Current perspective on artificial intelligence, machine learning and deep learning

Abstract

Artificial intelligence has exploded in the past few years, especially after 2015. Much of it is due to the widespread use of GPUs, which has made parallel computing faster, cheaper, and more efficient. Of course, the combination of infinite expansion of storage capacity and sudden explosion of data torrent (big data) also makes image data, text data, transaction data, mapping data comprehensive and massive explosion. The wave of artificial intelligence has swept the world, and many words still plague us: artificial intelligence, machine learning, and deep learning. Many people do not have a deep understanding of the meaning of these high-frequency words and the relationship behind them. In order to better understand artificial intelligence, this article explains the meaning of these words in the simplest language to clarify the relationship between them, hoping to be helpful to the beginners. Deep learning expands the scope of artificial intelligence by enabling a wide range of applications for machine learning. Deep learning can overwhelmingly accomplish a variety of tasks, making all machine access capabilities available. For more complex applications, many implementations do not have to rely on supercomputing environments and big data. Data is indispensable, but too much data will also lead to overfitting. Algorithms are the key to solving learning problems. Efficient algorithms make artificial intelligence and machine learning less dependent on big data and supercomputer environment. Data, algorithms and computing power (computing speed, space) maintain a dynamic triangle in the implementation of artificial intelligence.