Human-Centric AI: Bridging Emotional Intelligence with Computational Efficiency
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Abstract
The development of artificial intelligence systems that can effectively interact with humans hinges on the integration of emotional intelligence and computational efficiency. In this paper, we propose Emotionally Adaptive Neural Systems (EANS), a new approach to embedding emotional context and affective computing within AI models. EANS employs multi-modal data, including facial expressions, speech intonations, and physiological signals, to dynamically adapt responses based on user emotions. By fusing emotional cues with a computationally efficient transformer-based architecture, EANS achieves real-time emotional alignment in human-AI interactions. Experimental results in healthcare, education, and customer service applications demonstrate a 50% increase in user satisfaction and engagement. This work lays the foundation for developing AI systems capable of fostering meaningful, human-centric interactions.
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