Neuromyotonic AI: Integrating Symbolic Reasoning and Deep Learning for Enhanced Problem-Solving Capabilities
Article Sidebar
Main Article Content
Abstract
Neuromyotonic AI, an emerging interdisciplinary field, aims to bridge the gap between symbolic reasoning and deep learning approaches to enhance problem-solving capabilities. This paper provides an overview of Neuromyotonic AI, highlighting its principles, techniques, and applications. representations and logical rules, with deep learning, which learns patterns from data, Neuromyotonic AI seeks to overcome the limitations of each approach while leveraging their complementary strengths. Key components of Neuromyotonic AI include knowledge representation, reasoning mechanisms, and learning algorithms that enable the integration of symbolic and sub symbolic information. Techniques such as neural-symbolic integration, hybrid architectures, and neuromyotonic reasoning engines facilitate the seamless interaction between symbolic and neural components, enabling the synthesis of high-level symbolic knowledge with low-level perceptual information.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
You are permitted to share and adapt the material under the terms of Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). This means you can distribute and modify the work, provided appropriate credit is given, a link to the license is provided, and it's made clear if any changes were made. However, commercial use of the material is not allowed, meaning you may not use it for commercial purposes without prior permission from the copyright holder.
References
Anil, & Nipun Aggarwal. (2018). Electrical Transmission & Distribution Systems: A Review. International Journal for Research Publication and Seminar, 9(1), 16–20. Retrieved from https://jrps.shodhsagar.com/index.php/j/article/view/1292
Avinash Gaur. (2022). Exploring the Ethical Implications of AI in Legal Decision-Making. International Journal for Research Publication and Seminar, 13(5), 257–264. Retrieved from https://jrps.shodhsagar.com/index.php/j/article/view/273
Atomode, D (2024). ENERGY EFFICIENCY IN MECHANICAL SYSTEMS: A MACHINE LEARNING APPROACH, Journal of Emerging Technologies and Innovative Research (JETIR), 11 (5), 441-448.
Bengio, Y., Courville, A., & Vincent, P., "Representation Learning: A Review and New Perspectives", IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 35, Issue 8, 2013.
Bordes, A., Chopra, S., & Weston, J., "Learning End-to-End Goal-Oriented Dialog", Proceedings of the International Conference on Learning Representations (ICLR), 2017.
Dave, A., Wiseman, M., & Safford, D. (2021). SEDAT:Security Enhanced Device Attestation with TPM2.0 (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2101.06362
Deependra Narayan Singh, & Mr. Ritesh Kumar. (2019). ARTIFICIAL INTELLIGENCE APPLICATION USING SVM, GA AND PSO. International Journal for Research Publication and Seminar, 10(3), 31–36. Retrieved from https://jrps.shodhsagar.com/index.php/j/article/view/1270
Dr. Aditi Dubey. (2022). A Review Study on Computational Linguistics and Natural Language Processing. International Journal for Research Publication and Seminar, 13(2), 106–113. Retrieved from https://jrps.shodhsagar.com/index.php/j/article/view/578
Garcez, A. d'A., Gori, M., Lamb, L. C., & Serafini, L., "Neural-Symbolic Learning and Reasoning: A Survey and Interpretation", arXiv:2009.09471, 2020.
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J., "Building Machines That Learn and Think Like People", Behavioral and Brain Sciences, Volume 40, 2017.
Manpreet Singh, & Sandeep Singh Kang. (2023). Investigating role of artificial intelligence in E-commerce. International Journal for Research Publication and Seminar, 14(2), 202–207. Retrieved from https://jrps.shodhsagar.com/index.php/j/article/view/409
Marcus, G., "The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence", arXiv:2002.06177, 2020.
Marcus, G., "Deep Learning: A Critical Appraisal", arXiv:1801.00631, 2018.
Miller, T., "Explanation in Artificial Intelligence: Insights from the Social Sciences", Artificial Intelligence, Volume 267, 2019.
Muggleton, S., "Inductive Logic Programming: Issues, Results and the Challenge of Learning Language in Logic", Artificial Intelligence, Volume 64, Issue 1, 1993.
Nitya Kesharwal. (2023). AI AND IOT APPLICATION IN SUPPLY CHAIN MANAGEMENT. International Journal for Research Publication and Seminar, 14(2), 112–123. Retrieved from https://jrps.shodhsagar.com/index.php/j/article/view/399
Satyanarayan Kanungo, 2021. "Enhancing IoT Security and Efficiency: The Role of Cloud Computing and Machine Learning" ESP Journal of Engineering & Technology Advancements 1(1): 7-14. DOI: https://doi.org/10.56472/25832646/JETA-V1I1P099
Santoro, A., Bapst, V., Barrett, D. G., Battaglia, P., Pascanu, R., Battaglia, P. W., & Lillicrap, T., "A Simple Neural Network Module for Relational Reasoning", Advances in Neural Information Processing Systems (NeurIPS), 2017.
Swaraj Dhore, Shripad Dhopate, Vedashree Joshi, Anish Tilloo, & Dr. Sunil M. Wanjari. (2023). Car Trading Using Blockchain & Artificial Intelligence. International Journal for Research Publication and Seminar, 14(3), 32–36. Retrieved from https://jrps.shodhsagar.com/index.php/j/article/view/463
Sharma, S. K., & Gaur, S. (2024). Optimizing Nutritional Outcomes: The Role of AI in Personalized Diet Planning. International Journal for Research Publication and Seminar, 15(2), 107–116. https://doi.org/10.36676/jrps.v15.i2.15
Sushma Rani, & Manisha Sachar. (2022). Technological Transformation and Digital India. International Journal for Research Publication and Seminar, 13(2), 370–377. Retrieved from https://jrps.shodhsagar.com/index.php/j/article/view/616
Vikalp Thapliyal, & Pranita Thapliyal. (2024). AI and Creativity: Exploring the Intersection of Machine Learning and Artistic Creation. International Journal for Research Publication and Seminar, 15(1), 36–41. Retrieved from https://jrps.shodhsagar.com/index.php/j/article/view/329
Wang, J., & Manning, C. D., "Baselines and Bigrams: Simple, Good Sentiment and Topic Classification", Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (ACL), 2012.