Deep Learning Impacts in the Field of Artificial Intelligence
Wasswa Shafik
Abstract
Deep Learning (DL) is a subfield of Artificial Intelligence (AI) that utilizes neural networks to learn and analyze large datasets. DL has become an indispensable part of AI, making it possible to achieve unprecedented accuracy and efficiency in tasks, for example, Natural Language Processing (NLP), predictive analytics, image recognition, autonomous systems, and speech recognition. One of the perilous merits of DL is its process capability and massive data analysis. In the past, traditional machine learning algorithms required a lot of hand-crafted feature engineering, which could be time-consuming and expensive. The neural network learns to identify relevant features from the raw data, saving significant time and effort. DL has profoundly impacted AI research and development. This chapter discusses DL impacts in AI as it is applied in different arenas, enabling the current DL developments and practical solutions to these impacts. Alongside, it presents future directions since DL has significantly impacted the industry, particularly in finance, healthcare, and manufacturing. Predictive analytics, for example, has become a vital tool for businesses forecasting future trends and making informed decisions. Finally, DL algorithms in healthcare are being used to develop personalized treatments and diagnostic tools to identify diseases more accurately and quickly.