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.

AI isn't going to be a threat to humanity because it's evil or cruel, AI will be a threat to humanity because we haven't properly explained what it is we want it to do. Consider the classic "paperclip maximizer" thought experiment, in which an all-powerful AI is told, simply, "make paperclips." The AI, not constrained by any human morality or reason, does so, eventually transforming all resources on Earth into paperclips, and wiping out our species in the process. As with any relationship, when talking to our computers, communication is key. That's why a new piece of research published yesterday by Google's DeepMind and the Elon Musk-funded OpenAI institute is so interesting. It offers a simple way for humans to give feedback to AI systems -- crucially, without the instructor needing to know anything about programming or artificial intelligence.

One step towards building safe AI systems is to remove the need for humans to write goal functions, since using a simple proxy for a complex goal, or getting the complex goal a bit wrong, can lead to undesirable and even dangerous behavior. In collaboration with DeepMind's safety team, we've developed an algorithm which can infer what humans want by being told which of two proposed behaviors is better. We present a learning algorithm that uses small amounts of human feedback to solve modern RL environments. Machine learning systems with human feedback have been explored before, but we've scaled up the approach to be able to work on much more complicated tasks. Our algorithm needed 900 bits of feedback from a human evaluator to learn to backflip -- a seemingly simple task which is simple to judge but challenging to specify.