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Your AI Friends Have Awoken, With Noam Shazeer
No Priors: Artificial Intelligence | Technology | Startups
Explanation of the last letter in ChatGPT: Transformer Based Model
The difference between an RNN and a transformer based or attention based model is that while RNNs process words sequentially and predict the next word based on the previous state, transformer models process the entire sequence at once and take advantage of parallelism. Transformers use attention, which creates a key-value associative memory and allows for looking up information in a fuzzy and differentiable way. This attention mechanism can be used not only for problems with two sequences, like machine translation, but also to look back at the past of the sequence being produced. Transformers are highly efficient on GPUs and TPUs, similar to how deep learning is thriving due to hardware advancements. The ability of transformers to handle sequences with different word orderings without information loss makes them an elegant solution.
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