This paper proposes a generative model which builds on ideas from dynamical systems and previous deep learning work like the Kanerva Machine. The main idea is to design and train an architecture that, when unrolled as a dynamical system, has points from the target distribution as attractors. I found the presentation of the model reasonably clear, but thought it suffered from excessive formality. E.g., the description of p(M) could just say that the rows of M are isotropic Gaussian distributions with each row having its own mean and scaled-identity covariance. The references to matrix-variate Gaussians, Kronecker products, vectorization operators, etc. don't contribute to clarity.

"Synergy" Why it's annoying: Every time I say it on the job, I feel like I've failed somehow as a human being. Why not just say: "You and I make a good team." "Value-add" Why it's annoying: It feels like a term you use when you want to sound more important. Why not just say: added value "Drinking the Kool-Aid" Why it's annoying: The saying is "there's no such thing as bad publicity." The origin of this phrase comes from tragedy.

Google is set to let users pay off their debts without lifting a finger. The tech giant has announced a new feature that will allow users to transfer funds to friends or family via Google Assistant. Those living in the US will just say'Ok Google, send $10 to Jane for pizza', and the AI will pull money from a linked debit card and forward it to the recipient. Google announced a new feature that will allow users to transfer funds to friends or family via Google Assistant. Those living in the US will just say'Ok Google, send $25 to Manuel Martinez', and the AI will pull money from a linked debit card and forward it to the recipient Google is rolling out a feature that lets users add a credit or debit card to their Google Account, enabling them to easily purchase goods online without.