The Ledger
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Medlock: 2015 AI technology cannot abstract concepts across domains
“This is what the human brain does so incredibly well, and what our technology today is currently almost entirely unable to do.”
Ben Medlock: Touchscreen typing is an inference problem, not a mechanical one
“This is a virtual keyboard, and the problem of typing was now not a mechanical problem. It's an inference problem, and that means that there is a degree of uncertainty.”
Medlock: SwiftKey is used on close to a billion devices worldwide
“We designed this in 2010, and it's now used on close to a billion devices worldwide.”
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.”
Medlock: N-gram models are extremely hard to beat in voice recognition
“If you've done anything in voice recognition, you will know that it's really, really hard to beat Ngram models.”
Medlock: Neural network language models are a highly promising research avenue
“Another possibility is to move away from engrams and towards neural network-based language models, which is a really promising, ah, avenue of research at the moment.”
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.”
Medlock: Online profiles offer the best seed for personal predictive text models
“To get a really strong predictive model, you need to know how an individual user uses language, and online profiles give you an ideal way of getting a seed for this.”
Ben Medlock predicted physical keyboards would dominate programming until AGI arrives
“I think for things like programming, it's really hard to see it changing significantly. And it's sort of almost It's very difficult to imagine what would actually do a much better job of this outside of a general AI that's better at programming than we are. So…”
Ben Medlock: Early AI symbolic logic models failed to scale
“And the systems that, that we tried to build to begin with were based on scaled down worlds where we could develop symbolic logics to manipulate sort of simplified objects within these worlds and just failed miserably to scale up to the real world.”
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.”
SwiftKey saved users 15 trillion keystrokes out of 50 trillion typed characters
“We've had about 50 trillion characters written through the software, and we saved about 15 trillion keystrokes”
SwiftKey built Stephen Hawking's upgraded communication technology
“We just recently built Stephen Hawking's new communication technology”