Deep generative models parameterized by neural networks have recently achieved state-of-the-art performance in unsupervised and semi-supervised learning. We extend deep generative models with auxiliary variables which improves the variational approximation. The auxiliary variables leave the generative model unchanged but make the variational distribution more expressive. Inspired by the structure of the auxiliary variable we also propose a model with two stochastic layers and skip connections. Our findings suggest that more expressive and properly specified deep generative models converge faster with better results. We show state-of-the-art performance within semi-supervised learning on MNIST, SVHN and NORB datasets.

Android N notifications are getting a new look to help provide a better user experience. They now have a fresh look, improved for custom views, and expanded functionality in the forms of Direct Reply. The default look and feel of notifications has changed, and now the fields around the notifications have been collapsed into a new header row with the app's icon and name anchoring the notification, according to Android developer advocate Ian Lake on the Android Developers Blog. This makes the title, text and large icon have a lot of space, and now the notifications are slightly larger and easier to read. Notification actions have also received a redesign and are now in a visually separate bar below the notification, according to the blog.

Elon Musk is getting involved in AI, too, by supporting OpenAI, a non-profit research company focused on advancing digital intelligence for the common good. "Elon Musk has launched the OpenAI project with a star-studded list of backers – Palantir CEO Peter Thiel, LinkedIn founder Reid Hoffman and Y Combinator president Sam Altman," says an impressed Jones. OpenAI is headed up by machine learning expert Ilya Sutskever, ex-Google Brain Team member, and has just opened the OpenAI Gym in beta to help developers working with'reinforced learning', a type of machine learning that's central to AI. Essentially, it's about getting software to alter its behaviour in a dynamic environment in order to get a reward (you can't give Siri a biscuit every time she'found this on the web'). The arrival of AI means a changing of the guard in the tech industry, with disruption, innovation – and the complete domination of the cloud. Expertise, not investment, will become king.

Today, Google's newest machine learning project released its first piece of generated art, a 90-second piano melody created through a trained neural network, provided with just four notes up front. The drums and orchestration weren't generated by the algorithm, but added for emphasis after the fact. It's the first tangible product of Google's Magenta program, which is designed to put Google's machine learning systems to work creating art and music systems. The program was first announced at Moogfest last week. Along with the melody, Google published a new blog post delving into Magenta's goals, offering the most detail yet on Google's artistic ambitions.

My favorite post in a while, look forward to seeing more. I actually have a question for you (or anyone else who reads this) I have some experience with Box2D and other stuff OpenAI/Gym uses and I'd love to create some games/sims that people could train there AI's for, is there a good guide line I could use when making these?

You've probably heard about OpenAI -- a new nonprofit to focus on artificial intelligence research that is good for humanity. So here we have a case of knowledgeable people recognizing a threat and deciding that the way forward is to create a nonprofit organization. This moment has some historical precedent. In 1955, Albert Einstein, Bertrand Russell and several other scientists got together and issued a manifesto about the dangers of nuclear technologies. It would launch decades of Pugwash conferences and the anti-nuclear movement. The founders of OpenAI also issued a manifesto.

Gym-TORCS is the reinforcement learning (RL) environment in TORCS domain with OpenAI-gym-like interface. TORCS is the open-rource realistic car racing simulator recently used as RL benchmark task in several AI studies. Gym-TORCS is the python wrapper of TORCS for RL experiment with the simple interface (similar, but not fully) compatible with OpenAI-gym environments. The current implementaion is for only the single-track race in practie mode. If you want to use multiple tracks or other racing mode (quick race etc.), you may need to modify the environment, "autostart.sh" or the race configuration file using GUI of TORCS.

"Because of AI's surprising history, it's hard to predict when human-level AI might come within reach," the group, OpenAI, said in a statement on their website. "When it does, it'll be important to have a leading research institution which can prioritise a good outcome for all over its own self-interest." The project is a non-profit effort and their main objective is to save humanity, not generate income. "OpenAI is a non-profit artificial intelligence research company. Our goal is to advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return," the OpenAI website explains.

Humans have an impressive ability to reason about new concepts and experiences from just a single example. In particular, humans have an ability for one-shot generalization: an ability to encounter a new concept, understand its structure, and then be able to generate compelling alternative variations of the concept. We develop machine learning systems with this important capacity by developing new deep generative models, models that combine the representational power of deep learning with the inferential power of Bayesian reasoning. We develop a class of sequential generative models that are built on the principles of feedback and attention. These two characteristics lead to generative models that are among the state-of-the art in density estimation and image generation. We demonstrate the one-shot generalization ability of our models using three tasks: unconditional sampling, generating new exemplars of a given concept, and generating new exemplars of a family of concepts. In all cases our models are able to generate compelling and diverse samples---having seen new examples just once---providing an important class of general-purpose models for one-shot machine learning.

OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms. It makes no assumptions about the structure of your agent, and is compatible with any numerical computation library, such as TensorFlow or Theano. You can use it from Python code, and soon from other languages. To get started, you'll need to have Python 2.7 or Python 3.5. You can later run pip install -e .[all] to do a full install (this requires cmake and a recent pip version).