Large AI Models and Their Applications: Classification, Limitations, and Potential Solutions

Background: 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. Aims: 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. Methods: This work first introduces the development of LMs and identifies their current problems, including data and energy consumption, catastrophic forgetting, reasoning ability, localization fault, and ethical problems. Then, potential solutions to these problems are provided, including increase data and computation capability, neural-symbolic synergy, and data orientation to human pattern. Discussion: This work discusses developing vertical domain LMs on top of some base LMs. In addition, this work introduces three typical real-world applications of LMs, including autonomous driving, smart industrial productions, and intelligent medical assistance. Conclusion: By embracing the advantages of LMs and solving their fundamental problems, many industries are expected to achieve promising prospects in the future.