Artificial General Intelligence (AGI): A Comprehensive Review
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https://doi.org/10.56450/JEFI.2024.v2i03.004Keywords:
Artificial General Intelligence (AGI), Machine Learning, Cognitive Architectures, Neuroscience-Inspired AI, Ethics in AI, Deep Learning, Autonomy, Reasoning, Artificial Intelligence (AI), Ethical FrameworksIssue
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Artificial General Intelligence (AGI) represents a significant leap in the field of artificial intelligence, defined by its ability to perform any intellectual task that a human can. Unlike narrow AI, which is task-specific, AGI is characterized by versatility, adaptability, autonomy, and reasoning capabilities. This comprehensive review explores the defining features of AGI, including its cognitive flexibility and capacity for autonomous decision-making and problem-solving. The current state of AGI research is examined, highlighting key areas such as cognitive architectures, neuroscience-inspired AI, and machine learning and deep learning advancements. The review also addresses the technical challenges and ethical considerations associated with AGI development, including potential impacts on employment, privacy, and security, as well as the necessity for robust safety and control measures. Looking ahead, the potential benefits of AGI in various domains, such as healthcare, climate change, education, and economic growth, are discussed. Finally, the importance of establishing ethical frameworks and governance structures to guide AGI development and usage is emphasized. By addressing these challenges and fostering collaboration among scientists, policymakers, and society, AGI can be developed and deployed to benefit humanity as a whole.Abstract
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Author Biography
Krupal Joshi, All India Institute of Medical Science Rajkot, Gujrat, Gujarat
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Bubeck, S., et al. (2023). Sparks of Artificial General Intelligence: Early Experiments with GPT-4. arXiv preprint arXiv:2303.12712. Brown, T., et al. Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems, 2020;33:1877-1901. Goertzel, B., et al. (2014). OpenCog: A Software Framework for Integrative Artificial General Intelligence. Frontiers in Robotics and AI, 1, 1-13. Markram, H. (2012). The Human Brain Project. Scientific American, 306(6), 50-55. Hassabis, D., et al. (2017). Neuroscience-inspired Artificial Intelligence. Neuron, 95(2), 245-258. LeCun, Y., et al. (2015). Deep Learning. Nature, 521(7553), 436-444. Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. Russell, S. (2019). Human Compatible: Artificial Intelligence and the Problem of Control. Viking
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