Older items first published newest-to-oldest across the feed — page 123 of 123.
The OpenAI team is now 45 people. Together, we’re pushing the frontier of AI capabilities—whether by validating novel ideas, creating new software systems, or deploying machine learning on robots.
PixelCNN++: Improving the PixelCNN with discretized logistic mixture likelihood and other modifications.
Reinforcement learning algorithms can break in surprising, counterintuitive ways. In this post we’ll explore one failure mode, which is where you misspecify your reward function.
We’re releasing Universe, a software platform for measuring and training an AI’s general intelligence across the world’s supply of games, websites and other applications.
We’re working with Microsoft to start running most of our large-scale experiments on Azure.
#Exploration: A study of count-based exploration for deep reinforcement learning.
On the quantitative analysis of decoder-based generative models.
A connection between generative adversarial networks, inverse reinforcement learning, and energy-based models.
RL²: Fast reinforcement learning via slow reinforcement learning.
Extensions and limitations of the neural GPU.
Semi-supervised knowledge transfer for deep learning from private training data.
Last week we hosted over a hundred and fifty AI practitioners in our offices for our first self-organizing conference on machine learning.
Transfer from simulation to real world through learning deep inverse dynamics model.
Deep learning is an empirical science, and the quality of a group’s infrastructure is a multiplier on progress. Fortunately, today’s open-source ecosystem makes it possible for anyone to build...
The latest information about the Unconference is now available at the Unconference wiki, which will be periodically updated with more information for attendees.
Impactful scientific work requires working on the right problems—problems which are not just interesting, but whose solutions matter.
We (along with researchers from Berkeley and Stanford) are co-authors on today’s paper led by Google Brain researchers, Concrete Problems in AI Safety. The paper explores many research problems...
OpenAI’s mission is to build safe AI, and ensure AI’s benefits are as widely and evenly distributed as possible.
This post describes four projects that share a common theme of enhancing or using generative models, a branch of unsupervised learning techniques in machine learning. In addition to describing...
Adversarial training methods for semi-supervised text classification.
We’re releasing the public beta of OpenAI Gym, a toolkit for developing and comparing reinforcement learning (RL) algorithms. It consists of a growing suite of environments (from simulated robots...
We’ve had some fantastic people join over the past few months (and we’re still hiring). Welcome, everyone!
Weight normalization: A simple reparameterization to accelerate training of deep neural networks.
OpenAI is a non-profit artificial intelligence research company. Our goal is to advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need...
