Igor Mordatch is working to build machines that can carry on a conversation. That's something so many people are working on. In Silicon Valley, chatbot is now a bona fide buzzword. He doesn't deal in the AI techniques that typically reach for language. He's a roboticist who began his career as an animator. He spent time at Pixar and worked on Toy Story 3, in between stints as an academic at places like Stanford and the University of Washington, where he taught robots to move like humans.

ChainerRL is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python using Chainer, a flexible deep learning framework. ChainerRL is tested with Python 2.7 and 3.5.1 . For other requirements, see requirements.txt. ChainerRL contains atari_py as dependencies, and windows users may face error while installing it. This problem is discussed in OpenAI gym issues, and one possible counter measure is to enable "Bash on Ubuntu on Windows" for Windows 10 users.

We explore methods of producing adversarial examples on deep generative models such as the variational autoencoder (VAE) and the VAE-GAN. Deep learning architectures are known to be vulnerable to adversarial examples, but previous work has focused on the application of adversarial examples to classification tasks. Deep generative models have recently become popular due to their ability to model input data distributions and generate realistic examples from those distributions. We present three classes of attacks on the VAE and VAE-GAN architectures and demonstrate them against networks trained on MNIST, SVHN and CelebA. Our first attack leverages classification-based adversaries by attaching a classifier to the trained encoder of the target generative model, which can then be used to indirectly manipulate the latent representation. Our second attack directly uses the VAE loss function to generate a target reconstruction image from the adversarial example. Our third attack moves beyond relying on classification or the standard loss for the gradient and directly optimizes against differences in source and target latent representations. We also motivate why an attacker might be interested in deploying such techniques against a target generative network.

His present superhuman AI agents can see, learn from experience, simulate our world and orchestrate meta-solutions (General-Purpose AI). The international metric consists of the state-of-the-art OpenAI Gym.

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.

Apple has officially joined a group called "Partnership on AI" as a founding member, alongside other major tech firms like Amazon, Facebook, Google, IBM, and Microsoft. Also joining the board of trustees are representatives of non-profit groups, including the American Civil Liberties Union, Association for the Advancement of Artificial Intelligence, MacArthur Foundation, OpenAI, Peterson Institute of International Economics, and UC Berkeley. The stated goals of the partnership are to support best practices for, advance understanding of, and create an open platform for discussion about artificial intelligence. Given Apple's tendency toward secrecy, it will be interesting to see what role it will take in the group. Regardless, considering the promise and risks associated with artificial intelligence, it's good to see tech giants and non-profits coming together in this way.

The Friday afternoon news dump, a grand tradition observed by politicians and capitalists alike, is usually supposed to hide bad news. So it was a little weird that Elon Musk, founder of electric car maker Tesla, and Sam Altman, president of famed tech incubator Y Combinator, unveiled their new artificial intelligence company at the tail end of a weeklong AI conference in Montreal this past December. But there was a reason they revealed OpenAI at that late hour. It wasn't that no one was looking. It was that everyone was looking. When some of Silicon Valley's most powerful companies caught wind of the project, they began offering tremendous amounts of money to OpenAI's freshly assembled cadre of artificial intelligence researchers, intent on keeping these big thinkers for themselves. The last-minute offers--some made at the conference itself--were large enough to force Musk and Altman to delay the announcement of the new startup.

I ask 67 questions to OpenAI Engineer Catherine Olsson as we take a stroll around OpenAI HQ in San Francisco. Catherine graciously agreed to an interview right after the release of OpenAI's Universe. I ask her questions that range from her deepest fears to her favorite Operating System. Please hit that subscribe button if you liked this interview! That's what keeps me going.

Arguably, the open source movement -- the idea that a group of technologists freely contributing their own work and commenting on the work of others, can create a final product that is comparable with anything that a commercial enterprise might create -- has been one of the great innovation catalysts of the technology industry. It's no wonder, then, that a group of Silicon Valley luminaries -- including Elon Musk, Peter Thiel and Reid Hoffman -- have lined up to contribute $1 billion to a new open-source AI project known as OpenAI that is led by Ilya Sutskever, one of the world's top experts in machine learning. For now, we don't really know. The OpenAI website is basically just a single blog post outlining the organization's manifesto and an "About" page detailing all the technologists and engineers working on the project. Thus far, we only have a long announcement from the founding members that they are going to do something amazing.

In the last few years, the world of artificial intelligence has mainly been dominated by large internet companies with huge computing infrastructures like Google and Facebook, or research universities like MIT or Stanford. The non-profit research firm is backed by heavy hitters like co-chairs Elon Musk (of SpaceX and Tesla fame), Y Combinator's Sam Altman, as well as investor Peter Thiel (who worked with Musk at PayPal). They claim to have garnered a billion dollars in private funding, from people like Thiel and Amazon Web Services. "We believe AI should be an extension of individual human wills and, in the spirit of liberty, as broadly and evenly distributed as is possible safely," OpenAI writes in its first blog post, published just a few moments ago. The goal? Make the scope of A.I less narrow.