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Unlocking the Power of Lifelong Learning: Advancing AI Capabilities in Dynamic Environments
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Author(s): Aanal Sanjivbhai Raval (Gujarat Technological University, India), Arpita Pareshkumar Maheriya (Gujarat Technological University, India), Shailesh Panchal (Gujarat Technological University, India)and Komal Borisagar (Gujarat Technological University, India)
Copyright: 2027
Pages: 25
Source title:
Encyclopedia of Modern Artificial Intelligence
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Founding Editor-in-Chief, Information Resources Management Journal (IRMJ), USA)
DOI: 10.4018/408153
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Abstract
In an era marked by the rapid growth of big data and high-performance computing, machine learning (ML) has seen unprecedented expansion. This increase in data availability and advancements in computational power have opened new avenues for solving complex tasks. However, a significant challenge remains: datasets often exist in isolation, limiting the potential insights that can be derived from integrating multiple data sources. Moreover, ML algorithms frequently perform asymmetrically with varying data volumes. To overcome these challenges, the AI community is developing lifelong machine learning (LML), a paradigm that mirrors human incremental learning. LML systems continuously acquire knowledge and adapt to different tasks, promising enhanced flexibility, adaptability, and intelligence. This article provides a comprehensive overview of LML techniques and methodologies, highlighting the limitations of traditional static learning approaches.
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