الآثار المحتملة لتطبيق نظرية المزج المفاهيمي في مجال الذكاء الاصطناعي
DOI:
https://doi.org/10.25130/jfa.conf.10.5.18الكلمات المفتاحية:
نظرية المزج المفاهيمي، الذكاء الاصطناعي، اللغويات المعرفية، المساحات العقلية، الإبداع الآلي، أخلاقيات الذكاء الاصطناعي، التعلم العميقالملخص
تبحث هذه الدراسة في تطبيق نظرية المزج المفاهيمي (CBT)، وهي نموذج أساسي للإدراك البشري مستقى من اللغويات المعرفية، على مجال الذكاء الاصطناعي. مع التطور السريع لأنظمة الذكاء الاصطناعي لمحاكاة القدرات المعرفية البشرية، يصبح فهم الآليات التي تدعم عملياتها الإبداعية والاستدلالية أمرًا بالغ الأهمية. تفترض هذه الدراسة أن نظرية المزج المفاهيمي، كما صاغها فوكونير وتيرنر (2002)، توفر إطارًا قويًا لتحليل كيفية دمج الذكاء الاصطناعي للمفاهيم المتباينة لتوليد مخرجات جديدة وذات معنى. تعتمد الدراسة على نهج ثنائي المنهج، يجمع بين دراسة نظرية لنظرية المزج المفاهيمي والذكاء الاصطناعي وتحليل عملي للنصوص المرئية التي يُنتجها الذكاء الاصطناعي والمستمدة من وسائل التواصل الاجتماعي.
يُظهر التحليل أن الذكاء الاصطناعي التوليدي المتقدم يُجري مزجًا مفاهيميًا متطورًا، حيث يُسقط عناصر انتقائية من مساحات ذهنية متعددة المدخلات لإنشاء مزيج بصري متماسك ومثير. تؤكد النتائج أن الذكاء الاصطناعي لا يقتصر على محاكاة العمليات الهيكلية للتكامل المفاهيمي البشري - التأليف، والاكتمال، والتوضيح - بل يُنتج أيضًا معانٍ ناشئة ذات صدى عملي وعاطفي كبير. يشير هذا إلى وجود ارتباط وثيق بين الإدراك البشري والآلي، مما يوحي بأن الذكاء الاصطناعي يسد الفجوة في الفهم السياقي والارتجال الإبداعي.
التنزيلات
المراجع
Alexa, S. (2013).CONCEPTUAL BLENDING FOR THE VISUAL DOMAIN,Bachelor Opleiding Kunstmatige Intelligentie Faculty of Science University of Amsterdam .
Bengio, Y., Goodfellow, I., & Courville, A. (2017). Deep learning (Vol. 1, pp. 23-24). Cambridge, MA, USA: MIT press.
Bishop, C. M., & Nasrabadi, N. M. (2006). Pattern recognition and machine learning (Vol. 4, No. 4, p. 738). New York: springer.
Bourou, D., Schorlemmer, M., Plaza, E., & Veiner, M. (2024). Characterising cognitively useful blends: Formalising governing principles of conceptual blending. Cognitive Systems Research, 86, 101245.
Brandt, L., & Brandt, P. A. (2005). Making sense of a blend: A cognitive-semiotic approach to metaphor. Annual Review of Cognitive Linguistics, 3(1), 216–249. https://doi.org/10.1075/arcl.3.11bra
Brownlee, J. (2017). Deep learning for natural language processing: develop deep learning models for your natural language problems. Machine Learning Mastery
Câmara Pereira, F. (2007). Creativity and artificial intelligence: a conceptual blending approach. Mouton de Gruyter.
Câmara Pereira, F. (2007). Creativity and artificial intelligence: a conceptual blending approach. Mouton de Gruyter.
Câmara Pereira, F. (2007). Creativity and artificial intelligence: a conceptual blending approach. Mouton de Gruyter.
Castillo-Girones, S., Munera, S., Martínez-Sober, M., Blasco, J., Cubero, S., & Gómez-Sanchis, J. (2025). Artificial Neural Networks in Agriculture, the core of artificial intelligence: What, When, and Why. Computers and Electronics in Agriculture, 230, 109938.
Chandrahas Mishra, D. L. Gupta (2017).Deep Machine Learning and Neural Networks: An Overview IAES International Journal of Artificial Intelligence (IJ-AI) Vol. 6, No. 2, June 2017, pp. 66~73
Coulson, S. (2001). Semantic leaps: Frame-shifting and conceptual blending in meaning construction. Cambridge University Press.
Deng, L., & Liu, Y. (Eds.). (2018). Deep learning in natural language processing. Springer.
Eppe, M., Maclean, E., Confalonieri, R., Kutz, O., Schorlemmer, M., Plaza, E., & Kühnberger, K. U. (2018). A computational framework for conceptual blending. Artificial Intelligence, 256, 105-129.
Evans, V., & Green, M. (2006). Cognitive linguistics: An introduction. Edinburgh University Press.
Fauconnier, G. (2000). Response to Gibbs. Cognitive Linguistics11(1-2), 27–28.
Fauconnier, G. (2018). Mental spaces. In Ten Lectures on Cognitive Construction of Meaning (pp. 1-23). Brill.
Fauconnier, G., & Turner, M. (1998). Conceptual integration networks. Cognitive science, 22(2), 133-187
Fauconnier, G., & Turner, M. (2002). The way we think: Conceptual blending and the mind's hidden complexities. Basic Books.
Fauconnier, G., & Turner, M. (2003). Conceptual blending, form and meaning. Recherches en communication, 19, 57-86.
Fauconnier, G., & Turner, M. B. (2008). Rethinking metaphor.
Gibbs, R. W. (2000). Making good psychology out of blending theory. *Cognitive Linguistics11 (1-2), 347–358.
Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1, No. 2). Cambridge: MIT press.
Grady, J., Oakley, T., & Coulson, S. (1999). Blending and metaphor. In Metaphor in cognitive linguistics (p. 101). John Benjamins.
Hsu, K., Levine, S., & Finn, C. (2018). Unsupervised learning via meta-learning. arXiv preprint arXiv:1810.02334.
Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic markets, 31(3), 685-695.
Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic markets, 31(3), 685-695.
Kim, K. G. (2016). Book review: Deep learning. Healthcare informatics research, 22(4), 351-354
Kotsiantis, S. B., Zaharakis, I., & Pintelas, P. (2007). Supervised machine learning: A review of classification techniques. Emerging artificial intelligence applications in computer engineering, 160(1), 3-24.
Kumar, K., & Thakur, G. S. M. (2012). Advanced applications of neural networks and artificial intelligence: A review. International journal of information technology and computer science, 4(6), 57-68.
Lakoff, G. (2006). Conceptual metaphor. Cognitive linguistics: Basic readings, 34, 185-238.
Lakoff, G., & Johnson, M. (1980). Metaphors we live by. University of Chicago Press.
Lane, H., & Dyshel, M. (2025). Natural language processing in action. Simon and Schuster.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
Muhammad, I., & Yan, Z. (2015). SUPERVISED MACHINE LEARNING APPROACHES: A SURVEY. ICTACT Journal on Soft Computing, 5(3).
Oakley, T., & Coulson, S. (1999). “Blending and Metaphor” pp.99. Steen, G. and Gibbs, R (1999). Metaphor in Cognitive Linguistics. Philadelphia: John Benjamins.
Ponsen, M., Spronck, P., Munoz-Avila, H., & Aha, D. W. (2007). Knowledge acquisition for adaptive game AI. Science of Computer Programming, 67(1), 59-75.
Qamar, R., & Zardari, B. A. (2023). Artificial neural networks: An overview. Mesopotamian Journal of Computer Science, 2023, 130-139.
Ritchie, D. (2004). Metaphors in conversational context: Toward a connectivity theory of metaphor interpretation. Metaphor and symbol, 19(4), 265-287.
Ritchie, D. (2004). Metaphors in conversational context: Toward a connectivity theory of metaphor interpretation. Metaphor and Symbol, 19(4), 265–287.
Russell, S., Norvig, P., Popineau, F., Miclet, L., & Cadet, C. (2021). Intelligence artificielle: une approche moderne (4ᵉ édition). Pearson France.
Sarker, I. H. (2021). Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions. SN computer science, 2(6), 1-20.
Sharma, R., Sharma, K., & Khanna, A. (2020). Study of supervised learning and unsupervised learning. International Journal for Research in Applied Science and Engineering Technology, 8(6), 588-593.
Siemens, G., Marmolejo-Ramos, F., Gabriel, F., Medeiros, K., Marrone, R., Joksimovic, S., & de Laat, M. (2022). Human and artificial cognition. Computers and Education: Artificial Intelligence, 3, 100107.
Szeliski, R. (2022). Computer vision: algorithms and applications. Springer Nature.
Timbs, A. (2025). The Cognitive Convergence of AI and Human Learning: How Learning Theories Illuminate Artificial Intelligence. Preprint.
Turner, M. (1993). An image-schematic constraint on metaphor. Conceptualizations and mental processing in language, 291-306.
Turner, M. (2001). Cognitive dimensions of social science. Oxford University Press.
Turner, M., & Fauconnier, G. (1995). Conceptual integration and formal expression. In Metaphor and Philosophy Metaphor and Symbolic Activity, Vol. 10, No. 3, pp. 183-203, 1995,https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1650417.
Turner, M., & Fauconnier, G. (2000). Metaphor, metonymy, and binding. Topics in English Linguistics, 30, 133-148.
Veale, T. (2019). From conceptual mash-ups to badass blends: A robust computational model of conceptual blending. Computational creativity: The philosophy and engineering of autonomously creative systems, 71-89.
Yaghoubi, E., Yaghoubi, E., Khamees, A., & Vakili, A. H. (2024). A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering. Neural Computing and Applications, 36(21), 12655-12699.
Zbikowski, L. M. (2018). Conceptual blending, creativity, and music. Musicae Scientiae, 22(1), 6-23.
Zhuang, Z. (2021, March). The Working Mechanism of Conceptual Blending Theory in Metaphor Understanding. In 2020 International Conference on Language, Communication and Culture Studies (ICLCCS 2020) (pp. 94-96). Atlantis Press.
https://builtin.com/artificial-intelligence
https://cloud.google.com/discover/supervised-vs-unsupervised-learning
https://flashcards.world/flashcards/sets/d912cca0-89f4-40fa-a889-b3397a475a17/
https://www.coursera.org/articles/ai-vs-deep-learning-vs-machine-learning-beginners- guide?isNewUser=true
https://www.sciencedirect.com/science/article/pii/S2666920X22000625
https://www.trados.com/learning/topic/linguistic-AI/
https://www.uobabylon.edu.iq/eprints/publication_9_29260_34.pdf
