The first run of our Retro Contest—exploring the development of algorithms that can generalize from previous experience—is now complete.

We’ve obtained state-of-the-art results on a suite of diverse language tasks with a scalable, task-agnostic system, which we’re also rele...

We’re now accepting applications for the next cohort of OpenAI Fellows, a program which offers a compensated 6-month apprenticeship in AI...

We’re releasing the full version of Gym Retro, a platform for reinforcement learning research on games. This brings our publicly-released...

We’re releasing an analysis showing that since 2012, the amount of compute used in the largest AI training runs has been increasing expon...

We’re proposing an AI safety technique which trains agents to debate topics with one another, using a human to judge who wins.

We’re releasing an experimental metalearning approach called Evolved Policy Gradients, a method that evolves the loss function of learnin...

We’re launching a transfer learning contest that measures a reinforcement learning algorithm’s ability to generalize from previous experi...

On March 3rd, we hosted our first hackathon with 100 members of the artificial intelligence community.

We’ve developed a simple meta-learning algorithm called Reptile which works by repeatedly sampling a task, performing stochastic gradient...

We’re providing 6–10 stipends and mentorship to individuals from underrepresented groups to study deep learning full-time for 3 months an...

We’re releasing eight simulated robotics environments and a Baselines implementation of Hindsight Experience Replay, all developed for ou...

Come to OpenAI’s office in San Francisco’s Mission District for talks and a hackathon on Saturday, March 3rd.

We’re excited to welcome new donors to OpenAI.

We’ve co-authored a paper that forecasts how malicious actors could misuse AI technology, and potential ways we can prevent and mitigate ...

We’ve designed a method that encourages AIs to teach each other with examples that also make sense to humans. Our approach automatically ...