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 :)
We have
- 1 ML/NLP engineer (me)
- 1 CEO w/ a linguistics background
- 1 founder is an English professor at Berkeley
- 2 with previous experience teaching English
- 1 Berkeley PhD in Deep Learning / NLP advising us
- 1 Berkeley PhD in English helping us categorize writing issues
We're working on assessing writing quality too. Get in touch!
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.
Strong background in machine learning and experience deploying ML models in production is a must. NLP and DL experience is a strong plus.
Interview process:
initial video call with NLP engineer
onsite interview to discuss previous experience and go through an NLP / ML problem
lunch with CEO
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).
I actually like the keyboard. I tend to glide over the keyboard to find the right keys, minimizing wrist movement. Combination of feel, sound, flatness are somehow very satisfying.
But! The butterfly keys are stiffer for smaller keys so ironically(?), the Fn keys and arrow keys are hard to find and press. I actually use the arrow keys so I find using the Ctrl shortcuts more often for moving the cursor around.
I feel like a touch screen on the trackpad would've made more sense. Since it's so large there's room to be creative with on-screen shortcuts, dragging sliders, choosing an emoji :), etc. You can still keep all the keyboard shortcuts you need and not need to look at 3 things at once (screen, keyboard/trackpad, touch bar).
1 checks for compatibility of words in a sentence (essentially popularity)
2 give example sentences for a certain word
3 word suggestions depending on context.
Language models would be a decent way to check popularity though it would be noisy. Sentence level rewrites would be hard unless you make it template driven.
How would ELMo work if a neural network needs to be run?