When you ask ChatGPT a question or generate an image with DALL-E, you're witnessing the magic of the Transformer architecture. This deep learning model, introduced in 2017, revolutionized how machines understand and generate human language.
Unlike older models that processed words sequentially, Transformers use a mechanism called "attention" to weigh the importance of every word relative to others in a sentence. This parallel processing allows them to grasp context far more efficiently, making GPT and similar generative AI possible.
"Attention is all you need," as the original paper put it.
Today, Transformers power not only text generation but also translation, summarization, and even code writing. Their ability to scale has led to the explosion of large language models (LLMs), with billions of parameters trained on vast internet text.
The result? AI that can converse, create, and assist in ways that were science fiction just a decade ago.