Comparing Transformers & Attention Mechanisms, Generative Adversarial Networks, and Reinforcement Learning to Human Learning Processes – A Literature Review

Abstract

Recent advancements in Artificial Intelligence (AI) have introduced powerful machine learning techniques, such as Transformers & Attention Mechanisms, Generative Adversarial Networks (GANs), and Reinforcement Learning (RL). Comparing these methods with human learning processes provides insights into their strengths and limitations. Transformers & Attention Mechanisms demonstrate human-like selective attention, effectively comprehending the broader context while focusing on essential details, reflecting cognitive processes in humans. However, the inherent differences between AI and human attention mechanisms highlight distinct approaches: algorithmic processing in AI versus human cognition. GANs are capable of generating synthetic data, showcasing creativity in producing realistic artifacts, but their algorithmic nature limits their depth compared to human artistic creativity. To address this limitation, researchers have explored introducing "arousal potential" to encourage deviations from established styles in art generation, enhancing the artistic appeal of GANs' output. RL, mimicking human learning in sequential decision-making tasks, often outperforms human strategies, indicating its effectiveness in learning complex strategies and exploring solutions more effectively than humans. Although AI still lacks the intrinsic depth, intuition, and creativity found in human cognition. Understanding these similarities and distinctions can guide the future development of more sophisticated and human-like AI systems, facilitating progress in AI research and applications. and applications.