As AI systems scale, the limitations of cloud-based architectures, including latency, bandwidth, and privacy concerns, demand decentralized alternatives. Federated learning (FL) and Edge AI provide a paradigm shift by...As AI systems scale, the limitations of cloud-based architectures, including latency, bandwidth, and privacy concerns, demand decentralized alternatives. Federated learning (FL) and Edge AI provide a paradigm shift by combining privacy preserving training with efficient, on device computation. This paper introduces a cutting-edge FL-edge integration framework, achieving a 10% to 15% increase in model accuracy and reducing communication costs by 25% in heterogeneous environments. Blockchain based secure aggregation ensures robust and tamper-proof model updates, while exploratory quantum AI techniques enhance computational efficiency. By addressing key challenges such as device variability and non-IID data, this work sets the stage for the next generation of adaptive, privacy-first AI systems, with applications in IoT, healthcare, and autonomous systems.展开更多
If you look into the EU AI Act,you will find that the requirements are specified at a very high level.This is because the details are defined elsewhere.That is what standardization is doing.If we look at the world of ...If you look into the EU AI Act,you will find that the requirements are specified at a very high level.This is because the details are defined elsewhere.That is what standardization is doing.If we look at the world of AI standardization,there are three tiers-the international tier,the European tier and the national tier.There are also AI standardization committees at all three levels.展开更多
The rapid development of Artificial Intelligence(AI)has profoundly reshaped numerous scientific and technological domains,including cartography and geographic information science[1].AI techniques—particularly deep le...The rapid development of Artificial Intelligence(AI)has profoundly reshaped numerous scientific and technological domains,including cartography and geographic information science[1].AI techniques—particularly deep learning and generative models—have exhibited significant potential in automating diverse cartographic processes,including design,visualization,generation,and application.Despite these advancements,challenges remain concerning AI’s interpretability and transparency,which are fundamental to the usability of maps.展开更多
At present,the AI field is in a golden window period,which provides a historic opportunity for establishing new AI standards.Over the years,sensing enhanced AI technology has constantly developed,promoting the develop...At present,the AI field is in a golden window period,which provides a historic opportunity for establishing new AI standards.Over the years,sensing enhanced AI technology has constantly developed,promoting the development and progress of industries.With the development of new technologies,the demand for standards has become more prominent.The Global Center for Sensing Enhanced AI(GCSEA)is expected to integrate domestic and foreign resources and make joint efforts to promote the development of relevant sensing standards.展开更多
文摘As AI systems scale, the limitations of cloud-based architectures, including latency, bandwidth, and privacy concerns, demand decentralized alternatives. Federated learning (FL) and Edge AI provide a paradigm shift by combining privacy preserving training with efficient, on device computation. This paper introduces a cutting-edge FL-edge integration framework, achieving a 10% to 15% increase in model accuracy and reducing communication costs by 25% in heterogeneous environments. Blockchain based secure aggregation ensures robust and tamper-proof model updates, while exploratory quantum AI techniques enhance computational efficiency. By addressing key challenges such as device variability and non-IID data, this work sets the stage for the next generation of adaptive, privacy-first AI systems, with applications in IoT, healthcare, and autonomous systems.
文摘If you look into the EU AI Act,you will find that the requirements are specified at a very high level.This is because the details are defined elsewhere.That is what standardization is doing.If we look at the world of AI standardization,there are three tiers-the international tier,the European tier and the national tier.There are also AI standardization committees at all three levels.
文摘The rapid development of Artificial Intelligence(AI)has profoundly reshaped numerous scientific and technological domains,including cartography and geographic information science[1].AI techniques—particularly deep learning and generative models—have exhibited significant potential in automating diverse cartographic processes,including design,visualization,generation,and application.Despite these advancements,challenges remain concerning AI’s interpretability and transparency,which are fundamental to the usability of maps.
文摘At present,the AI field is in a golden window period,which provides a historic opportunity for establishing new AI standards.Over the years,sensing enhanced AI technology has constantly developed,promoting the development and progress of industries.With the development of new technologies,the demand for standards has become more prominent.The Global Center for Sensing Enhanced AI(GCSEA)is expected to integrate domestic and foreign resources and make joint efforts to promote the development of relevant sensing standards.
