Cybertronian language translator8/16/2023 For example, for analyzing images, we’ll typically use convolutional neural networks or “CNNs.” Vaguely, they mimic the way the human brain processes visual information. Credit: Renanar2 / Wikicommons A typical Convolutional Neural NetworkĪnd since around 2012, we’ve been quite successful at solving vision problems with CNNs, like identifying objects in photos, recognizing faces, and reading handwritten digits. That was unfortunate, because language is the main way we humans communicate.īefore Transformers were introduced in 2017, the way we used deep learning to understand text was with a type of model called a Recurrent Neural Network or RNN that looked something like this: Credit: Wikimedia A typical Recurrent Neural Network (RNN) But for a long time, nothing comparably good existed for language tasks (translation, text summarization, text generation, named entity recognition, etc). Let’s say you wanted to translate a sentence from English to French. An RNN would take as input an English sentence, process the words one at a time, and then, sequentially, spit out their French counterparts. The key word here is “sequential.” In language, the order of words matters and you can’t just shuffle them around.
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