Transformers

In the realm of artificial intelligence (AI), transformers refer to a type of deep learning model architecture that has revolutionized various natural language processing (NLP) tasks. Transformers utilize self-attention mechanisms to weigh the importance of different words in a sentence, allowing them to capture contextual dependencies and semantic relationships more effectively than previous models. This architecture has enabled the development of larger and more powerful language models, such as OpenAI's GPT (Generative Pre-trained Transformer) series and Google's BERT (Bidirectional Encoder Representations from Transformers).

Transformers have significantly advanced the capabilities of AI in tasks such as language translation, text summarization, sentiment analysis, and question answering, among others. Moreover, transformers' versatility extends beyond NLP, with applications in computer vision, speech recognition, and various other domains. Their effectiveness in capturing complex patterns and understanding context has made transformers a cornerstone of modern AI research and development.