Smart Machines, Smarter Outcomes the Rise of Self-Learning Systems
DOI:
https://doi.org/10.15662/IJARCST.2023.0605009Keywords:
Self-Learning Systems, Fusion, Meta-Learning, Reinforcement Learning, Human-AI CollaborationAbstract
The evolution of smart machines into fully autonomous machine learning systems that are able to selflearn to improve performance. These systems use state-of-the-art artificial intelligence approaches, such as reinforcement learning, meta-learning, or sensor fusion. These ecosystems create unique opportunities to drive transformational impact across industries through optimally enhancing resilience, efficiency and intelligent decisionmaking capability. As per the findings, it is evident that we need an ecosystem-wide approach to design and enact frameworks that encompass authentic human systems-AI collaborations and governance mechanisms that are ethical, this will minimise risk around emerging technologies and maximise society's potential benefits. In summary, selflearning AI is shaping up to be one of the tent pole elements of the next technological and industrial revolution that can lead to better and more intelligent, safer, and sustainable world-wide outcomes for humanity. As indicated in the research, we will need to take a proactive, multidisciplinary approach to leveraging the evolving nature of self-learning AI systems so we can know the implications and prepare our economic and social systems accordingly.
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