We often think of Momentum as a means of dampening oscillations and speeding up the iterations, leading to faster convergence. But it has other interesting behavior. It allows a larger range of step-sizes to be used, and creates its own oscillations. This excellent article will give you more details and let you play with momentum through interactive visualizations.
"We used computer vision and deep learning advances such as bi-directional Long Short Term Memory (LSTMs), Connectionist Temporal Classification (CTC), convolutional neural nets (CNNs), and more."
Looking at how the popularity of Deep Learning frameworks, models and optimization algorithms has evolved.
Google shares a few details on its "Collaborative Machine Learning" approach where models are trained in a de-centralized manner. Could be used to collaboratively train models directly on users' smartphones without having to upload personal data.
Chatbots have never been able to empathize. That looks set to change, thanks to a Chinese team that has built a chatbot capable of conveying specific emotions.