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Large AI Models and Their Applications: Classification, Limitations, and Potential Solutions
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  • Jing Bi,
  • Ziqi Wang,
  • Haitao Yuan,
  • Xiankun Shi,
  • Ziyue Wang,
  • Jia Zhang,
  • Mengchu Zhou,
  • Rajkumar Buyya
Jing Bi
Beijing University of Technology
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Ziqi Wang
Beijing University of Technology
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Haitao Yuan
Beihang University

Corresponding Author:yuan@buaa.edu.cn

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Xiankun Shi
Beijing University of Technology
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Ziyue Wang
Beijing Information Science and Technology University
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Jia Zhang
Southern Methodist University
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Mengchu Zhou
New Jersey Institute of Technology
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Rajkumar Buyya
The University of Melbourne
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Abstract

not-yet-known not-yet-known not-yet-known unknown In recent years, Large Models (LMs) have been rapidly developed, including large language models, visual foundation models, and multimodal LMs. They are updated and iterated at a very fast pace. These LMs can accomplish many tasks, e.g., daily work assistant, intelligent customer service, and intelligent factory scheduling. Their development has contributed to various industries in human society. However, the architectural flaws of LMs lead to several problems, including illusions and difficulty in locating errors, limiting their performance. Solving these problems properly can facilitate their further development. This work first introduces the development of LMs and identifies their current problems, including data and energy consumption, catastrophic forgetting, reasoning ability, and localization fault. Then, potential solutions to these problems are provided. Finally, LMs’ applications in autonomous driving technologies and smart industrial productions are discussed. By embracing the advantages of LMs, many industries are expected to achieve promising prospects in the future.
16 Aug 2024Submitted to Software: Practice and Experience
19 Aug 2024Submission Checks Completed
19 Aug 2024Assigned to Editor
19 Aug 2024Review(s) Completed, Editorial Evaluation Pending
02 Sep 2024Reviewer(s) Assigned
30 Oct 2024Editorial Decision: Revise Major
04 Dec 20241st Revision Received
05 Dec 2024Submission Checks Completed
05 Dec 2024Assigned to Editor
05 Dec 2024Review(s) Completed, Editorial Evaluation Pending
06 Dec 2024Reviewer(s) Assigned
20 Dec 2024Editorial Decision: Revise Minor