Advancing Artificial Intelligence through Multi-Modal Learning Architectures for Generalized Human-Like Reasoning
Keywords:
Multi-modal learning, Deep learning, Generalized AI, CNN-RNN architectures, Human-like reasoning, Artificial cognitionAbstract
Purpose
This study investigates how multi-modal learning architectures contribute to advancing artificial intelligence (AI) systems capable of generalized, human-like reasoning.
Design/methodology/approach
The research synthesizes findings from foundational works published before 2016, focusing on architectures integrating visual, auditory, and textual modalities. It also explores contemporary architectural patterns like CNN-RNN hybrids and deep belief networks (DBNs), emphasizing their role in perception, abstraction, and contextual reasoning.
Findings
The integration of multiple data modalities significantly enhances model robustness and inference accuracy, particularly in tasks that mimic human cognition, such as emotion recognition, object understanding, and dialog generation.
Practical implications
Multi-modal learning paves the way for developing AI systems with improved real-world interaction capabilities, suitable for healthcare diagnostics, autonomous driving, and cognitive robotics.
Originality/value
This paper consolidates early research insights to reveal the enduring value of multimodal learning and proposes a unified framework aligning with human cognitive processes.
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