Towards artificial general intelligence via a multimodal foundation model
By Nanyi Fei, Zhiwu Lu, Yizhao Gao, Guoxing Yang, Yuqi Huo, Jing Wen, Haoyu Lu, Ruihua Song, Xin Gao, Tao Xiang and 2 more. Nature Communications, 2021
331 citations recorded by the source corpus.
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”. Artificial intelligence approaches inspired by human cognitive function have usually single learned ability. The authors propose a multimodal foundation model that demonstrates the cross-domain learning and adaptation for broad range of downstream cognitive tasks.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/cb8dcaf8e5fe7256577c6bc83e11dd64d8f3ae31
Citations in the corpus
- Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook (2024, unknown): “This underscores the importance of developing any-to-any M-LLMs, which are crucial for realizing real artificial general intelligence (AGI) [52,53].”
- A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics (2023, unknown): “LLMs for Healthcare: from fundamental task to advanced applications. have been deployed, which can answer professional medical questions for patients and serve as guides in hospitals.”