Artificial Intelligence (AI) and Deep Learning, with a focus on Natural Language Processing (NLP), have seen substantial changes in the last few years. The area has advanced quickly in both theoretical development and practical applications, from the early days of Recurrent Neural Networks (RNNs) to the current dominance of Transformer models.
Models that are capable of processing and producing natural language with efficiency have advanced significantly as a result of research and development in the field of neural networks, particularly with regard to managing sequences. RNN’s innate ability to process sequential data makes them well-suited for tasks involving sequences, such as time-series data, text, and speech. Though RNNs are ideally suited for these kinds of jobs, there are still problems with scalability and training complexity, particularly with lengthy sequences.
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