I don't think (some) people understand; a slick data annotation tool like this is vastly more useful than the 20th variant of GAN that DeepMind produces :)
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WriteLab | ML Engineer | Berkeley, CA | ONSITE, SALARY: 100K-130K
We at WriteLab (writelab.com) are building ML tools to give immediate writing feedback for students and English language learners. There is plenty of room to impact the product by designing and implementing new features, usually starting with data collection. We use all the good stuff in deep learning and NLP including: SpaCy, scikit-learn, TensorFlow, etc.
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I think spaCy uses perceptrons (essentially a shallow neural network) so it should be faster. Accuracy is pretty similar with SyntaxNet at least on the training data but I'm guessing SyntaxNet works better on long range dependencies.
I don't understand the math completely but it looks like dropout can be derived from a Gaussian prior (approximating the Bernoulli) in a Bayesian context.
One useful tidbit is that you can get prediction intervals from deep learning models by running it forward N times with dropout and take the mean and variance of that distribution (plus another precision term).
How would ELMo work if a neural network needs to be run?