Artificial Intelligence and the Future of Human Knowledge Creation

  • Authors

    • Anatoly Kitov Colonel & Computer Scientist, Soviet Academy of Sciences, Russia. Author

    DOI:

    https://doi.org/10.67228/3071561X/IJIRHT-2024PII6F4K

    Published 10-05-2024

  • Artificial Intelligence, Knowledge Creation, Machine Learning, Generative AI, Human-AI Collaboration, Deep Learning, Innovation, Intelligent Systems

    Issue

    Vol. 7 No. 2 (2024)

    Section

    Articles

    How to Cite

    [1]
    A. Kitov, “Artificial Intelligence and the Future of Human Knowledge Creation”, IJIRHT, vol. 7, no. 2, pp. 01–16, Oct. 2024, doi: 10.67228/3071561X/IJIRHT-2024PII6F4K.

  • Abstract

    Artificial Intelligence (AI) is transforming knowledge creation by enabling faster data processing, pattern recognition, content generation, and decision support. Technologies such as machine learning, deep learning, natural language processing, and generative AI enhance human creativity, research, and innovation across fields like education, healthcare, business, and science. AI improves the speed, scalability, and efficiency of knowledge generation, but challenges such as bias, misinformation, lack of transparency, and ethical concerns remain. The future of knowledge creation lies in human–AI collaboration, where AI complements human intelligence while ethical governance and human oversight ensure trustworthy and responsible outcomes.

  • References

    [1] John McCarthy, “What is Artificial Intelligence?,” Stanford University, 2007.

    [2] Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 4th ed. Pearson, 2021.

    [3] Yann LeCun, Yoshua Bengio, and Geoffrey Hinton, “Deep Learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.

    [4] Tom M. Mitchell, Machine Learning. McGraw-Hill, 1997.

    [5] Herbert A. Simon, The Sciences of the Artificial, 3rd ed. MIT Press, 1996.

    [6] Erik Brynjolfsson and Andrew McAfee, The Second Machine Age. W. W. Norton & Company, 2014.

    [7] Daniel Kahneman, Thinking, Fast and Slow. Farrar, Straus and Giroux, 2011.

    [8] Ashish Vaswani et al., “Attention Is All You Need,” in Advances in Neural Information Processing Systems (NeurIPS), 2017.

    [9] Jacob Devlin et al., “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” in NAACL-HLT, 2019.

    [10] Ian Goodfellow et al., “Generative Adversarial Nets,” in NeurIPS, 2014.

    [11] Tom B. Brown et al., “Language Models are Few-Shot Learners,” in NeurIPS, 2020.

    [12] World Economic Forum, “The Future of Jobs Report,” 2023.

    [13] UNESCO, “Recommendation on the Ethics of Artificial Intelligence,” 2021.

    [14] OECD, “OECD Principles on Artificial Intelligence,” 2019.

    [15] Luciano Floridi and Josh Cowls, “A Unified Framework of Five Principles for AI in Society,” Harvard Data Science Review, 2019.

    [16] Gajula, S. (2023). A review of anomaly identification in finance frauds using machine learning system. International Journal of Current Engineering and Technology, 13(6), 568–575. https://ijcet.evegenis.org/index.php/ijcet/article/view/820

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