In recent years,large-scale artificial intelligence(AI)models have become a focal point in technology,attracting widespread attention and acclaim.Notable examples include Google’s BERT and OpenAI’s GPT,which have sc...In recent years,large-scale artificial intelligence(AI)models have become a focal point in technology,attracting widespread attention and acclaim.Notable examples include Google’s BERT and OpenAI’s GPT,which have scaled their parameter sizes to hundreds of billions or even tens of trillions.This growth has been accompanied by a significant increase in the amount of training data,significantly improving the capabilities and performance of these models.Unlike previous reviews,this paper provides a comprehensive discussion of the algorithmic principles of large-scale AI models and their industrial applications from multiple perspectives.We first outline the evolutionary history of these models,highlighting milestone algorithms while exploring their underlying principles and core technologies.We then evaluate the challenges and limitations of large-scale AI models,including computational resource requirements,model parameter inflation,data privacy concerns,and specific issues related to multi-modal AI models,such as reliance on text-image pairs,inconsistencies in understanding and generation capabilities,and the lack of true“multi-modality”.Various industrial applications of these models are also presented.Finally,we discuss future trends,predicting further expansion of model scale and the development of cross-modal fusion.This study provides valuable insights to inform and inspire future future research and practice.展开更多
基金supported in part by the National Natural Science Foundation of China(Nos.62406207 and 62476224)the Project of Basic Scientific Research of Central Universities of China(No.J2023-026)+2 种基金the project of Science and Technology Department in Sichuan Province(No.25QNJJ5597)the Science and Technology Project of the Tibet Autonomous Region(No.XZ202401ZY0016)the Project of Sichuan Province Engineering Technology Research Center of General Aircraft Maintenance(No.GAMRC2023YB06).
文摘In recent years,large-scale artificial intelligence(AI)models have become a focal point in technology,attracting widespread attention and acclaim.Notable examples include Google’s BERT and OpenAI’s GPT,which have scaled their parameter sizes to hundreds of billions or even tens of trillions.This growth has been accompanied by a significant increase in the amount of training data,significantly improving the capabilities and performance of these models.Unlike previous reviews,this paper provides a comprehensive discussion of the algorithmic principles of large-scale AI models and their industrial applications from multiple perspectives.We first outline the evolutionary history of these models,highlighting milestone algorithms while exploring their underlying principles and core technologies.We then evaluate the challenges and limitations of large-scale AI models,including computational resource requirements,model parameter inflation,data privacy concerns,and specific issues related to multi-modal AI models,such as reliance on text-image pairs,inconsistencies in understanding and generation capabilities,and the lack of true“multi-modality”.Various industrial applications of these models are also presented.Finally,we discuss future trends,predicting further expansion of model scale and the development of cross-modal fusion.This study provides valuable insights to inform and inspire future future research and practice.
