Document Type

Article

Publication Title

Journal of Artificial Intelligence and Robotics

Abstract

The rapid advancement of Artificial Intelligence (AI) has significantly impacted various domains. Yet, the exploration of AI's potential to develop a deep understanding of family culture and identity remains underexplored. This study introduces the concept of "a love of grandma and apple pie" to symbolize the potential of various AI to internalize and appreciate familial relationships, cultural traditions, and personal identity. The proposed study would investigate how an advanced deep learning model, trained on diverse unstructured datasets—including multimedia data from 100 families-could learn and reflect human-like emotions, values, and cultural understanding. Utilizing Convolutional Neural Networks (CNNs) for visual data processing and Bidirectional Encoder Representations from Transformers (BERT) for Natural Language Processing (NLP), the AI agent can be trained in a high-performance computing environment. The results demonstrated through the simulated training data show that AI agents could successfully interpret and engage with complex family dynamics, cultural contexts, and individual identities, achieving an overall precision of 92% and recall of 89% in recognizing emotional states, cultural traditions, and family roles. The research team reported high satisfaction with the empathetic and contextually appropriate interactions of the agents. These findings suggest significant potential for AI applications in personalized healthcare, education, and elderly care. The study further underscores the importance of integrating ethical frameworks and cultural awareness into AI development to ensure that such systems are empathetic, culturally sensitive, and aligned with human values. Future research is recommended to explore the long-term impact of AI interactions, cross-cultural comparisons, and the ethical implications of AI systems that simulate human relationships.

DOI

https://doi.org/10.59232/AIR-V2I3P101

Publication Date

9-2024

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