Toward Artificial General Intelligence: Deep Reinforcement Learning Method to AI in Medicine
Toward Artificial General Intelligence: Deep Reinforcement Learning Method to AI in Medicine作者机构：Aspen University Phoenix AZ USA
出 版 物：《Journal of Computer and Communications》 (电脑和通信（英文）)
年 卷 期：2023年第11卷第9期
页 面：84-120页
学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
主 题：Artificial Intelligence Deep Learning General-Purpose Learning Agent Generalizability Algorithmic Flexibility Internal Autonomy
摘 要：Artificial general intelligence (AGI) is the ability of an artificial intelligence (AI) agent to solve somewhat-arbitrary tasks in somewhat-arbitrary environments. Despite being a long-standing goal in the field of AI, achieving AGI remains elusive. In this study, we empirically assessed the generalizability of AI agents by applying a deep reinforcement learning (DRL) approach to the medical domain. Our investigation involved examining how modifying the agent’s structure, task, and environment impacts its generality. Sample: An NIH chest X-ray dataset with 112,120 images and 15 medical conditions. We evaluated the agent’s performance on binary and multiclass classification tasks through a baseline model, a convolutional neural network model, a deep Q network model, and a proximal policy optimization model. Results: Our results suggest that DRL agents with the algorithmic flexibility to autonomously vary their macro/microstructures can generalize better across given tasks and environments.
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