Transformer

Transformers are deep learning models that use self-attention to weigh the importance of different parts of input data. They are used mainly in natural language processing (NLP) and computer vision. Unlike recurrent neural networks, transformers process the entire input at once, allowing for more parallelization and faster training times. Transformers have become the model of choice for NLP problems, replacing RNNs such as LSTM, and have led to the development of pre-trained systems such as BERT and GPT, which can be fine-tuned for specific tasks.

The graph shows the popularity of the term "transformer" over time. The peaks in interest before 2017 likely correspond to the release of the "Transformers" movies, which drew significant public attention. After 2017, there was a decline, which may reflect the reduced novelty or frequency of the movies. However, the term "transformer" gained new relevance in AI following the introduction of the Transformer architecture in 2017, a groundbreaking development in natural language processing that did not immediately reach the same level of general public interest as the films but has gradually grown in the AI community.