인공지능 딥러닝의 역사와 현황, 그리고 미래 방향
History, Current Status and Future Directions of Deep Learning
이원진,
소속 상세정보
이원진 ( Yi Won-Jin ) - Seoul National University School of Dentistry Department of Oral and Maxillofacial Radiology
Abstract
Deep learning is a subset of machine learning, and machine learning is also a subset of artificial intelligence (AI). The biggest difference between machine learning and deep learning is that in the learning of artificial intelligence models, machine learning basically requires a human feature extraction process before learning, but deep learning does not require this process and the original data is directly used as input. The development of deep learning coincides with the development of artificial neural networks (ANNs), and many people have contributed to the development of artificial neural networks for decades. The following five models are the representative architectures most widely used in deep learning. That is, Deep Feedforward Neural Network (DFFNN), Convolutional Neural Network (CNN), Deep Belief Network (DBN), Autoencoders (AE), and Long Short-Term Memory (LSTM) Network. A convolutional neural network (CNN) is a feedforward NN composed of a convolutional layer, a ReLU activation function, and a pooling layer. CNNs provide properties of weight sharing and local connectivity to process high-dimensional data. In dental and medical fields, an AI model that can be interpretable or explainable (XAI) is needed to increase patient persuasiveness. In the future, explainable AI (XAI) will become an indispensable and practical component in order to obtain an improved, transparent, secure, fair and unbiased AI learning model.
키워드
Artificial Intelligence (AI); Deep learning; Artificial Neural Networks (ANN); Convolutional Neural Network (CNN); Explainable AI (XAI)
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