Advantages

New Capabilities Construction is No Needed One major benefit of deep learning is no need to build new capabilities and algorithms by the use of an artificial neural network, which means that runs feature engineering on its own. Deep learning algorithms enable faster learning without being explicitly instructed by scanning data and searching and combining correlated functions. This feature can save data scientists months of work. In addition, neural networks with deep learning can discover new and more complex features that humans may miss. According to Udas (2020), “Unlike machine learning, there is no need to build new features and algorithms because deep learning directly identifies features from the data. It uses 150 layers of information to process features directly from the data received and also monitor its own performance” (para. 7). Data Labeling Process is no needed Another benefit of deep learning is that we do not need to build a separate data labeling process. Sometimes, if looking at the Internet, we can see that artificial intelligence cannot distinguish between dog pictures and muffin pictures. In order for artificial intelligence learning and building algorithm, the name must be labeled on materials such as photos, which are time-consuming and expensive. However, deep learning can be built on its own without the need for such a process. Deep learning can label data on its own because it has a system network that allows to learn on their own without having to input information separately. According to Valeryia (2018), “With deep learning, the need for well-labeled data is made obsolete as deep learning algorithms excel at learning without guidelines. Other forms of machine learning are not nearly as successful with this type of learning. In the example above, a deep learning algorithm would be able to detect physical anomalies of the human body, even at earlier stages than human doctors” (para. 14). Securing Safety due to the Proliferation of Autonomous Vehicle The last benefit of deep learning technology can be clearly found in autonomous driving, which is the most representative example of deep learning technology being used. The deep learning technology of autonomous vehicles has the effect of preventing accidents by establishing an artificial neural network based on data collected by learning road conditions on their own. Therefore, the autonomous driving system can significantly reduce the mortality rate of traffic accidents and traffic accidents. Rowland (2021) states, “Motion control and object recognition improve road traffic safety and reduce fatalities by use of sensing and navigation systems and mobile data traffic. Computer vision, sensor data processing, and adaptive and dynamic planning optimize road user safety and mobility across autonomous vehicle control systems through real-time object detection and recognition” (p. 31). Unlike the conventional way of constantly updating new road conditions to build algorithms, deep learning builds and updates itself to expand the information network, increasing safety and lowering accident rates.