What is the primary use case for transformers in AI?

Transformers are primarily designed for processing sequential data, making them particularly effective in tasks such as natural language processing (NLP). The architecture of transformers allows them to handle sequences of varying lengths while maintaining the context of each element in relation to the others. This is achieved through mechanisms like self-attention, which enables the model to weigh the relevance of each part of the input sequence dynamically.

In the realm of AI, transformers excel at understanding relationships in data where the order and context are critical. For example, when analyzing text, understanding the sequence of words is vital for comprehending the meaning. This is why transformers have become foundational in various applications involving language, such as translation, text generation, and sentiment analysis.

While transformers could theoretically be adapted for image recognition or audio signal processing, their unique strengths lie in their capability to deal with sequences. Other architectures might be more efficient for specific use cases in image or audio processing, where convolutional neural networks (CNNs) or recurrent neural networks (RNNs) are typically preferred. Thus, processing sequential data is the primary illustration of transformers' utility in AI.