SUMMARIES OF TOP NEWS STORIES CHINA Safeguarding Gaokao Fairness This year’s gaokao,China’s national college entrance exam,began on 7 June,with a record 13.35 million students taking part.As one of the country’s mo...SUMMARIES OF TOP NEWS STORIES CHINA Safeguarding Gaokao Fairness This year’s gaokao,China’s national college entrance exam,began on 7 June,with a record 13.35 million students taking part.As one of the country’s most important annual events,the 2025 gaokao has seen extensive nation-wide efforts to ensure fairness,safety,and support for all candidates.Authorities have implemented a range of security and logistical measures to safeguard exam integrity.AI-powered monitoring systems have been rolled out in provinces like Jiangxi,Hubei,and Guangdong,enabling real-time behaviour analysis and early warnings without human intervention.These tools reduce pressure on staff and strengthen fairness.展开更多
The rapid evolution and expanding scale of AI(artificial intelligence)technologies exert unprecedented energy demands on global electrical grids.Powering computationally intensive tasks such as large-scale AI model tr...The rapid evolution and expanding scale of AI(artificial intelligence)technologies exert unprecedented energy demands on global electrical grids.Powering computationally intensive tasks such as large-scale AI model training and widespread real-time inference necessitates substantial electricity consumption,presenting a significant challenge to conventional power infrastructure.This paper examines the critical need for a fundamental shift towards smart energy grids in response to AI’s growing energy footprint.It delves into the symbiotic relationship wherein AI acts as a significant energy consumer while offering the intelligence required for dynamic load management,efficient integration of renewable energy sources,and optimized grid operations.We posit that advanced smart grids are indispensable for facilitating AI’s sustainable growth,underscoring this synergy as a pivotal advancement toward a resilient energy future.展开更多
Artificial Intelligence(AI)constitutes a rapidly evolving set of technologies that offer significant economic,environmental,and societal benefits.However,the application of AI systems may also pose considerable risks ...Artificial Intelligence(AI)constitutes a rapidly evolving set of technologies that offer significant economic,environmental,and societal benefits.However,the application of AI systems may also pose considerable risks and inflict harm—whether material or immaterial,including physical,psychological,societal,or economic harm—to public interests and fundamental rights protected under Union law.展开更多
This paper proposes a novel approach to use artificial intelligence(Al),particularly large language models(LLMs)and other foundation models(FMs)in an educational environment.It emphasizes the integration of teams of t...This paper proposes a novel approach to use artificial intelligence(Al),particularly large language models(LLMs)and other foundation models(FMs)in an educational environment.It emphasizes the integration of teams of teachable and self-learning LLMs agents that use neuro-symbolic cognitive architecture(NSCA)to provide dynamic personalized support to learners and educators within self-improving adaptive instructional systems(SIAIS).These systems host these agents and support dynamic sessions of engagement workflow.We have developed the never ending open learning adaptive framework(NEOLAF),an LLM-based neuro-symbolic architecture for self-learning AI agents,and the open learning adaptive framework(OLAF),the underlying platform to host the agents,manage agent sessions,and support agent workflows and integration.The NEOLAF and OLAF serve as concrete examples to illustrate the advanced AI implementation approach.We also discuss our proof of concept testing of the NEOLAF agent to develop math problem-solving capabilities and the evaluation test for deployed interactive agent in the learning environment.展开更多
Adversarial examples revealed the weakness of machine learning techniques in terms of robustness,which moreover inspired adversaries to make use of the weakness to attack systems employing machine learning.Existing re...Adversarial examples revealed the weakness of machine learning techniques in terms of robustness,which moreover inspired adversaries to make use of the weakness to attack systems employing machine learning.Existing researches covered the methodologies of adversarial example generation,the root reason of the existence of adversarial examples,and some defense schemes.However practical attack against real world systems did not appear until recent,mainly because of the difficulty in injecting a artificially generated example into the model behind the hosting system without breaking the integrity.Recent case study works against face recognition systems and road sign recognition systems finally abridged the gap between theoretical adversarial example generation methodologies and practical attack schemes against real systems.To guide future research in defending adversarial examples in the real world,we formalize the threat model for practical attacks with adversarial examples,and also analyze the restrictions and key procedures for launching real world adversarial example attacks.展开更多
文摘SUMMARIES OF TOP NEWS STORIES CHINA Safeguarding Gaokao Fairness This year’s gaokao,China’s national college entrance exam,began on 7 June,with a record 13.35 million students taking part.As one of the country’s most important annual events,the 2025 gaokao has seen extensive nation-wide efforts to ensure fairness,safety,and support for all candidates.Authorities have implemented a range of security and logistical measures to safeguard exam integrity.AI-powered monitoring systems have been rolled out in provinces like Jiangxi,Hubei,and Guangdong,enabling real-time behaviour analysis and early warnings without human intervention.These tools reduce pressure on staff and strengthen fairness.
文摘The rapid evolution and expanding scale of AI(artificial intelligence)technologies exert unprecedented energy demands on global electrical grids.Powering computationally intensive tasks such as large-scale AI model training and widespread real-time inference necessitates substantial electricity consumption,presenting a significant challenge to conventional power infrastructure.This paper examines the critical need for a fundamental shift towards smart energy grids in response to AI’s growing energy footprint.It delves into the symbiotic relationship wherein AI acts as a significant energy consumer while offering the intelligence required for dynamic load management,efficient integration of renewable energy sources,and optimized grid operations.We posit that advanced smart grids are indispensable for facilitating AI’s sustainable growth,underscoring this synergy as a pivotal advancement toward a resilient energy future.
文摘Artificial Intelligence(AI)constitutes a rapidly evolving set of technologies that offer significant economic,environmental,and societal benefits.However,the application of AI systems may also pose considerable risks and inflict harm—whether material or immaterial,including physical,psychological,societal,or economic harm—to public interests and fundamental rights protected under Union law.
文摘This paper proposes a novel approach to use artificial intelligence(Al),particularly large language models(LLMs)and other foundation models(FMs)in an educational environment.It emphasizes the integration of teams of teachable and self-learning LLMs agents that use neuro-symbolic cognitive architecture(NSCA)to provide dynamic personalized support to learners and educators within self-improving adaptive instructional systems(SIAIS).These systems host these agents and support dynamic sessions of engagement workflow.We have developed the never ending open learning adaptive framework(NEOLAF),an LLM-based neuro-symbolic architecture for self-learning AI agents,and the open learning adaptive framework(OLAF),the underlying platform to host the agents,manage agent sessions,and support agent workflows and integration.The NEOLAF and OLAF serve as concrete examples to illustrate the advanced AI implementation approach.We also discuss our proof of concept testing of the NEOLAF agent to develop math problem-solving capabilities and the evaluation test for deployed interactive agent in the learning environment.
基金partially sponsored by Shanghai Sailing Program No.18YF1402200。
文摘Adversarial examples revealed the weakness of machine learning techniques in terms of robustness,which moreover inspired adversaries to make use of the weakness to attack systems employing machine learning.Existing researches covered the methodologies of adversarial example generation,the root reason of the existence of adversarial examples,and some defense schemes.However practical attack against real world systems did not appear until recent,mainly because of the difficulty in injecting a artificially generated example into the model behind the hosting system without breaking the integrity.Recent case study works against face recognition systems and road sign recognition systems finally abridged the gap between theoretical adversarial example generation methodologies and practical attack schemes against real systems.To guide future research in defending adversarial examples in the real world,we formalize the threat model for practical attacks with adversarial examples,and also analyze the restrictions and key procedures for launching real world adversarial example attacks.
