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
DeepSeek,a Chinese artificial intelligence(AI)startup,has released their V3 and R1 series models,which attracted global attention due to their low cost,high performance,and open-source advantages.This paper begins by ...DeepSeek,a Chinese artificial intelligence(AI)startup,has released their V3 and R1 series models,which attracted global attention due to their low cost,high performance,and open-source advantages.This paper begins by reviewing the evolution of large AI models focusing on paradigm shifts,the mainstream large language model(LLM)paradigm,and the DeepSeek paradigm.Subsequently,the paper highlights novel algorithms introduced by DeepSeek,including multi-head latent attention(MLA),mixture-of-experts(MoE),multi-token prediction(MTP),and group relative policy optimization(GRPO).The paper then explores DeepSeek's engineering breakthroughs in LLM scaling,training,inference,and system-level optimization architecture.Moreover,the impact of DeepSeek models on the competitive AI landscape is analyzed,comparing them to mainstream LLMs across various fields.Finally,the paper reflects on the insights gained from DeepSeek's innovations and discusses future trends in the technical and engineering development of large AI models,particularly in data,training,and reasoning.展开更多
With the rapid development of large AI models,large decision models have further broken through the limits of human cognition and promoted the innovation of decision-making paradigms in extensive fields such as medici...With the rapid development of large AI models,large decision models have further broken through the limits of human cognition and promoted the innovation of decision-making paradigms in extensive fields such as medicine and transportation.In this paper,we systematically expound on the intelligent decision-making technology and prospects driven by large AI models.Specifically,we first review the development of large AI models in recent years.Then,from the perspective of methods,we introduce important theories and technologies of large decision models,such as model architecture and model adaptation.Next,from the perspective of applications,we introduce the cutting-edge applications of large decision models in various fields,such as autonomous driving and knowledge decision-making.Finally,we discuss existing challenges,such as security issues,decision bias and hallucination phenomenon as well as future prospects,from both technology development and domain applications.We hope this review paper can help researchers understand the important progress of intelligent decision-making driven by large AI models.展开更多
With the exponential growth in the complexity of Internet architectures and the widespread adoption of cloud-native service technologies,traditional operation and maintenance(Artificial Intelligence for IT Operations,...With the exponential growth in the complexity of Internet architectures and the widespread adoption of cloud-native service technologies,traditional operation and maintenance(Artificial Intelligence for IT Operations,AIOps)models-largely reliant on the paradigm of"expert rules+fixed scripts"-have become increasingly passive and inefficient when confronted with unknown faults and massive volumes of alerts.This study focuses on the application of large-scale AI model-based intelligent agents across the full lifecycle of Internet fault handling,aiming to construct autonomous O&M agents endowed with capabilities of perception,decision-making,and execution.The paper first analyzes the core challenges in current fault management:alert storms leading to missed and false incident reports,cross-system data silos hindering root cause localization,and heavy reliance on expert experience in manual troubleshooting,resulting in delayed response times.On this basis,a hierarchical solution architecture based on large-model agents is proposed,comprising a multi-source data perception layer,a fault reasoning and decision-making layer,and an automated execution layer[1].By integrating Retrieval-Augmented Generation(RAG)techniques with an O&M knowledge base,the proposed approach equips intelligent agents with the ability to interpret topology metrics,log semantics,and change events.Furthermore,the introduction of chain-of-thought reasoning and reflection mechanisms enables the agents to simulate expert diagnostic pathways,thereby achieving millisecond-level anomaly detection and minute-level root cause identification.展开更多
With the rapid progress of AI technology,AI big models with transformer architecture as the core has made great progress in natural language processing,computer vision and other fields.Smart grid is a modern power sys...With the rapid progress of AI technology,AI big models with transformer architecture as the core has made great progress in natural language processing,computer vision and other fields.Smart grid is a modern power system integrated with advanced information,communication and control technology.The complexity and variability of the system and the massive reference data provide application scenarios for the application of AI large model.This paper systematically expounds the key technologies matching with AI large model and its adaptability to the core links of smart grid,and focuses on the role of AI large model in smart grid construction,such as new energy grid connection,equipment management,grid topology optimization and dispatching decision,such as specific application modes and cases in load forecasting,real-time dispatching and multi-energy complementarity.At the same time,this paper deeply analyzes the key challenges in data,technology,engineering and security faced by the application of AI large model in various fields of power,and puts forward the corresponding optimal solutions.Finally,combined with typical cases,the future development direction of the integration of digital twins,generative AI and other technologies is conceived,which provides a theoretical reference and practical path for promoting the autonomous and efficient development of smart grid.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
The 2025 World Artificial Intelligence Conference(WAIC)and High-Level Meeting on Global AI Governance was held in Shanghai from July 26-29,2025.During this conference,many new artificial intelligence(AI)ideas were sha...The 2025 World Artificial Intelligence Conference(WAIC)and High-Level Meeting on Global AI Governance was held in Shanghai from July 26-29,2025.During this conference,many new artificial intelligence(AI)ideas were shared by leading AI pioneers,such as Geoffrey Hinton,Yoshua Bengio,and Andrew Chi-Chih Yao.More than 40 new AI models were released,and more than 60 robots were demonstrated.展开更多
聚焦大型公共建筑尤其是体育场馆的智慧低碳运维问题,针对当前运维过程中存在的数据割裂、认知鸿沟与流程非标准化等痛点,提出了以大模型为核心的“AI as Hub”运维模式,并构建了数据标准化、认知标准化与流程标准化三位一体的“DCP”...聚焦大型公共建筑尤其是体育场馆的智慧低碳运维问题,针对当前运维过程中存在的数据割裂、认知鸿沟与流程非标准化等痛点,提出了以大模型为核心的“AI as Hub”运维模式,并构建了数据标准化、认知标准化与流程标准化三位一体的“DCP”架构。通过建立标准数据管理体系,实现从数据采集、建模、传输到开放的规范化;通过增强认知框架,将复杂物理实体逐级降维为大模型可理解的语义信息;并在流程层面形成“感知-决策-执行-反馈”的闭环机制。以杭州奥体中心的实践为例,体系化介绍了所述方法的应用过程与措施层面的实现。结果显示,场馆年度节电约517万kW·h,运营期能耗费用降低18%,碳排放降低2634 tCO_(2),并实现碳资产开发与交易,形成经济与环境双重效益。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supported by the National Natural Science Foundation of China(62233005,62293502,U2441245,62176185,U23B2057,62306112)the STCSM Science and Technology Innovation Action Plan Computational Biology Program(24JS2830400)+2 种基金the State Key Laboratory of Industrial Control Technology,China(ICT2024A22)the Shanghai Sailing Program(23YF1409400)the National Science and Technology Major Project(2024ZD0532403).
文摘DeepSeek,a Chinese artificial intelligence(AI)startup,has released their V3 and R1 series models,which attracted global attention due to their low cost,high performance,and open-source advantages.This paper begins by reviewing the evolution of large AI models focusing on paradigm shifts,the mainstream large language model(LLM)paradigm,and the DeepSeek paradigm.Subsequently,the paper highlights novel algorithms introduced by DeepSeek,including multi-head latent attention(MLA),mixture-of-experts(MoE),multi-token prediction(MTP),and group relative policy optimization(GRPO).The paper then explores DeepSeek's engineering breakthroughs in LLM scaling,training,inference,and system-level optimization architecture.Moreover,the impact of DeepSeek models on the competitive AI landscape is analyzed,comparing them to mainstream LLMs across various fields.Finally,the paper reflects on the insights gained from DeepSeek's innovations and discusses future trends in the technical and engineering development of large AI models,particularly in data,training,and reasoning.
基金supported by the National Natural Science Foundation of China(Grant 62293545)Shenzhen Science and Technology Program(Grant ZDSYS20220323112000001).
文摘With the rapid development of large AI models,large decision models have further broken through the limits of human cognition and promoted the innovation of decision-making paradigms in extensive fields such as medicine and transportation.In this paper,we systematically expound on the intelligent decision-making technology and prospects driven by large AI models.Specifically,we first review the development of large AI models in recent years.Then,from the perspective of methods,we introduce important theories and technologies of large decision models,such as model architecture and model adaptation.Next,from the perspective of applications,we introduce the cutting-edge applications of large decision models in various fields,such as autonomous driving and knowledge decision-making.Finally,we discuss existing challenges,such as security issues,decision bias and hallucination phenomenon as well as future prospects,from both technology development and domain applications.We hope this review paper can help researchers understand the important progress of intelligent decision-making driven by large AI models.
文摘With the exponential growth in the complexity of Internet architectures and the widespread adoption of cloud-native service technologies,traditional operation and maintenance(Artificial Intelligence for IT Operations,AIOps)models-largely reliant on the paradigm of"expert rules+fixed scripts"-have become increasingly passive and inefficient when confronted with unknown faults and massive volumes of alerts.This study focuses on the application of large-scale AI model-based intelligent agents across the full lifecycle of Internet fault handling,aiming to construct autonomous O&M agents endowed with capabilities of perception,decision-making,and execution.The paper first analyzes the core challenges in current fault management:alert storms leading to missed and false incident reports,cross-system data silos hindering root cause localization,and heavy reliance on expert experience in manual troubleshooting,resulting in delayed response times.On this basis,a hierarchical solution architecture based on large-model agents is proposed,comprising a multi-source data perception layer,a fault reasoning and decision-making layer,and an automated execution layer[1].By integrating Retrieval-Augmented Generation(RAG)techniques with an O&M knowledge base,the proposed approach equips intelligent agents with the ability to interpret topology metrics,log semantics,and change events.Furthermore,the introduction of chain-of-thought reasoning and reflection mechanisms enables the agents to simulate expert diagnostic pathways,thereby achieving millisecond-level anomaly detection and minute-level root cause identification.
文摘With the rapid progress of AI technology,AI big models with transformer architecture as the core has made great progress in natural language processing,computer vision and other fields.Smart grid is a modern power system integrated with advanced information,communication and control technology.The complexity and variability of the system and the massive reference data provide application scenarios for the application of AI large model.This paper systematically expounds the key technologies matching with AI large model and its adaptability to the core links of smart grid,and focuses on the role of AI large model in smart grid construction,such as new energy grid connection,equipment management,grid topology optimization and dispatching decision,such as specific application modes and cases in load forecasting,real-time dispatching and multi-energy complementarity.At the same time,this paper deeply analyzes the key challenges in data,technology,engineering and security faced by the application of AI large model in various fields of power,and puts forward the corresponding optimal solutions.Finally,combined with typical cases,the future development direction of the integration of digital twins,generative AI and other technologies is conceived,which provides a theoretical reference and practical path for promoting the autonomous and efficient development of smart grid.
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 by the National Key R&D Program of China(2022YFF1203202)the Major Project of Guangzhou National Laboratory(GZNL2024A01003)。
文摘The 2025 World Artificial Intelligence Conference(WAIC)and High-Level Meeting on Global AI Governance was held in Shanghai from July 26-29,2025.During this conference,many new artificial intelligence(AI)ideas were shared by leading AI pioneers,such as Geoffrey Hinton,Yoshua Bengio,and Andrew Chi-Chih Yao.More than 40 new AI models were released,and more than 60 robots were demonstrated.
文摘聚焦大型公共建筑尤其是体育场馆的智慧低碳运维问题,针对当前运维过程中存在的数据割裂、认知鸿沟与流程非标准化等痛点,提出了以大模型为核心的“AI as Hub”运维模式,并构建了数据标准化、认知标准化与流程标准化三位一体的“DCP”架构。通过建立标准数据管理体系,实现从数据采集、建模、传输到开放的规范化;通过增强认知框架,将复杂物理实体逐级降维为大模型可理解的语义信息;并在流程层面形成“感知-决策-执行-反馈”的闭环机制。以杭州奥体中心的实践为例,体系化介绍了所述方法的应用过程与措施层面的实现。结果显示,场馆年度节电约517万kW·h,运营期能耗费用降低18%,碳排放降低2634 tCO_(2),并实现碳资产开发与交易,形成经济与环境双重效益。
