The aim of this article is to explore potential directions for the development of artificial intelligence(AI).It points out that,while current AI can handle the statistical properties of complex systems,it has difficu...The aim of this article is to explore potential directions for the development of artificial intelligence(AI).It points out that,while current AI can handle the statistical properties of complex systems,it has difficulty effectively processing and fully representing their spatiotemporal complexity patterns.The article also discusses a potential path of AI development in the engineering domain.Based on the existing understanding of the principles of multilevel com-plexity,this article suggests that consistency among the logical structures of datasets,AI models,model-building software,and hardware will be an important AI development direction and is worthy of careful consideration.展开更多
This study explores the determinants of impact on ecology in Northern Tanzania.By examining key socio-economic,institutional,and structural factors influencing engagement the study provides insights in strengthening a...This study explores the determinants of impact on ecology in Northern Tanzania.By examining key socio-economic,institutional,and structural factors influencing engagement the study provides insights in strengthening agribusiness networks and improving livelihoods.Data was collected from 215 farmers and 320 traders through a multistage sampling procedure.Heckman AI sample selection model was used in data analysis whereby the findings showed key factors influencing farmers’decisions on ecology were gender and years of formal education at p<0.1,and access to finance and off-farm income at p<0.05.The degree of farmers participation in social groups was influenced by age,household size,off-farm income and business network at p<0.05,number of years in formal education and access to finance at p<0.01,and distance to the market at p<0.1.The decision of traders to impact on ecology was significantly influenced by age and trading experience at p<0.1.Meanwhile,the degree of their involvement in social groups was strongly affected by gender,formal education,and trust at p<0.01,as well as by access to finance and business networks at p<0.05.The study concluded that natural ecology is influenced by socio economic and structural factors but trust among group members determine the degree of participation.The study recommends that strategies to improve agribusiness networks should understand underlying causes of impact on ecology and strengthen available social groups to improve performance of farmers and traders.展开更多
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
By comparing price plans offered by several retail energy firms,end users with smart meters and controllers may optimize their energy use cost portfolios,due to the growth of deregulated retail power markets.To help s...By comparing price plans offered by several retail energy firms,end users with smart meters and controllers may optimize their energy use cost portfolios,due to the growth of deregulated retail power markets.To help smart grid end-users decrease power payment and usage unhappiness,this article suggests a decision system based on reinforcement learning to aid with electricity price plan selection.An enhanced state-based Markov decision process(MDP)without transition probabilities simulates the decision issue.A Kernel approximate-integrated batch Q-learning approach is used to tackle the given issue.Several adjustments to the sampling and data representation are made to increase the computational and prediction performance.Using a continuous high-dimensional state space,the suggested approach can uncover the underlying characteristics of time-varying pricing schemes.Without knowing anything regarding the market environment in advance,the best decision-making policy may be learned via case studies that use data from actual historical price plans.Experiments show that the suggested decision approach may reduce cost and energy usage dissatisfaction by using user data to build an accurate prediction strategy.In this research,we look at how smart city energy planners rely on precise load forecasts.It presents a hybrid method that extracts associated characteristics to improve accuracy in residential power consumption forecasts using machine learning(ML).It is possible to measure the precision of forecasts with the use of loss functions with the RMSE.This research presents a methodology for estimating smart home energy usage in response to the growing interest in explainable artificial intelligence(XAI).Using Shapley Additive explanations(SHAP)approaches,this strategy makes it easy for consumers to comprehend their energy use trends.To predict future energy use,the study employs gradient boosting in conjunction with long short-term memory neural networks.展开更多
文摘The aim of this article is to explore potential directions for the development of artificial intelligence(AI).It points out that,while current AI can handle the statistical properties of complex systems,it has difficulty effectively processing and fully representing their spatiotemporal complexity patterns.The article also discusses a potential path of AI development in the engineering domain.Based on the existing understanding of the principles of multilevel com-plexity,this article suggests that consistency among the logical structures of datasets,AI models,model-building software,and hardware will be an important AI development direction and is worthy of careful consideration.
基金financed as part of the project“Development of a methodology for instrumental base formation for analysis and modeling of the spatial socio-economic development of systems based on internal reserves in the context of digitalization”(FSEG-2023-0008).
文摘This study explores the determinants of impact on ecology in Northern Tanzania.By examining key socio-economic,institutional,and structural factors influencing engagement the study provides insights in strengthening agribusiness networks and improving livelihoods.Data was collected from 215 farmers and 320 traders through a multistage sampling procedure.Heckman AI sample selection model was used in data analysis whereby the findings showed key factors influencing farmers’decisions on ecology were gender and years of formal education at p<0.1,and access to finance and off-farm income at p<0.05.The degree of farmers participation in social groups was influenced by age,household size,off-farm income and business network at p<0.05,number of years in formal education and access to finance at p<0.01,and distance to the market at p<0.1.The decision of traders to impact on ecology was significantly influenced by age and trading experience at p<0.1.Meanwhile,the degree of their involvement in social groups was strongly affected by gender,formal education,and trust at p<0.01,as well as by access to finance and business networks at p<0.05.The study concluded that natural ecology is influenced by socio economic and structural factors but trust among group members determine the degree of participation.The study recommends that strategies to improve agribusiness networks should understand underlying causes of impact on ecology and strengthen available social groups to improve performance of farmers and traders.
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By comparing price plans offered by several retail energy firms,end users with smart meters and controllers may optimize their energy use cost portfolios,due to the growth of deregulated retail power markets.To help smart grid end-users decrease power payment and usage unhappiness,this article suggests a decision system based on reinforcement learning to aid with electricity price plan selection.An enhanced state-based Markov decision process(MDP)without transition probabilities simulates the decision issue.A Kernel approximate-integrated batch Q-learning approach is used to tackle the given issue.Several adjustments to the sampling and data representation are made to increase the computational and prediction performance.Using a continuous high-dimensional state space,the suggested approach can uncover the underlying characteristics of time-varying pricing schemes.Without knowing anything regarding the market environment in advance,the best decision-making policy may be learned via case studies that use data from actual historical price plans.Experiments show that the suggested decision approach may reduce cost and energy usage dissatisfaction by using user data to build an accurate prediction strategy.In this research,we look at how smart city energy planners rely on precise load forecasts.It presents a hybrid method that extracts associated characteristics to improve accuracy in residential power consumption forecasts using machine learning(ML).It is possible to measure the precision of forecasts with the use of loss functions with the RMSE.This research presents a methodology for estimating smart home energy usage in response to the growing interest in explainable artificial intelligence(XAI).Using Shapley Additive explanations(SHAP)approaches,this strategy makes it easy for consumers to comprehend their energy use trends.To predict future energy use,the study employs gradient boosting in conjunction with long short-term memory neural networks.
