OpenAI Five Finals
We’ll be holding our final live event for OpenAI Five at 11:30am PT on April 13.
Topics: Models
We’ll be holding our final live event for OpenAI Five at 11:30am PT on April 13.
Topics: Models
We’ve made progress towards stable and scalable training of energy-based models (EBMs) resulting in better sample quality and generalization ability than existing models. Generation in EBMs spends more compute to continually refine its answers and doing so...
Topics: ModelsInfrastructure
Entities: ModelsInfrastructure
Our class of eight scholars (out of 550 applicants) brings together collective expertise in literature, philosophy, cell biology, statistics, economics, quantum physics, and business innovation.
Topics: Models
We’ve created activation atlases (in collaboration with Google researchers), a new technique for visualizing what interactions between neurons can represent. As AI systems are deployed in increasingly sensitive contexts, having a better understanding of...
Entities: Google
We’re releasing a Neural MMO, a massively multiagent game environment for reinforcement learning agents. Our platform supports a large, variable number of agents within a persistent and open-ended task. The inclusion of many agents and species leads to...
Topics: Agents
Entities: Agents
On February 2, we held our first Spinning Up Workshop as part of our new education initiative at OpenAI.
Topics: Models
We’ve written a paper arguing that long-term AI safety research needs social scientists to ensure AI alignment algorithms succeed when actual humans are involved. Properly aligning advanced AI systems with human values requires resolving many uncertainties...
We’ve trained a large-scale unsupervised language model which generates coherent paragraphs of text, achieves state-of-the-art performance on many language modeling benchmarks, and performs rudimentary reading comprehension, machine translation, question...
Topics: ModelsInfrastructure
Entities: ModelsInfrastructure
We continue the study of statistical/computational tradeoffs in learning robust classifiers, following the recent work of Bubeck, Lee, Price and Razenshteyn who showed examples of classification tasks where (a) an efficient robust classifier exists, in the...
Our first cohort of OpenAI Fellows has concluded, with each Fellow going from a machine learning beginner to core OpenAI contributor in the course of a 6-month apprenticeship.
Topics: Models
We’ve discovered that the gradient noise scale, a simple statistical metric, predicts the parallelizability of neural network training on a wide range of tasks. Since complex tasks tend to have noisier gradients, increasingly large batch sizes are likely to...
Topics: Infrastructure
Entities: Infrastructure