We study parameter inference in large-scale latent variable models. We first propose an unified treatment of online inference for latent variable models from a non-canonical exponential family, and draw explicit links between several previously proposed frequentist or Bayesian methods. We then propose a novel inference method for the frequentist estimation of parameters, that adapts MCMC methods to online inference of latent variable models with the proper use of local Gibbs sampling. Then, for latent Dirich-let allocation,we provide an extensive set of experiments and comparisons with existing work, where our new approach outperforms all previously proposed methods. In particular, using Gibbs sampling for latent variable inference is superior to variational inference in terms of test log-likelihoods. Moreover, Bayesian inference through variational methods perform poorly, sometimes leading to worse fits with latent variables of higher dimensionality.

Whether it's bad code or bad data, the potential an AI could go off-reservation is very real. Just look at Microsoft's chatbot, Tay, which was programmed to talk like a teenage girl. Less than 24 hours of exposure to Twitter had Tay spewing racist and sexist slurs mixed with conspiracy theories. Naturally, Microsoft pulled the plug and took Tay back to the drawing board. An AI call center could just as easily short-circuit an interaction with a customer if it isn't monitored for customer satisfaction.

I played around with it in 2016 but seemingly all of the API has changed since then. Additionally, Eager seems to be the new hot thing. I have experience with Keras and PyTorch but I find the tutorials on the TF website somewhat lacking. Does anyone have a pointer to some good tutorials that use all the new (at least to me) features like Estimators and Datasets?

February will hopefully be rewarding for sci-fi fans who subscribe to Netflix. In addition to Altered Carbon's debut this week, later in the month we'll finally get a peek at director Duncan Jones' Mute. The movie has been floating around for about as long as Jones has been a filmmaker (he previously directed Moon, Source Code and Warcraft), and now we finally have a look at it. The movie follows a mute bartender caught up in a jam, and features as much neon, mustachioed Paul Rudd and as many flying cars as you'd hope. If this was enough to stoke your curiosity, the movie premieres February 23rd.

Digitization has changed the way we process and analyze information. There is an exponential increase in online availability of information. From web pages to emails, science journals, e-books, learning content, news and social media are all full of textual data. The idea is to create, analyze and report information fast. This is when automated text classification steps up.

Ever since Facebook finally admitted to having a fake news problem, it's been trying to fix it. It hired thousands of people to help block fake ads, pledged to work with third-party fact-checking organizations and is busy building algorithms to detect fake news. But even as it attempts to fight back against fraudulent ads and made-up facts, another potential fake news threat looms on the horizon: Artificially generated fake video. Motherboard recently uncovered a disturbing new trend on Reddit, where users create AI-generated pornographic clips by swapping other people's faces onto porn stars. The outlet first reported on the phenomenon a month ago when Reddit user "deepfakes" posted a video of Gal Gadot's face swapped onto a porn star's body (he's since created more fake porn with other celebrities).

A creepy life-like robot called Erica is set to become a TV news anchor in Japan. According to her creator Hiroshi Ishiguro, the droid is warm and caring, and may soon have an'independent consciousness'. She has been described as so realistic she could'have a soul'. A robot called Erica (pictured) can not only make jokes but also has a'soul', according to her creator. Very few details have been revealed about Erica's new job, however Dr Ishiguro said she will use AI to read news put together by humans.

Would you care if a story you read in a newspaper or online was "written" by a machine rather than a stressed-out hack? Would you even be able to tell the difference? Welcome to the world of "robo journalism" - and it's coming faster than you think. Squirrelled away at the Press Association's (PA) headquarters in London is a small team of journalists and software engineers. They're working on a computer system that can do the work of multiple human beings, picking out interesting local data trends - everything from crime statistics to how many babies are being born out of wedlock.

Hi, this is a nice first paper. How does one interpret figure 7? Test error of 99%? You stated "Our experiments show that E-swish systematically outperforms any other well-known activation function" however you only really compare to Relu. You need to compare it with other options that people have published about. What is the added computational speed of each model because of the more complex form, compared to other activation functions?

Preface: Lately we hear too many news about Artificial Intelligence (AI). Google, IBM, Apple, Microsoft etc. years ago announced "mobile" support. Today, mobile is obvious, and to differentiate, they claim to use "AI". The word "AI" for most people can be only the sci-fi movies AI, since we get too many AI movies too: Her, Ex Machina, … and even "Alien: Covenant" is about a rogue AI, the aliens are secondary. Companies went mobile for real: their services run in cell phones. But, we don't see sci-fi "AI" in any service like Alexa, Cortana, Siri etc.