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
In recent years,the rapid advancement of artificial intelligence(AI)has fostered deep integration between large AI models and robotic technology.Robots such as robotic dogs capable of carrying heavy loads on mountaino...In recent years,the rapid advancement of artificial intelligence(AI)has fostered deep integration between large AI models and robotic technology.Robots such as robotic dogs capable of carrying heavy loads on mountainous terrain or performing waste disposal tasks and humanoid robots that can execute high-precision component installations have gradually reached the public eye,raising expectations for embodied intelligent robots.展开更多
The rapid advancement of artificial intelligence technologies,particularly in recent years,has led to the emergence of several large parameter artificial intelligence weather forecast models.These models represent a s...The rapid advancement of artificial intelligence technologies,particularly in recent years,has led to the emergence of several large parameter artificial intelligence weather forecast models.These models represent a significant breakthrough,overcoming the limitations of traditional numerical weather prediction models and indicating the emergence of profound potential tools for atmosphere-ocean forecasts.This study explores the evolution of these advanced artificial intelligence forecast models,and based on the identified commonalities,proposes the“Three Large Rules”for large weather forecast models:a large number of parameters,a large number of predictands,and large potential applications.We discuss the capacity of artificial intelligence to revolutionize numerical weather prediction,briefly outlining the underlying reasons for the significant improvement in weather forecasting.While acknowledging the high accuracy,computational efficiency,and ease of deployment of large artificial intelligence forecast models,we also emphasize the irreplaceable values of traditional numerical forecasts and explore the challenges in the future development of large-scale artificial intelligence atmosphere-ocean forecast models.We believe that the optimal future of atmosphere-ocean weather forecast lies in achieving a seamless integration of artificial intelligence and traditional numerical models.Such a synthesis is anticipated to offer a more advanced and reliable approach for improved atmosphere-ocean forecasts.Finally,we illustrate how forecasters can leverage the large weather forecast models through an example by building an artificial intelligence model for global ocean wave forecast.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.
文摘In recent years,the rapid advancement of artificial intelligence(AI)has fostered deep integration between large AI models and robotic technology.Robots such as robotic dogs capable of carrying heavy loads on mountainous terrain or performing waste disposal tasks and humanoid robots that can execute high-precision component installations have gradually reached the public eye,raising expectations for embodied intelligent robots.
基金supported by the National Key Research and Development Program of China(Grant No.2020YFA0608000)the National Natural Science Foundation of China(Grant No.42030605)。
文摘The rapid advancement of artificial intelligence technologies,particularly in recent years,has led to the emergence of several large parameter artificial intelligence weather forecast models.These models represent a significant breakthrough,overcoming the limitations of traditional numerical weather prediction models and indicating the emergence of profound potential tools for atmosphere-ocean forecasts.This study explores the evolution of these advanced artificial intelligence forecast models,and based on the identified commonalities,proposes the“Three Large Rules”for large weather forecast models:a large number of parameters,a large number of predictands,and large potential applications.We discuss the capacity of artificial intelligence to revolutionize numerical weather prediction,briefly outlining the underlying reasons for the significant improvement in weather forecasting.While acknowledging the high accuracy,computational efficiency,and ease of deployment of large artificial intelligence forecast models,we also emphasize the irreplaceable values of traditional numerical forecasts and explore the challenges in the future development of large-scale artificial intelligence atmosphere-ocean forecast models.We believe that the optimal future of atmosphere-ocean weather forecast lies in achieving a seamless integration of artificial intelligence and traditional numerical models.Such a synthesis is anticipated to offer a more advanced and reliable approach for improved atmosphere-ocean forecasts.Finally,we illustrate how forecasters can leverage the large weather forecast models through an example by building an artificial intelligence model for global ocean wave forecast.
