👉 Transformation computing is a paradigm that leverages deep learning models, particularly transformers, to perform complex data transformations and generate new data forms. Unlike traditional neural networks that process data in a sequential manner, transformers utilize self-attention mechanisms to understand and manipulate relationships between different parts of the input data simultaneously. This allows them to efficiently handle tasks like natural language processing, image generation, and video synthesis by capturing long-range dependencies and contextual information. By transforming input data into a more abstract representation, these models can generate new content that closely resembles the original data, making them highly effective for a wide range of applications in artificial intelligence.
