Article citationsMore>>
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., Hassabis, D., Clopath, C., Kumaran, D. and Hadsell, R. (2017) Overcoming Catastrophic Forgetting in Neural Networks. Proceedings of the National Academy of Sciences of the United States of America, 114, 3521-3526.
https://doi.org/10.1073/pnas.1611835114
has been cited by the following article:
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TITLE:
Toward Artificial General Intelligence: Deep Reinforcement Learning Method to AI in Medicine
AUTHORS:
Daniel Schilling Weiss Nguyen, Richard Odigie
KEYWORDS:
Artificial Intelligence, Deep Learning, General-Purpose Learning Agent, Generalizability, Algorithmic Flexibility, Internal Autonomy
JOURNAL NAME:
Journal of Computer and Communications,
Vol.11 No.9,
September
28,
2023
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.
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