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 working with Microsoft to start running most of our large-scale experiments on Azure.
Last week we hosted over a hundred and fifty AI practitioners in our offices for our first self-organizing conference on machine learning.
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 great deep learning infrastructure.
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 around ensuring that modern machine learning systems operate as intended.
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 our work, this post will tell you a bit more about generative models: what they are, why they are importan…
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 to Atari games), and a site for comparing and reproducing results.
