The Ledger
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Medlock: SwiftKey chose independent probability distributions over neural networks for tractability
“And we took the first one of these because it's a lot easier and a lot more tractable.”
SwiftKey scraped the entire public internet to train its language models
“We scraped all of the publicly available texts off the internet and dumped it into language specific buckets.”
SwiftKey used Large Hadron Collider grid infrastructure to process language models
“So we, ah, we partnered with the University of Cambridge in the UK to use the European grid, which was put together to analyze the data from the Large Hadron Collider, which is why I show you this picture.”
SwiftKey's initial touch model used Gaussian distributions to map keypresses
“The system that we first built was to use Gaussian distributions to model the interaction of the person with the keyboard surface, and then we can use a linear Gaussian for the space bar, and the others are single point Gaussians.”
SwiftKey continuously retrains its touch model on-device as users type
“We build all this technology that runs on the phone. So while you're tapping on the keyboard, it's retraining via a re-parameterized version of MAP.”