Information Processing & Management, Год журнала: 2024, Номер 62(3), С. 104054 - 104054
Опубликована: Дек. 30, 2024
Язык: Английский
Information Sciences, Год журнала: 2024, Номер 663, С. 120282 - 120282
Опубликована: Фев. 5, 2024
Machine learning has proven to be a powerful tool for knowledge extraction from large data sets across different domains. Data quality and results interpretability are essential when applying machine inform decision-making processes. This is especially true clustering methods, which frequently employed extracting sets, due their unsupervised nature. Although there significant recent developments in explainable artificial intelligence (XAI) applied problems, they focus primarily on cluster often overlook challenges. Moreover, these typically designed use specific algorithms, limiting adaptability incorporate alternative techniques. We propose novel comprehensive four-step sequential framework analysis high-dimensional mixed-type address limitations. The encompasses preprocessing, dimensionality reduction, clustering, classification ensure robust results. proposed methodology also been implemented an open-source Python package called Clust-learn, accessible customizable researchers practitioners. validated by case study focusing large-scale assessments education, effectively illustrating the strength usefulness of synthesizing complex real-world data.
Язык: Английский
Процитировано
9
Cognitive Computation, Год журнала: 2020, Номер 12(5), С. 897 - 939
Опубликована: Июль 5, 2020
Язык: Английский
Процитировано
41
Scientific Reports, Год журнала: 2025, Номер 15(1)
Опубликована: Март 11, 2025
This study examines the imperative to align artificial general intelligence (AGI) development with societal, technological, ethical, and brain-inspired pathways ensure its responsible integration into human systems. Using PRISMA framework BERTopic modeling, it identifies five key shaping AGI's trajectory: (1) societal integration, addressing broader impacts, public adoption, policy considerations; (2) technological advancement, exploring real-world applications, implementation challenges, scalability; (3) explainability, enhancing transparency, trust, interpretability in AGI decision-making; (4) cognitive ethical considerations, linking evolving architectures frameworks, accountability, consequences; (5) systems, leveraging neural models improve learning efficiency, adaptability, reasoning capabilities. makes a unique contribution by systematically uncovering underexplored themes, proposing conceptual that connects AI advancements practical multifaceted technical, challenges of development. The findings call for interdisciplinary collaboration bridge critical gaps governance, alignment while strategies equitable access, workforce adaptation, sustainable integration. Additionally, highlights emerging research frontiers, such as AGI-consciousness interfaces collective offering new integrate human-centered applications. By synthesizing insights across disciplines, this provides comprehensive roadmap guiding ways balance innovation responsibilities, advancing progress well-being.
Язык: Английский
Процитировано
0
Machine Learning with Applications, Год журнала: 2024, Номер unknown, С. 100591 - 100591
Опубликована: Окт. 1, 2024
Язык: Английский
Процитировано
4
Advances in computational intelligence and robotics book series, Год журнала: 2025, Номер unknown, С. 271 - 290
Опубликована: Фев. 21, 2025
Blockchain technology and Artificial Intelligence (AI) play variety of roles in sustainable economic growth tackling a range social, economic, environmental issues. AI boosts labor productivity propels by automating processes increasing across industries. Transparency transactions is guaranteed blockchain's decentralized unchangeable structure, which essential for business processes. Since they foster social inclusion, prosperity, sustainability, blockchain are line with the Sustainable Development Goals. Ultimately, formidable tools that advance growth. Their unique set abilities provides ground-breaking ways to improve performance, transparency wide Keeping view above, this chapter analyzes integral role enhancing development. It explores legal framework concerning usage several regions countries.
Язык: Английский
Процитировано
0
2022 ACM Conference on Fairness, Accountability, and Transparency, Год журнала: 2024, Номер 7, С. 776 - 796
Опубликована: Июнь 3, 2024
Responding to the rapid roll-out and large-scale commercialization of foundation models, large language generative AI, an emerging body work is shedding light on myriad impacts these technologies are having across society. Such research expansive, ranging from production discriminatory, fake toxic outputs, privacy copyright violations, unjust extraction labor natural resources. The same has not been case in some most prominent AI governance initiatives global north like UK's Safety Summit G7's Hiroshima process, which have influenced much international dialogue around governance. Despite wealth cautionary tales evidence algorithmic harm, there ongoing over-emphasis within discourse technical matters safety catastrophic or existential risks. This narrowed focus tended draw attention away very pressing social ethical challenges posed by current brute-force industrialization applications. To address such a visibility gap between real-world consequences speculative risks, this paper offers critical framework account for social, political, environmental dimensions models AI. Drawing review literature harms risks foundations insights data studies, science technology justice scholarship, we identify 14 categories map them according their individual, biospheric impacts. We argue that novel typology integrative perspective urgent negative downstream conclude with recommendations how could be used inform normative interventions advance responsible
Язык: Английский
Процитировано
3
Journal of Clinical Neuroscience, Год журнала: 2024, Номер 125, С. 104 - 105
Опубликована: Май 17, 2024
Язык: Английский
Процитировано
1
Harvard data science review, Год журнала: 2024, Номер Special Issue 5
Опубликована: Май 31, 2024
Crisis 5 stakeholders from across industry, academia, government, and civil society, around the globe, had made concerted efforts to develop standards, policies, governance mechanisms ensure ethical, responsible, equitable production use of AI systems.However, as we then show, despite these ostensibly supportive activities background conditions, several primary drivers future shock converged produce an international policy crisis in wake dawning GenAI era.Such a crisis, argue, was marked by disconnect between strengthening thrust public concerns about hazards posed hasty industrial scaling absence effectual regulatory needed interventions address such hazards.In painting broad-stroked picture this underscore two sets contributing factors.First, there have been factors that demonstrated various vital aspects capability execution-and thus key preconditions for readiness resilience managing technological transformation.These include prevalent enforcement gaps existing digital-and data-related laws (e.g., intellectual property data protection statutes), lack capacity, democratic deficits standards trustworthy AI, widespread evasionary tactics ethic washing state-enabled deregulation.Second, significantly contributed presence new scale order systemic-, societal-, biospheric-level risks harms.Chief among were closely connected dynamics unprecedented centralization emerged both by-products revolution.We focus, particular, on model scaling.Whereas data, size, compute linked emergence serious intrinsic deriving unfathomability training opacity complexity, emergent capabilities, exponentially expanding costs, rapid industrialization FMs systems meant onset systemic spanned social, political, economic, cultural, natural ecosystems which embedded.The brute-force commercialization ushered age exposure increasing numbers impacted people communities at large susceptible harms issuing possibilities misuse, abuse, cascading system-level effects.Alongside scaling, patterns economic geopolitical only further intensified conditions shock.The steering momentum lay largely hands few tech corporations, essentially controlled compute, skills knowledge infrastructures required systems.This small number corporate actors labs disproportionate influence direction pace revolution, pursuing market-oriented values led acceleration.This also that, if left unchecked, concentration techno-scientific market power could lead consolidation.Moreover, impetus industry consolidation
Язык: Английский
Процитировано
1
Neurocomputing, Год журнала: 2024, Номер 599, С. 128073 - 128073
Опубликована: Июнь 15, 2024
In the rapidly evolving domain of machine learning, ability to adapt unforeseen circumstances and novel data types is paramount importance. The deployment Artificial Intelligence progressively aimed at more realistic open scenarios where data, tasks, conditions are variable not fully predetermined, therefore a closed set assumption cannot be hold. such environments, learning asked autonomous, continuous, adaptive, requiring effective management uncertainty unknown fulfill expectations. response, there vigorous effort develop new generation models, which characterized by enhanced autonomy broad capacity generalize, enabling them perform effectively across wide range tasks. field in environments poses many challenges also brings together different paradigms, some traditional but others emerging, overlapping confusion between makes it difficult distinguish or give necessary relevance. This work delves into frontiers methodologies that thrive these identifying common practices, limitations, connections paradigms Open-Ended Learning, Open-World Open Set Recognition, other related areas as Continual Out-of-Distribution detection, Novelty Detection, Active Learning. We seek easy understanding fields their roots, uncover problems suggest several research directions may motivate articulate future efforts towards robust autonomous systems.
Язык: Английский
Процитировано
1
Journal of Global Information Management, Год журнала: 2024, Номер 32(1), С. 1 - 27
Опубликована: Дек. 28, 2024
As artificial intelligence rapidly advances, addressing the interplay of technical, ethical, and risk factors in optimizing digital market decision-making through AI platforms has become increasingly prominent. However, impact these on performance, particularly investment value, remains underexplored. The study, based 412 validated responses from service industry professionals gathered a carefully designed questionnaire, aims to predict relationship among their influence performance. It also explores how cognitive engagement mediates between financial metrics. Key findings:(1) optimizes guides investments; (2) engagement, especially services sector, is essential maximize study provides valuable insights into AI's role shaping dynamics within sector relevant governance recommendations for policymakers.
Язык: Английский
Процитировано
1