IBM's transformer architecture explainer connects the technical details to the practical results

The transformer architecture is the thing underneath most of the AI tools people use daily and IBM's explainer https://www.ibm.com/think/topics/transformer-model covers the mechanics in a way that connects the technical details to the practical results you experience.

The parallel processing capability is the thing that made transformers practically viable at scale. Earlier sequence models like RNNs processed text token by token, which meant they could not be parallelised across GPU cores efficiently. Transformers process the entire input simultaneously, which is why training became economically feasible at the scale required for capable models.

The attention mechanism being the core innovation of the transformer architecture is why 3Blue1Brown's attention video (covered in the other forum thread) is worth watching alongside this article. Understanding attention is understanding why transformers can handle long context windows in a way that earlier architectures could not.

The article's coverage of why transformers replaced earlier approaches in language AI is the history worth knowing to interpret current research directions. The field has been building on the transformer architecture for years now and understanding why it won tells you what properties future architectures would need to improve on.

What specific limitation of current transformer-based models do you think is most likely to be solved by a new architectural approach in the next few years?