Securing tomorrow: a comprehensive survey on the synergy of Artificial Intelligence and information security

AI and Ethics, Год журнала: 2024, Номер unknown

Опубликована: Июль 30, 2024

Abstract This survey paper explores the transformative role of Artificial Intelligence (AI) in information security. Traditional methods, especially rule-based approaches, faced significant challenges protecting sensitive data from ever-changing cyber threats, particularly with rapid increase volume. study thoroughly evaluates AI’s application security, discussing its strengths and weaknesses. It provides a detailed review impact on examining various AI algorithms used this field, such as supervised, unsupervised, reinforcement learning, highlighting their respective limitations. The identifies key areas for future research focusing improving algorithms, strengthening addressing ethical issues, exploring safety security-related concerns. emphasizes security risks, including vulnerability to adversarial attacks, aims enhance robustness reliability systems by proposing solutions potential threats. findings aim benefit cybersecurity professionals researchers offering insights into intricate relationship between AI, emerging technologies.

Язык: Английский

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Large language models for life cycle assessments: Opportunities, challenges, and risks

Journal of Cleaner Production, Год журнала: 2024, Номер 466, С. 142824 - 142824

Опубликована: Июнь 10, 2024

Язык: Английский

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CECA: An Intelligent Large-Language-Model-Enabled Method for Accounting Embodied Carbon in Buildings

Building and Environment, Год журнала: 2025, Номер unknown, С. 112694 - 112694

Опубликована: Фев. 1, 2025

Язык: Английский

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Ethical Guidelines for the Application of Generative AI in German Journalism

Deleted Journal, Год журнала: 2025, Номер 4(1)

Опубликована: Янв. 16, 2025

Abstract Generative Artificial Intelligence (genAI) holds immense potential in revolutionizing journalism and media production processes. By harnessing genAI, journalists can streamline various tasks, including content creation, curation, dissemination. Through already automate the generation of diverse news articles, ranging from sports updates financial reports to weather forecasts. However, this raises ethical questions high relevance for organizations societies especially when genAI is used more sensitive topics at larger scale. To not jeopardize trustworthiness journalistic organizations, it important that use guided by moral principles. We therefore conducted 18 interviews with researchers practitioners expertise AI-based technologies, journalism, ethics a German perspective order identify guidelines organizations. derived requirements introduction actionable which explain how decision makers should address principles AI life cycle, contribute products.

Язык: Английский

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Engineering a Sustainable Future: Harnessing Automation, Robotics, and Artificial Intelligence with Self-Driving Laboratories

ACS Sustainable Chemistry & Engineering, Год журнала: 2024, Номер 12(34), С. 12695 - 12707

Опубликована: Авг. 6, 2024

The accelerating depletion of natural resources undoubtedly demands a radical reevaluation research practices addressing the escalating climate crisis. From traditional approaches to modern-day advancements, integration automation and artificial intelligence (AI)-guided decision-making has emerged as transformative route in shaping new methodologies. Harnessing robotics high-throughput alongside intelligent experimental design, self-driving laboratories (SDLs) offer an innovative solution expedite chemical/materials timelines while significantly reducing carbon footprint scientific endeavors, which could be utilized not only generate green materials but also make process itself more sustainable. In this Perspective, we examine potential SDLs driving sustainability forward through case studies discovery optimization, thereby paving way for greener efficient future. While hold immense promise, discuss challenges that persist their development deployment, necessitating holistic approach both design implementation.

Язык: Английский

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An assessment framework of higher-order thinking skills based on fine-tuned large language models

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 126531 - 126531

Опубликована: Янв. 1, 2025

Язык: Английский

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Climate Sustainability through AI-Crypto Synergies and Energy Transition in the Digital Landscape to Cut 0.7 GtCO₂e by 2030

Environmental Science & Technology, Год журнала: 2025, Номер unknown

Опубликована: Фев. 19, 2025

The rapid expansion of artificial intelligence (AI)-enabled systems and cryptocurrency mining poses significant challenges to climate sustainability due energy-intensive operations relying on fossil-powered grids. This work investigates the strategic coupling AI data centers through shared energy infrastructure including colocated renewable power installations, battery storage, green hydrogen infrastructure, carbon offsetting measures achieve cost-effective climate-neutral operations. Employing a novel modeling framework, it explores synergistic AI-crypto with detailed scenario design along an optimization framework assess decarbonization potential economic implications, enabling transformative shift in digital landscape. results indicate that synergizing while achieving net-zero targets can avoid up 0.7 Gt CO2-equiv 2030. Moreover, reaching these strategies globally requires 90.7 GW solar 119.3 wind capacity. findings advocate for robust policy facilitate credit schemes tailored sector, incentives efficiency improvements, international collaborations bridge disparities. Future research should focus refining interventions across different geopolitical contexts enhance global applicability.

Язык: Английский

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Large Language Models (LLMs) for Smart Manufacturing and Industry X.0

Springer series in advanced manufacturing, Год журнала: 2025, Номер unknown, С. 97 - 119

Опубликована: Янв. 1, 2025

Язык: Английский

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Multi-Agent Large Language Model Frameworks: Unlocking New Possibilities for Optimizing Wastewater Treatment Operation

Environmental Research, Год журнала: 2025, Номер unknown, С. 121401 - 121401

Опубликована: Март 1, 2025

Язык: Английский

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To Improve Literacy, Improve Equality in Education, Not Large Language Models

Cognitive Science, Год журнала: 2025, Номер 49(4)

Опубликована: Апрель 1, 2025

Abstract Huettig and Christiansen in an earlier issue argue that large language models (LLMs) are beneficial to address declining cognitive skills, such as literacy, through combating imbalances educational equity. However, we warn this technosolutionism may be the wrong frame. LLMs labor intensive, economically infeasible, pollute environment, these properties outweigh any proposed benefits. For example, poor quality air directly harms human cognition, thus has compounding effects on educators' pupils' ability teach learn. We urge extreme caution facilitating use of LLMs, which like much modern academia run private technology sector infrastructure, classrooms lest further normalize: pupils losing their right privacy security, reducing contact between learner educator, deskilling teachers, polluting environment. Cognitive scientists instead can learn from past mistakes with petrochemical tobacco industries consider cognition LLMs.

Язык: Английский

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