Recent Trends in Artificial Intelligence: From Machine Learning to Generative AI
Keywords:
Artificial Intelligence, Machine Learning, Deep Learning Architecture, Generative AI, Natural Language Processing (NLP)Abstract
Artificial Intelligence (AI) has developed into the rule-based symbolic systems to sophisticated data-driven and generative models that are changing modern technology. This review paper provides the overall overview of recent trends in AI, including the development of Generative AI and foundation models out of Machine Learning (ML) and Deep Learning (DL). It explores the fundamental principles of AI, such as algorithms, data representation and interdisciplinary impact and then the major ML paradigms of supervised, unsupervised and reinforcement learning. The paper also discusses key architectures of deep learning such as ANN, CNN, RNN, LSTM, GRU, Autoencoders, and Transformers that have contributed to the development of intelligent systems. Particular focus is placed on Generative AI, large language models, and transformer-based architectures which allow one to generate content in different domains. The literature review is an overview of the recent findings in the field of applications, ethical issues, governance, and future challenges, which gives a systematic insight into the active development of AI and its transformative nature.
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