We introduce a new approach to learning in hierarchical latent-variable generative models called the "distributed distributional code Helmholtz machine", which emphasises flexibility and accuracy in the inferential process. In common with the original Helmholtz machine and later variational autoencoder algorithms (but unlike adverserial methods) our approach learns an explicit inference or "recognition" model to approximate the posterior distribution over the latent variables. Unlike in these earlier methods, the posterior representation is not limited to a narrow tractable parameterised form (nor is it represented by samples). To train the generative and recognition models we develop an extended wake-sleep algorithm inspired by the original Helmholtz Machine. This makes it possible to learn hierarchical latent models with both discrete and continuous variables, where an accurate posterior representation is essential. We demonstrate that the new algorithm outperforms current state-of-the-art methods on synthetic, natural image patch and the MNIST data sets.

The process of translation is ambiguous, in that there are typically many valid trans- lations for a given sentence. This gives rise to significant variation in parallel cor- pora, however, most current models of machine translation do not account for this variation, instead treating the prob- lem as a deterministic process. To this end, we present a deep generative model of machine translation which incorporates a chain of latent variables, in order to ac- count for local lexical and syntactic varia- tion in parallel corpora. We provide an in- depth analysis of the pitfalls encountered in variational inference for training deep generative models. Experiments on sev- eral different language pairs demonstrate that the model consistently improves over strong baselines.

Artificial intelligence research organization OpenAI recently released a report that shows the amount of compute power needed for training runs in the largest machine learning systems has increased by 300,000 times since 2012. Because machine learning results improve when given additional computing resources, we'll likely see even greater demands for silicon infrastructure to drive better results. Enterprises are increasingly using machine learning to automate complex problems and analytical tasks. But OpenAI's research shows there's a key challenge ahead: How can enterprises build the infrastructure they need to produce the business results they want when the technical requirements keep changing? First off, enterprises should try to find the least complicated algorithm necessary to solve the business problem at hand.

This is a genuine question! If you look into the code, you'll find they are calling properties on the observation space variables that are passed into the learners that don't exist. I am trying to do policysearch with a dict based observationspace. Nothing suggests that wouldn't be possible. None, None) # None for shape and dtype, since it'll require special handling so ... rewriting the code to be a tuple now.

Demand for compute to train artificial intelligence models has shot up enormously over the past six years and is showing no signs of slowing down. Not for profit research firm OpenAI - which is sponsored by Peter Thiel, Elon Musk, Microsoft and Amazon Web Services, among others - published an analysis that showed the amount of compute used for the largest AI training runs has doubled every three-and-a-half months since 2012. This means compute amounts have grown by more than 300,000 times over the past six years, OpenAI said. In comparison, the well-known Moore's Law, which observed the number of transistors in an integrated circuit would double every year-and-a-half, would yield only a twelve-fold increase in performance over the same period. Part of the reason AI models still have enough compute is because of the use of massively parallel video cards or graphics processing units (GPUs) that can have thousands of cores per unit. Furthermore, over the past two years, optimisations such as huge batch sizes, architecture search and expert iteration using improved and specialised hardware such as Tensor processing units (TPUs) and fast data interconnects have increased past limits for algorithmic parallelism.

We're releasing an analysis showing that since 2012, the amount of compute used in the largest AI training runs has been increasing exponentially with a 3.5 month-doubling time (by comparison, Moore's Law had an 18-month doubling period). Since 2012, this metric has grown by more than 300,000x (an 18-month doubling period would yield only a 12x increase). Improvements in compute have been a key component of AI progress, so as long as this trend continues, it's worth preparing for the implications of systems far outside today's capabilities. The chart shows the total amount of compute, in petaflop/s-days, that was used to train selected results that are relatively well known, used a lot of compute for their time, and gave enough information to estimate the compute used. A petaflop/s-day (pfs-day) consists of performing 1015 neural net operations per second for one day, or a total of about 1020 operations.

This is a talk by Ilya Sutskever for course 6.S099: Artificial General Intelligence. He is the Co-Founder of OpenAI. This class is free and open to everyone. Our goal is to take an engineering approach to exploring possible paths toward building human-level intelligence for a better world.

Reinforcement learning (RL) is hot! It allows programmers to create software agents that learn to take optimal actions to maximize reward, through trying out different strategies in a given environment. This course will take you through all the core concepts in Reinforcement Learning, transforming a theoretical subject into tangible Python coding exercises with the help of OpenAI Gym. The videos will first guide you through the gym environment, solving the CartPole-v0 toy robotics problem, before moving on to coding up and solving a multi-armed bandit problem in Python. As the course ramps up, it shows you how to use dynamic programming and TensorFlow-based neural networks to solve GridWorld, another OpenAI Gym challenge.

Researchers at OpenAI have already proven AI can get really good at video games. Now they are teaching AI how to learn games quickly, like a human would. That's why they've challenged developers to submit their own code for an AI-only Sonic the Hedgehog competition. For more videos, subscribe to Mashable Daily: http://on.mash.to/SubscribeNews Give us a follow: Facebook: https://www.facebook.com/mashable/

We're proposing an AI safety technique which trains agents to debate topics with one another, using a human to judge who wins. We believe that this or a similar approach could eventually help us train AI systems to perform far more cognitively advanced tasks than humans are capable of, while remaining in line with human preferences. We're going to outline this method together with preliminary proof-of-concept experiments and are also releasing a web interface so people can experiment with the technique. The debate method visualized as a game tree, similar to a game like Go but with sentences between debaters for moves and human judgements at leaf nodes. In both debate and Go, the true answer depends on the entire tree, but a single path through the tree chosen by strong agents is evidence for the whole.