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• W4387110031 abstract "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." @default.
• W4387110031 created "2023-09-28" @default.
• W4387110031 creator A5047158749 @default.
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• W4387110031 date "2023-01-01" @default.
• W4387110031 modified "2023-09-28" @default.
• W4387110031 title "Toward Artificial General Intelligence: Deep Reinforcement Learning Method to AI in Medicine" @default.
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• W4387110031 doi "https://doi.org/10.4236/jcc.2023.119006" @default.
• W4387110031 hasPublicationYear "2023" @default.
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