The Role of Artificial Intelligence in Data Science: Theoretical Perspectives and Emerging Challenges
Abstract
The article explored how Artificial Intelligence (AI) and Data Science have revolutionized the creation and analysis of information by integrating advanced methodologies that overcome traditional barriers in the interpretation of complex data. Fundamental concepts and current technical and ethical challenges were addressed, highlighting the automation of the analytical lifecycle through AutoML, the implementation of explainable models and the management of algorithmic biases. The research also examined the limitations of AI in processing unstructured data and its interaction with emerging technologies such as blockchain and quantum computing. The results highlighted the importance of establishing regulations that guarantee the balance between technological innovation and the protection of human rights in a context of big data and automated decisions. It concludes by emphasizing that the impact of AI transcends the technical, consolidating it as an engine of interdisciplinary progress, promoting both the progress of human knowledge and sustainable practical applications, always under ethical and regulated approaches.
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References
F. Provost y T. Fawcett, Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking, O'Reilly Media, 2013. Available: https://www.academia.edu/38731456/Data_Science_for_Business
S. Russell y P. Norvig, Artificial Intelligence: A Modern Approach, Cuarta ed., Pearson, 2021. Available: https://api.pageplace.de/preview/DT0400.9781292401171_A41586057/preview-9781292401171_A41586057.pdf
I. Goodfellow, Y. Bengio y A. Courville, Deep Learning, 2016. Available: http://alvarestech.com/temp/deep/Deep%20Learning%20by%20Ian%20Goodfellow,%20Yoshua%20Bengio,%20Aaron%20Courville%20(z-lib.org).pdf
P. Domingos, The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World, Basic Books, 2015.
B. Marr, Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results, Wiley, 2016. Available: https://www.academia.edu/40809251/Bernard_marrbig_data_in_practice_how
J. Han, M. Kamber y J. Pei, Data Mining: Concepts and Techniques, Tercera ed., Morgan Kaufmann, 2011. Available: http://sves.org.in/ecap/Resources/_53.pdf
Y. LeCun, Y. Bengio y G. Hinton, «Deep learning,» Nature, p. 436–444, 2015. Available: https://www.researchgate.net/profile/Y-Bengio/publication/277411157_Deep_Learning/links/55e0cdf908ae2fac471ccf0f/Deep-Learning.pdf?_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6InB1YmxpY2F0aW9uIiwicGFnZSI6InB1YmxpY2F0aW9uIn19
Z. C. Lipton, «The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery,» ommunications of the ACM, vol. 61, nº 10, pp. 36-43, 2018. Available: https://dl.acm.org/doi/epdf/10.1145/3281635
M. T. Ribeiro, S. Singh y C. Guestrin, «Why Should I Trust You? Explaining the Predictions of Any Classifier,» Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, p. 1135–1144, 2016. Available: https://dl.acm.org/doi/epdf/10.1145/2939672.2939778
] K. P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012. Available: https://github.com/kerasking/book-1/blob/master/ML%20Machine%20Learning-A%20Probabilistic%20Perspective.pdf
L. Floridi, «Big Data and Their Epistemological Challenge,» Philosophy & Technology, p. 435–437, 2012. Available: https://link.springer.com/article/10.1007/s13347-012-0093-4
X. He, K. Zhao y X. Chu, «AutoML: A survey of the state-of-the-art,» Knowledge-Based Systems, 212, 106622, 2021. Available: https://www.researchgate.net/publication/334963534_AutoML_A_Survey_of_the_State-of-the-Art
M. A. Zöller y M. F. Huber, «Benchmark and survey of automated machine learning frameworks,» Journal of Artificial Intelligence Research, vol. 70, p. 409–472, 2021. Available: https://www.jair.org/index.php/jair/article/download/11854/26651/25924
F. Hutter, L. Kotthoff y J. Vanschoren, Automated Machine Learning: Methods, Systems, Challenges, Springer, 2019. Available: https://www.automl.org/wp-content/uploads/2019/05/AutoML_Book.pdf
C. O'Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, New York: Crown, 2016. Available: https://edisciplinas.usp.br/pluginfile.php/4605464/mod_resource/content/1/%28FFLCH%29%20LIVRO%20Weapons%20of%20Math%20Destruction%20-%20Cathy%20ONeal.pdf
S. Barocas, M. Hardt y A. Narayanan, Fairness and Machine Learning: Limitations and Opportunities, MIT Press, 2023. Available: https://fairmlbook.org/pdf/fairmlbook.pdf
F. Doshi-Velez y B. Kim, «Towards a rigorous science of interpretable machine learning,» arXiv preprint arXiv:1702.08608, 2017. Available: https://arxiv.org/pdf/1702.08608
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan y D. Amodei, «Language models are few-shot learners,» Advances in Neural Information Processing Systems, vol. 33, p. 1877–1901, 2020. Available: https://arxiv.org/pdf/2005.14165
J. Devlin, M.-W. Chang, K. Lee y K. Toutanova, «BERT: Pre-training of deep bidirectional transformers for language understanding,» Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, p. 4171–4186, 2019. Available: https://eva.fing.edu.uy/pluginfile.php/524749/mod_folder/content/0/BERT%20Pre-training%20of%20Deep%20Bidirectional%20Transformers%20for%20Language%20Understanding.pdf
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow y R. Fergus, Intriguing properties of neural networks, Proceedings of the International Conference on Learning Representations, 2014. Available: https://arxiv.org/pdf/1312.6199
L. Zhou, S. Pan, J. Wang y A. V. Vasilakos, «Machine Learning on Big Data: Opportunities and Challenges,» Elsevier, vol. 237, p. 350–361, 2017. Available: https://pdf.sciencedirectassets.com/271597/1-s2.0-S0925231217X00106/1-s2.0-S0925231217300577/am.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEF0aCXVzLWVhc3QtMSJIMEYCIQCSuy%2FRE0BaSB%2Fsshx6fCqcpqDQnOl%2FvFhGWvSVWVV7XQIhAMdBnd9GjEqbH3i10pt1AAQLZjQDCridzzI3Cw1B
J. Dean, D. Patterson y Y. Cliff, «A New Golden Age in Computer Architecture: Empowering the Machine-Learning Revolution,» IEEE Micro, vol. 38, nº 2, pp. 21-29, 2018. Available: https://ieeexplore.ieee.org/abstract/document/8259424/
M. Chen, S. Mao y Y. Liu, «Big data: A survey,» Mobile Networks and Applications, vol. 19, nº 2, p. 171–209, 2014. Available: https://www.cs.unibo.it/~montesi/CBD/Articoli/SurveyBigData.pdf
I. Rahwan, M. Cebrian, N. Obradovich, J. Bongard, J. F. Bonnefon, C. Breazeal, J. W. Crandall, N. A. Christakis, L. D. Couzin, M. O. Jackson, N. R. Jennings, A. Kamar y M. Wellman, «Machine behaviour,» Nature, vol. 568, nº 7753, p. 477–486, 2019. Available: https://www.nature.com/articles/s41586-019-1138-y
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser y I. Polosukhin, «Attention is all you need,» Advances in Neural Information Processing Systems, p. 5998–6008, 2017. Available: https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
S. Nakamoto, Bitcoin: A peer-to-peer electronic cash system, 2008. Available: https://bitcoin.org/bitcoin.pdf
F. Arute, K. Arya, R. Babbush, D. Bacon, J. C. Bardin, R. Barends, R. Biswas, S. Boixo, F. G. S. L. Brandao, D. A. Buell, B. Burkett, Y. Chen, Z. Chen, B. Chiaro, R. Collins, W. Courtney y J. M. Martinis, «Quantum supremacy using a programmable superconducting processor,» Nature, vol. 574, nº 7779, p. 505–510, 2019. Available: https://www.nature.com/articles/s41586-019-1666-5
J. Preskill, «Quantum computing in the NISQ era and beyond,» Quantum, vol. 2, p. 79, 2018. Available: https://quantum-journal.org/papers/q-2018-08-06-79/pdf/
European Commission, «Proposal for a regulation laying down harmonized rules on artificial intelligence,» COM/2021/206 final, 2021. Available: https://eur-lex.europa.eu/resource.html?uri=cellar:e0649735-a372-11eb-9585-01aa75ed71a1.0001.02/DOC_1&format=PDF
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