A Perspective on the Use of Sanskrit Language and Literature in Developing AI and GenAI Systems
DOI:
https://doi.org/10.70112/ajist-2025.15.1.4320Keywords:
Sanskrit, Generative AI (GenAI), Natural Language Processing (NLP), Panini’s Ashtadhyayi, Computational linguisticsAbstract
Artificial intelligence (AI) and generative AI (GenAI) have transformed various industries by enabling machines to understand, interpret, and generate human languages with remarkable precision, a capability popularly known as natural language processing (NLP). While dominant languages such as English and Mandarin have traditionally played a significant role in AI and GenAI model training, there is growing interest in exploring the potential of Sanskrit for AI and GenAI system development. Using its highly structured grammar and rich semantic framework, this article explores how Sanskrit offers unique advantages to AI and GenAI systems. Sanskrit’s deterministic and precise grammatical rules, as codified in Maharishi Panini’s Ashtadhyayi, present an opportunity to reduce ambiguity and enhance the computational efficiency of NLP models. The language’s inflected morphology allows for more compact and flexible expressions, which may improve AI’s ability to handle complex word relationships. Sanskrit’s cultural and philosophical significance also enables AI to engage with ancient wisdom and interdisciplinary research. However, challenges such as the limited availability of modern Sanskrit corpora, the lack of native speakers, and the computational complexity of its grammatical rules must be addressed to realize its full potential. Despite these challenges, incorporating Sanskrit into AI and GenAI systems could lead to innovations in linguistic research, philosophical AI, and computational logic.
References
[1] Alexandris, C. (2024). GenAI and socially responsible AI in natural language processing applications: A linguistic perspective. Proceedings of the AAAI Symposium Series.
[2] Bathulapalli, C., Desai, D., & Kanhere, M. (2016). Use of Sanskrit for natural language processing. International Journal of Sanskrit Research, 2(6), 78–81.
[3] Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency.
[4] Bharati, A., Chaitanya, V., Sangal, R., & Ramakrishnamacharyulu, K. (1995). Natural language processing: A Paninian perspective.
[5] Bitbol, M., & Darrigol, O. (1992). Erwin Schrödinger. Philosophy and the Birth of Quantum Mechanics.
[6] Briggs, R. (1985). Knowledge representation in Sanskrit and artificial intelligence. AI Magazine, 6(1), 32.
[7] Brown, T. B. (2020). Language models are few-shot learners. arXiv preprint, arXiv:2005.14165.
[8] Cambria, E., & White, B. (2014). Jumping NLP curves: A review of natural language processing research. IEEE Computational Intelligence Magazine, 9(2), 48–57.
[9] Casten Carlberg, C. J., & Jerhamre, E. (2021). Artificial intelligence in agriculture: Opportunities and challenges.
[10] Chowdhary, K., & Chowdhary, K. (2020). Natural language processing. In Fundamentals of Artificial Intelligence (pp. 603–649).
[11] Conneau, A. (2019). Unsupervised cross-lingual representation learning at scale. arXiv preprint, arXiv:1911.02116.
[12] CSU. (2024). About Sanskrit. Retrieved October 20, 2024, from https://www.sanskrit.nic.in/about_sanskrit.php
[13] Deshpande, M. M. (2019). Scope of early Sanskrit usage: A wider approach.
[14] Ferrucci, D., Brown, E., Chu-Carroll, J., Fan, J., Gondek, D., Kalyanpur, A. A., Lally, A., Murdock, J. W., Nyberg, E., & Prager, J. (2010). Building Watson: An overview of the Deep QA project. AI Magazine, 31(3), 59–79.
[15] Gomez-Uribe, C. A., & Hunt, N. (2015). The Netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems, 6(4), 1–19.
[16] Hijiya, J. A. (2000). The “Gita” of J. Robert Oppenheimer. Proceedings of the American Philosophical Society, 144(2), 123–167.
[17] Huet, G., Kulkarni, A., & Scharf, P. (2009). Sanskrit computational linguistics. Springer.
[18] Hughes, L., Dwivedi, Y. K., Rana, N. P., Williams, M. D., & Raghavan, V. (2022). Perspectives on the future of manufacturing within the Industry 4.0 era. Production Planning & Control, 33
(2–3), 138–158.
[19] Jain, R. (2023). Role of artificial intelligence in banking and finance. Journal of Management and Science, 13(3), 1–4.
[20] Khichar, V. (2024). Nalanda University: A dream Asian university of the 21st century. CHETANA International Journal of Education, 9(1), 77–79.
[21] KJ, S., Jain, V., Bhaduri, S., Roy, T., & Chadha, A. (2024). Decoding the diversity: A review of the Indic AI research landscape. arXiv preprint, arXiv:2406.09559.
[22] Luckin, R., & Holmes, W. (2016). Intelligence unleashed: An argument for AI in education.
[23] Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1–35.
[24] Mishra, V., & Mishra, R. (2012). English to Sanskrit machine translation system: A rule-based approach. International Journal of Advanced Intelligence Paradigms, 4(2), 168–184.
[25] Oak, N. N. (2024). Epic literature as source of Bhartiya cultural history. Pratnakīrti, 5(1), 1–20.
[26] Prothero, S. (2011). God is not one: The eight rival religions that run the world, and why their differences matter. Black Inc.
[27] Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., & Waldman-Brown, A. (2022). Tackling climate change with machine learning. ACM Computing Surveys, 55(2), 1–96.
[28] Roundtree, A. K. (2023). AI explainability, interpretability, fairness, and privacy: An integrative review of reviews. In International Conference on Human–Computer Interaction. Springer.
[29] Singh, M., Kumar, R., & Chana, I. (2020). Corpus-based machine translation system with deep neural network for Sanskrit to Hindi translation. Procedia Computer Science, 167, 2534–2544.
[30] Topol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Hachette UK.
[31] Ueda, D., Kakinuma, T., Fujita, S., Kamagata, K., Fushimi, Y., Ito, R., Matsui, Y., Nozaki, T., Nakaura, T., & Fujima, N. (2024). Fairness of artificial intelligence in healthcare: Review and recommendations. Japanese Journal of Radiology, 42(1), 3–15.
[32] Vaishya, R., Javaid, M., Khan, I. H., & Haleem, A. (2020). Artificial intelligence (AI) applications for COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), 337–339.
[33] van Wyk, K. (2015). Evidence of Hindu religion on the theory of Chomsky’s transformational grammar.
[34] Vasu, S. C. (1897). The Ashtadhyayi of Panini. Satyajnan Chaterji.
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