Technology News, Trends, And Future-Ready Signals

Spinning Up in Deep RL: Workshop review

On February 2, we held our first Spinning Up Workshop as part of our new education initiative at OpenAI.

AI safety needs social scientists

We’ve written a paper arguing that long-term AI safety research needs social scientists to ensure AI alignment algorithm...

Better language models and their implications

We’ve trained a large-scale unsupervised language model which generates coherent paragraphs of text, achieves state-of-t...

Computational limitations in robust classification and win-win results

OpenAI Fellows Summer 2018: Final projects

Our first cohort of OpenAI Fellows has concluded, with each Fellow going from a machine learning beginner to core OpenAI...

How AI training scales

We’ve discovered that the gradient noise scale, a simple statistical metric, predicts the parallelizability of neural ne...

Quantifying generalization in reinforcement learning

We’re releasing CoinRun, a training environment which provides a metric for an agent’s ability to transfer its experienc...

Spinning Up in Deep RL

We’re releasing Spinning Up in Deep RL, an educational resource designed to let anyone learn to become a skilled practit...

Learning concepts with energy functions

We’ve developed an energy-based model that can quickly learn to identify and generate instances of concepts, such as nea...

Plan online, learn offline: Efficient learning and exploration via model-based c...

Reinforcement learning with prediction-based rewards

We’ve developed Random Network Distillation (RND), a prediction-based method for encouraging reinforcement learning agen...

Learning complex goals with iterated amplification

We’re proposing an AI safety technique called iterated amplification that lets us specify complicated behaviors and goal...