In the context of generative AI, transformers can be thought of as the “brain” behind the technology. Just like how the human brain processes information and generates thoughts, transformers process and generate text, images, or other types of data.
Imagine a team of expert translators working together to translate a book from one language to another. Each translator has their own specialty and focuses on a specific part of the book. They work in parallel, constantly communicating and sharing information with each other to ensure the translation is accurate and coherent. This is similar to how transformers work in AI.
Transformers consist of multiple layers, each responsible for a specific aspect of understanding and generating data. These layers work together, passing information back and forth, to create a comprehensive understanding of the input data. This allows the AI to generate output that is contextually relevant and coherent.
Another way to think about transformers is like a recipe. Just as a recipe combines various ingredients in a specific order to create a dish, transformers combine different pieces of information in a specific order to generate the desired output. The “ingredients” in this case are the input data, such as text or images, and the “recipe” is the set of instructions (algorithms) that tell the transformer how to process and combine this information.
The transformer architecture has revolutionized generative AI by enabling the creation of large language models like GPT (Generative Pre-trained Transformer).