New model requires significantly fewer resources for running and training LLMs
Recurrent Neural Networks (RNNs) are a type of Artificial Intelligence primarily used in the field of deep learning. Unlike traditional neural networks, RNNs have a memory that captures information about what has been calculated so far. In other words, they use their understanding from previous inputs to influence the output they will produce.
RNNs are called “recurrent” because they perform the same task for every element in a sequence, with the output being dependent on the previous computations. RNNs are still used to power smart technologies like Apple’s Siri and Google Translate.
However, with the advent of transformers like ChatGPT, the landscape of natural language processing (NLP) has shifted. While transformers revolutionized NLP tasks, their memory and computational complexity scaled quadratically with sequence length, demanding more resources.
