A look under the hood of transfomers, the engine driving AI model evolution
1 min read
Summary
As transformer architecture becomes the backbone of the artificial intelligence (AI) industry, it is important to understand how it works and its benefits for scalable solution growth, according to Terrence Alsup, a senior data scientist at Finastra.
Originally created in 2017, transformers were designed to sequence data and are ideal for language translation, sentence completion and automatic speech recognition, among other uses.
The core component of transformer models is the attention layer, which allows the model to understand and learn the relationships between words and data sequences.
For the future, state-space models such as Mamba are anticipated to rise as they can handle very long data sequences whereas transformers are limited by the context window.
Furthermore, multimodal models are expected to be a focus for innovation, such as OpenAI’s GPT-4, which can handle text, audio and images, and offers a means to make AI more accessible.