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Seeing Through Machines: Leveraging AI for Enhanced and Automated Data Storytelling

Swathi Chundru

47-57

Vol 18, Jul-Dec, 2023

Date of Submission: 2023-07-29 Date of Acceptance: 2023-09-27 Date of Publication: 2023-10-21

Abstract

The capacity to convert intricate datasets into engaging stories has grown in significance in the big data era. Data storytelling presents data insights in an understandable, captivating, and useful way by fusing narrative techniques with data visualization. Nevertheless, the laborious manual procedures that are commonly employed in data storytelling are frequently constrained by human cognitive capacities. The transformational potential of artificial intelligence (AI) to improve and automate data storytelling is examined in this research article. Artificial intelligence (AI) can automate the extraction of insights, recognize patterns, and provide narratives that are accurate and insightful by integrating cutting-edge machine learning algorithms with natural language processing. This work explores the approaches used in AI-driven data storytelling, evaluates case examples from a range of sectors, and talks about the difficulties and moral issues raised by this developing discipline. According to the research, AI has the power to completely transform data storytelling and increase its effectiveness, scalability, and impact.

References

  1. M. Floridi, 'AI and its New Winter: From Myths to Realities,' Philosophy & Technology, vol. 33, no. 1, pp. 1-5, 2020.
  2. W. McKinney, 'Data Structures for Statistical Computing in Python,' in Proceedings of the 9th Python in Science Conference (SciPy 2010), Austin, TX, USA, 2010, pp. 51-56.
  3. T. Mitchell, Machine Learning, New York, NY, USA: McGraw-Hill, 1997.
  4. D. M. Blei, A. Y. Ng, and M. I. Jordan, 'Latent Dirichlet Allocation,' Journal of Machine Learning Research, vol. 3, no. 4-5, pp. 993-1022, May 2003.
  5. P. Domingos, 'A Few Useful Things to Know About Machine Learning,' Communications of the ACM, vol. 55, no. 10, pp. 78-87, Oct. 2012.
  6. L. Breiman, 'Random Forests,' Machine Learning, vol. 45, no. 1, pp. 5-32, Oct. 2001.
  7. D. R. Cox and D. V. Hinkley, Theoretical Statistics, London, UK: Chapman & Hall, 1974.
  8. T. Chen and C. Guestrin, 'XGBoost: A Scalable Tree Boosting System,' in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 2016, pp. 785-794.
  9. J. Ramos, 'Using TF-IDF to Determine Word Relevance in Document Queries,' in Proceedings of the First International Conference on Machine Learning, San Francisco, CA, USA, 2003, pp. 133-142.
  10. G. Hinton, S. Osindero, and Y. Teh, 'A Fast Learning Algorithm for Deep Belief Nets,' Neural Computation, vol. 18, no. 7, pp. 1527-1554, Jul. 2006.
  11. C. E. Shannon, 'A Mathematical Theory of Communication,' The Bell System Technical Journal, vol. 27, no. 3, pp. 379-423, Jul. 1948.
  12. J. R. Quinlan, 'Induction of Decision Trees,' Machine Learning, vol. 1, no. 1, pp. 81-106, Mar. 1986.
  13. S. Hochreiter and J. Schmidhuber, 'Long Short-Term Memory,' Neural Computation, vol. 9, no. 8, pp. 1735-1780, Nov. 1997.
  14. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, Cambridge, MA, USA: MIT Press, 2016.
  15. A. K. McCallum and K. Nigam, 'A Comparison of Event Models for Naive Bayes Text Classification,' in Proceedings of the AAAI-98 Workshop on Learning for Text Categorization, Madison, WI, USA, 1998, pp. 41-48.
  16. D. Bahdanau, K. Cho, and Y. Bengio, 'Neural Machine Translation by Jointly Learning to Align and Translate,' in Proceedings of the 3rd International Conference on Learning Representations (ICLR 2015), San Diego, CA, USA, 2015, pp. 1-15.
  17. R. J. Tibshirani, 'Regression Shrinkage and Selection via the Lasso,' Journal of the Royal Statistical Society, vol. 58, no. 1, pp. 267-288, Jan. 1996.
  18. F. Chollet, 'Xception: Deep Learning with Depth wise Separable Convolutions,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), Honolulu, HI, USA, 2017, pp. 1251-1258.
  19. J. Dean and S. Ghemawat, 'MapReduce: Simplified Data Processing on Large Clusters,' Communications of the ACM, vol. 51, no. 1, pp. 107-113, Jan. 2008.
  20. S. Bubeck, 'Convex Optimization: Algorithms and Complexity,' Foundations and Trends in Machine Learning, vol. 8, no. 3-4, pp. 231-357, 2015.
  21. G. E. Hinton, 'Connectionist Learning Procedures,' Artificial Intelligence, vol. 40, no. 1-3, pp. 185-234, Sep. 1989.
  22. A. Graves, A.-R. Mohamed, and G. Hinton, 'Speech Recognition with Deep Recurrent Neural Networks,' in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013), Vancouver, BC, Canada, 2013, pp. 6645-6649.
  23. S. T. Roweis and L. K. Saul, 'Nonlinear Dimensionality Reduction by Locally Linear Embedding,' Science, vol. 290, no. 5500, pp. 2323-2326, Dec. 2000.
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