OpenAI, the billion-dollar San Francisco artificial intelligence lab backed by Tesla CEO Elon Musk, just unveiled a new virtual world. It's called Universe, and it's a virtual world like no other. It's a place where AI can learn to do just about anything. Other AI labs have built similar worlds where AI agents can learn on their own. Researchers at the University of Alberta offer the Atari Learning Environment, where agents can learn to play old Atari games like Breakout and Space Invaders. Microsoft offers Malmo, based on the game Minecraft.

Understanding the nature of dark energy, the mysterious force driving the accelerated expansion of the Universe, is a major challenge of modern cosmology. The next generation of cosmological surveys, specifically designed to address this issue, rely on accurate measurements of the apparent shapes of distant galaxies. However, shape measurement methods suffer from various unavoidable biases and therefore will rely on a precise calibration to meet the accuracy requirements of the science analysis. This calibration process remains an open challenge as it requires large sets of high quality galaxy images. To this end, we study the application of deep conditional generative models in generating realistic galaxy images. In particular we consider variations on conditional variational autoencoder and introduce a new adversarial objective for training of conditional generative networks. Our results suggest a reliable alternative to the acquisition of expensive high quality observations for generating the calibration data needed by the next generation of cosmological surveys.

Exploration in multi-task reinforcement learning is critical in training agents to deduce the underlying MDP. Many of the existing exploration frameworks such as $E^3$, $R_{max}$, Thompson sampling assume a single stationary MDP and are not suitable for system identification in the multi-task setting. We present a novel method to facilitate exploration in multi-task reinforcement learning using deep generative models. We supplement our method with a low dimensional energy model to learn the underlying MDP distribution and provide a resilient and adaptive exploration signal to the agent. We evaluate our method on a new set of environments and provide intuitive interpretation of our results.

Microsoft is furthering its mission to democratize artificial intelligence. The company is teaming up with the AI research organization OpenAI to advance the acceleration of the technology. "We've made major strides in artificial intelligence just in the past five years, achieving milestones many people who have devoted their lives to the field wouldn't have thought possible," wrote Harry Shum executive vice president of Microsoft's artificial intelligence and research group, in a post. "Now we have the opportunity to help our partners and customers use these breakthroughs to achieve their goals." OpenAI will utilize Microsoft's Azure cloud service to conduct its large-scale AI and deep learning experiments.

Reuters – OpenAI, the non-profit artificial intelligence research firm backed by Tesla Motors Inc's Elon Musk and other prominent tech executives, has signed an agreement to run most of its large-scale experiments on Microsoft Corp's flagship cloud service, Azure. OpenAI will use Azure for its experiments in deep learning and AI, and Microsoft will collaborate with the company on advancing research and creating new tools and technologies. Musk, along with venture capitalist Sam Altman, co-chairs OpenAI, whose backers apart from Musk include Amazon Web Services and tech investor Peter Thiel and LinkedIn Corp co-founder Reid Hoffman. OpenAI is an early adopter of Microsoft's Azure N-Series Virtual Machines service, which will be generally available from December. These cloud-computing services, which are powered by Nvidia Corp graphics chips, are designed for the most intensive computing workloads, including deep learning and simulations.

First of all it might be useful to explain what OpenAI Gym actually does: OpenAI Gym aims to provide an easy environment to develop and test reinforcement learning algorithms. To be clear, OpenAI Gym doesn't power any algorithms itself, leaving it up to more specialised packages like TensorFlow or Theano. So what makes this the ultimate geek toy for AI-researchers? Well, this is because of the many environments OpenAI Gym provides, one of them being the'atari' environment. That's right, you can test the performance of your reinforcement learning algorithms on a variety of different atari games and what's more, you can automatically upload the performance of your algorithms and compare them to other people's approaches.

Musk has been vocal in his warnings about the potential downside of the rise of the robots. He has invested millions in OpenAI, a project to ensure that artificial intelligence benefits mankind, rather than destroys it, and last week he said that it was only a matter of time before AI was used to take down the internet. "There is a pretty good chance we end up with a universal basic income, or something like that, due to automation," Musk told CNBC. "I am not sure what else one would do. I think that is what would happen."

On the very eve itself of Microsoft's third major developer event this year -- Connect() 2016 in New York -- the firm has taken another directly open stride in its Artificial Intelligence (AI) efforts. Microsoft has announced a new partnership with OpenAI, the nonprofit research organisation co-founded by Elon Musk, Sam Altman, Greg Brockman and Ilya Sutskever. In line with this news, Microsoft also introduced a selection of Azure cloud releases designed to drive what it hopes will be advances in AI. But why did the firm do any of this? "We want to democratize AI," asserts the usually fairly gutsy non-marketing official Microsoft blog channel. Microsoft's says its partnership with OpenAI will be focused on making'significant contributions' to advancing the field of AI.

Deep generative models (DGMs) are effective on learning multilayered representations of complex data and performing inference of input data by exploring the generative ability. However, it is relatively insufficient to empower the discriminative ability of DGMs on making accurate predictions. This paper presents max-margin deep generative models (mmDGMs) and a class-conditional variant (mmDCGMs), which explore the strongly discriminative principle of max-margin learning to improve the predictive performance of DGMs in both supervised and semi-supervised learning, while retaining the generative capability. In semi-supervised learning, we use the predictions of a max-margin classifier as the missing labels instead of performing full posterior inference for efficiency; we also introduce additional max-margin and label-balance regularization terms of unlabeled data for effectiveness. We develop an efficient doubly stochastic subgradient algorithm for the piecewise linear objectives in different settings. Empirical results on various datasets demonstrate that: (1) max-margin learning can significantly improve the prediction performance of DGMs and meanwhile retain the generative ability; (2) in supervised learning, mmDGMs are competitive to the best fully discriminative networks when employing convolutional neural networks as the generative and recognition models; and (3) in semi-supervised learning, mmDCGMs can perform efficient inference and achieve state-of-the-art classification results on several benchmarks.

Microsoft has announced a new partnership with OpenAI, the $1 billion artificial intelligence research nonprofit cofounded by Tesla CEO Elon Musk and Y Combinator President Sam Altman. Under the terms of the partnership, OpenAI will use the Microsoft Azure cloud computing service as the "preferred" place to do its AI experiments. In return, Microsoft gets increased access to OpenAI's deep bench of robotics and experts, making Azure a better place for building AI-powered software. "You want to get a good feedback loop going," says Microsoft Executive VP of Cloud and Enterprise Scott Guthrie. "That helps you ultimately build a better platform."