
- Type
- journal article
- Language
- en
- Open access
- gold
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- Fei, N., Lu, Z., Gao, Y., Yang, G., Huo, Y., Wen, J., Lu, H., Song, R., Gao, X., Xiang, T., Sun, H., & Wen, J.-R. (2022). Towards artificial general intelligence via a multimodal foundation model. Nature Communications. https://doi.org/10.1038/s41467-022-30761-2
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