We introduce a method for learning the dynamics of complex nonlinear systems based on deep generative models over temporal segments of states and actions. Unlike dynamics models that operate over individual discrete timesteps, we learn the distribution over future state trajectories conditioned on past state, past action, and planned future action trajectories, as well as a latent prior over action trajectories. Our approach is based on convolutional autoregressive models and variational autoencoders. It makes stable and accurate predictions over long horizons for complex, stochastic systems, effectively expressing uncertainty and modeling the effects of collisions, sensory noise, and action delays. The learned dynamics model and action prior can be used for end-to-end, fully differentiable trajectory optimization and model-based policy optimization, which we use to evaluate the performance and sample-efficiency of our method.

Google is developing a new system designed to prevent artificial intelligence from going rogue and clashing with humans. It's an idea that has been explored by a multitude of sci-fi films, and has grown into a genuine fear for a number of people. Google is now hoping to tackle the issue by encouraging machines to work in a certain way. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar. Japan's On-Art Corp's CEO Kazuya Kanemaru poses with his company's eight metre tall dinosaur-shaped mechanical suit robot'TRX03' and other robots during a demonstration in Tokyo, Japan Japan's On-Art Corp's eight metre tall dinosaur-shaped mechanical suit robot'TRX03' performs during its unveiling in Tokyo, Japan Singulato Motors co-founder and CEO Shen Haiyin poses in his company's concept car Tigercar P0 at a workshop in Beijing, China A picture shows Singulato Motors' concept car Tigercar P0 at a workshop in Beijing, China Connected company president Shigeki Tomoyama addresses a press briefing as he elaborates on Toyota's "connected strategy" in Tokyo.

There's nothing new about worrying that superintelligent machines may endanger humanity, but the idea has lately become hard to avoid. A spurt of progress in artificial intelligence as well as comments by figures such as Bill Gates--who declared himself "in the camp that is concerned about superintelligence"--have given new traction to nightmare scenarios featuring supersmart software. Now two leading centers in the current AI boom are trying to bring discussion about the dangers of smart machines down to Earth. Google's DeepMind, the unit behind the company's artificial Go champion, and OpenAI, the nonprofit lab funded in part by Tesla's Elon Musk, have teamed up to make practical progress on a problem they argue has attracted too many headlines and too few practical ideas: How do you make smart software that doesn't go rogue? "If you're worried about bad things happening, the best thing we can do is study the relatively mundane things that go wrong in AI systems today," says Dario Amodei, a curly-haired researcher on OpenAI's small team working on AI safety.

There's nothing new about worrying that superintelligent machines may endanger humanity, but the idea has lately become hard to avoid. A spurt of progress in artificial intelligence as well as comments by figures such as Bill Gates--who declared himself "in the camp that is concerned about superintelligence"--have given new traction to nightmare scenarios featuring supersmart software. Now two leading centers in the current AI boom are trying to bring discussion about the dangers of smart machines down to Earth. Google DeepMind, the unit behind the company's artificial Go champion, and OpenAI, the nonprofit lab funded in part by Tesla's Elon Musk, have teamed up to make practical progress on a problem they argue has attracted too many headlines and too few practical ideas: How do you make smart software that doesn't go rogue? "If you're worried about bad things happening, the best thing we can do is study the relatively mundane things that go wrong in AI systems today," says Dario Amodei, a curly-haired researcher on OpenAI's small team working on AI safety.

We're open-sourcing a high-performance Python library for robotic simulation using the MuJoCo engine, developed over our past year of robotics research. This library is one of our core tools for deep learning robotics research, which we've now released as a major version of mujoco-py, our Python 3 bindings for MuJoCo. Many methods in trajectory optimization and reinforcement learning (like LQR, PI2, and TRPO) benefit from being able to run multiple simulations in parallel. Naive usage of the new version's MjSimPool interface shows a 400% speedup over the old, and still about 180% over an optimized and restricted usage pattern using Python's multiprocessing package to gain the same level of parallelism. The majority of the speedup comes from reduced access times to the various MuJoCo data structures.

Neural network based generative models with discriminative components are a powerful approach for semi-supervised learning. However, these techniques a) cannot account for model uncertainty in the estimation of the model's discriminative component and b) lack flexibility to capture complex stochastic patterns in the label generation process. To avoid these problems, we first propose to use a discriminative component with stochastic inputs for increased noise flexibility. We show how an efficient Gibbs sampling procedure can marginalize the stochastic inputs when inferring missing labels in this model. Following this, we extend the discriminative component to be fully Bayesian and produce estimates of uncertainty in its parameter values. This opens the door for semi-supervised Bayesian active learning.

Artificial Intelligence (AI) and Machine Learning (ML) are transforming entire industries because of higher performance and faster time to market. Part of the success is due to researchers creating and open sourcing datasets, frameworks, and algorithms (e.g., ImageNet, Caffe). Current leaders are following suit by opening up their own developments (e.g., DeepMind Lab and Sonnet, OpenAI Gym and Universe). Despite this generosity, operating and developing on these components still requires large amounts of expertise, vast computational resources, and lots of money to obtain and maintain. Jack Clark of OpenAI believes that this situation seems to benefit large-scale cloud providers like Amazon, Microsoft, and Google.

Earlier this year Tesla announced engineer Chris Lattner would leave Apple and lead its Autopilot engineering team, but just five months later he is departing. Lattner, the designer of Apple's Swift programming language, tweeted "Turns out that Tesla isn't a good fit for me after all," while Tesla announced it has hired Andrej Karpathy, "one of the world's leading experts in computer vision and deep learning." He will become the company's Director of AI and Autopilot Vision, reporting directly to CEO Elon Musk, who he may know well from his previous job as a research scientist at the Musk-backed OpenAI. Andrej Karpathy, one of the world's leading experts in computer vision and deep learning, is joining Tesla as Director of AI and Autopilot Vision, reporting directly to Elon Musk. Andrej has worked to give computers vision through his work on ImageNet, as well as imagination through the development of generative models, and the ability to navigate the internet with reinforcement learning.

That said, I would have thought he was a bit young for a "Director" role. Most other big tech companies have directors of his professors' generation. Not doubting his skill, ability to communicate, or his passion, it just seems a pretty surprising move from a large company. Has Andrej ever managed a team before (beyond running a course or supervising some students)? And does he have any serious SDC experience?

We present a probabilistic framework for overlapping community discovery and link prediction for relational data, given as a graph. The proposed framework has: (1) a deep architecture which enables us to infer multiple layers of latent features/communities for each node, providing superior link prediction performance on more complex networks and better interpretability of the latent features; and (2) a regression model which allows directly conditioning the node latent features on the side information available in form of node attributes. Our framework handles both (1) and (2) via a clean, unified model, which enjoys full local conjugacy via data augmentation, and facilitates efficient inference via closed form Gibbs sampling. Moreover, inference cost scales in the number of edges which is attractive for massive but sparse networks. Our framework is also easily extendable to model weighted networks with count-valued edges. We compare with various state-of-the-art methods and report results, both quantitative and qualitative, on several benchmark data sets.