What problem leads to Transformers in Neural network problems ?

Okay so when we have RNNs and CNNs , how they come up with the transformers ? what problem lead them to this solution ?

These are the basic quesiton come up in my mind whenver I think about some solution which create some kind of revolution changes in any field.

The development of transformers was driven by the need to overcome certain limitations of RNNs and CNNs when processing sequential data. The key problem that led to the creation of transformers was the difficulty in capturing long-range dependencies efficiently.

While RNNs are designed to model sequential data by maintaining memory of past information, they suffer from issues such as vanishing or exploding gradients, which make it challenging to capture dependencies that span long sequences. As a result, RNNs struggle to effectively model long-range dependencies in practical applications.

On the other hand, CNNs excel at capturing local patterns and hierarchical relationships in grid-like data, such as images. However, they are not explicitly designed to handle sequential data and do not naturally capture long-range dependencies.

Transformers were introduced as an alternative architecture that could capture long-range dependencies more effectively. The transformer model incorporates a self-attention mechanism, which allows the model to attend to different positions in the input sequence to establish relationships between words or tokens. This attention mechanism enables the transformer to consider the context of each word in relation to all other words in the sequence, irrespective of their relative positions.

By incorporating self-attention, transformers eliminate the need for recurrent connections used in RNNs, allowing for parallel processing and more efficient computation. This parallelism enables transformers to handle longer sequences more effectively and capture complex dependencies across the entire sequence.

The transformer architecture, first introduced in the context of machine translation with the "Transformer" model by Vaswani et al. in 2017, quickly gained popularity due to its ability to model sequential data efficiently and achieve state-of-the-art performance in various natural language processing tasks. Since then, transformers have been widely adopted in many domains, including language understanding, text generation, question answering, and even applications beyond natural language processing, such as image processing and time-series analysis.