Abstract

This paper reviews and analyzes the developments of deep learning in AI research, focusing particularly on innovations along with emerging problems. It points out how emerging developments and potential issues affect development and deployment through deep learning innovations. The analysis looks into innovation in new structures, data poor and data deficiency, AI interpretation, ethical factors, and both social and economic effects of DL. A qualitative methodology involving literature review and expert interviews is applied in order to insight these areas. The paper reveals some critical advancements in architectures developed for neural networks, improvement in data quality, interpretability, ethical considerations, and societal impact. Yet, the research casts aside other present major open problems of scalability, data bias, lack of transparency, and ethical dilemmas that significantly shape the future trajectory of deep learning.