Embedding Emotional Intelligence in AGI for Enhanced Cognition

Embedding emotional intelligence into Artificial General Intelligence (AGI) enhances its cognitive versatility beyond mere emotion recognition. This study develops a multimodal AGI prototype that integrates facial expression data (CK+ dataset) and speech emotion cues (RAVDESS dataset) to inform reasoning and adaptability across diverse tasks. A transformer-based architecture fuses visual and auditory embeddings, feeding them into a reasoning module enhanced by a fine-tuned large language model (LLM) to generate emotionally tailored responses. The AGI assists users in problem-solving or independently resolves scenarios, adapting to emotional contexts. Evaluation targets >85% emotion recognition accuracy and 20% higher task success rates—measured as user or AGI performance improvements—over emotion-agnostic baselines, plus user satisfaction (>4/5) from 50 evaluators. Early simulations suggest emotional integration boosts contextual reasoning by up to 25%. This work pioneers an affective AGI framework, leveraging emotional cues to enhance cognition, with applications spanning education, healthcare, conflict resolution, and ethical AI, and insights into emotion’s role in intelligence.