Unsupervised learning of probabilistic models is a central yet challenging problem in machine learning. Specifically, designing models with tractable learning, sampling, inference and evaluation is crucial in solving this task. We extend the space of such models using real-valued non-volume preserving (real NVP) transformations, a set of powerful, stably invertible, and learnable transformations, resulting in an unsupervised learning algorithm with exact log-likelihood computation, exact and efficient sampling, exact and efficient inference of latent variables, and an interpretable latent space. We demonstrate its ability to model natural images on four datasets through sampling, log-likelihood evaluation, and latent variable manipulations.

Tesla chief executive and OpenAI founder Elon Musk suggested last week that humanity might stave off irrelevance from the rise of the machines by merging with the machines and becoming cyborgs. This doubt is not only due to hardware limitations; it is also to do with the role the human brain would play in the match-up. Musk's thesis is straightforward: that sufficiently advanced interfaces between brain and computer will enable humans to massively augment their capabilities by being better able to leverage technologies such as machine learning and deep learning. But the exchange goes both ways. Brain-machine interfaces may help the performance of machine learning algorithms by having humans "fill in the gaps" for tasks that the algorithms are currently bad at, like making nuanced contextual decisions.

Researchers from Baidu's Silicon Valley AI Lab (SVAIL) have adapted a well-known HPC communication technique to boost the speed and scale of their neural network training and now they are sharing their implementation with the larger deep learning community. The technique, a modified version of the OpenMPI algorithm "ring all-reduce," is being used at Baidu to parallelize the training of their speech recognition model, Deep Speech 2, across many GPU nodes. The two pieces of software Baidu is announcing today are the baidu-allreduce C library, as well as a patch for TensorFlow, which allows people who have already modeled in TensorFlow to compile this new version and use it for parallelizing across many devices. The codes are available on GitHub. Baidu's SVAIL team developed the approach about two years ago for their internal deep learning framework, named Gene and Majel (in tribute to the famous Star Trek creator and the actress who voiced the onboard computer interfaces for the series).

News concerning Artificial Intelligence (AI) abounds again. The progress with Deep Learning techniques are quite remarkable with such demonstrations of self-driving cars, Watson on Jeopardy, and beating human Go players. This rate of progress has led some notable scientists and business people to warn about the potential dangers of AI as it approaches a human level. Exascale computers are being considered that would approach what many believe is this level. However, there are many questions yet unanswered on how the human brain works, and specifically the hard problem of consciousness with its integrated subjective experiences.

Watch out world, machines are beating flesh and blood opponents in a variety of different contests. There was Watson, a smarty pants question answering system developed by IBM that took on and defeated the world's best Jeopardy opponents. Then Google's DeepMind division built an artificial intelligence program called AlphaGo that became the world's top Go player. And now there's an AI that seemingly has no equal in Nintendo's Super Smash Bros. game. The AI team was led by Vlad Firoiu at Massachusetts Institute of Technology (MIT).

We believe that there exist classic deep learning papers which are worth reading regardless of their application domain. Rather than providing overwhelming amount of papers, We would like to provide a curated list of the awesome deep learning papers which are considered as must-reads in certain research domains. Before this list, there exist other awesome deep learning lists, for example, Deep Vision and Awesome Recurrent Neural Networks. Also, after this list comes out, another awesome list for deep learning beginners. Although the Roadmap List includes lots of important deep learning papers, it feels overwhelming for me to read them all.

Google announced today that Google Assistant will start rolling out to devices running Android 6.0 (Marshmallow) and 7.0 (Nougat) later this week. Previously, the Assistant was only available on Google's Pixel phones, Home personal assistant, Allo messaging app and Android wear devices. Google is at the forefront of machine and deep-learning based natural language processing which they have used to power their vaunted speech recognition system. Google Assistant leverages this research to enable two-way, context-sensitive, conversational interactions between the user and the device. The Assistant syncs across devices and apps like Allo and Google Calendar and it allows voice-control of enabled smart-home devices.

Provide "AI for everyone" from large-scale processing to edge device by integrating Fujitsu's unique technologies owned from HPC to smartphones.

Editor's note: This is the first chapter of a book I'm working on called Demystifying Artificial Intelligence. The goal of the book is to demystify what modern AI is and does for a general audience. So something to smooth the transition between AI fiction and highly mathematical descriptions of deep learning. I'm developing the book over time - so if you buy the book on Leanpub know that there is only one chaper in there so far, but I'll be adding more over the next few weeks and you get free updates. The cover of the book was inspired by this amazing tweet by Twitter user @notajf. Feedback is welcome and encouraged!

In this series of exploratory blog posts, we explore the relationship between recurrent neural networks (RNNs) and IoT data. The article is written by Ajit Jaokar, Dr Paul Katsande and Dr Vinay Mehendiratta as part of the Data Science for Internet of Things practitioners course. RNNs are already used for Time series analysis. Because IoT problems can often be modelled as a Time series, RNNs could apply to IoT data. In this multi-part blog, we first discuss Time series applications and then discuss how RNNs could apply to Time series applications.