Codex is now powering 70 different applications across a variety of use cases through the OpenAI API.

Early users have created over 3 million images to date and helped us improve our safety processes. We’re excited to begin adding up to 1,000 new users from our waitlist each week.

We’re happy to announce several executive role changes that reflect our recent progress and will ensure continued momentum toward our next major milestones.

Goodhart’s law famously says: “When a measure becomes a target, it ceases to be a good measure.” Although originally from economics, it’s something we have to grapple with at OpenAI when figuring out how to optimize objectives that are difficult or costly to measure.

We’ve released new versions of GPT-3 and Codex which can edit or insert content into existing text, rather than just completing existing text.

We describe our latest thinking in the hope of helping other AI developers address safety and misuse of deployed models.

Call for expressions of interest to study the economic impacts of large language models.

We built a neural theorem prover for Lean that learned to solve a variety of challenging high-school olympiad problems, including problems from the AMC12 and AIME competitions, as well as two problems adapted from the IMO.

We’ve trained language models that are much better at following user intentions than GPT-3 while also making them more truthful and less toxic, using techniques developed through our alignment research. These InstructGPT models, which are trained with humans in the loop, are now deployed as the default language models on our API.

We are introducing embeddings, a new endpoint in the OpenAI API that makes it easy to perform natural language and code tasks like semantic search, clustering, topic modeling, and classification.

We’ve fine-tuned GPT-3 to more accurately answer open-ended questions using a text-based web browser.

Fine-tune with a single command.

As part of our effort to support and develop AI talent, we’re excited to announce the OpenAI Residency.

Wider availability made possible by safety progress.

We’ve trained a system that solves grade school math problems with nearly twice the accuracy of a fine-tuned GPT-3 model. It solves about 90% as many problems as real kids: a small sample of 9-12 year olds scored 60% on a test from our dataset, while our system scored 55% on those same problems.

Scaling human oversight of AI systems for tasks that are difficult to evaluate.

Today, we’re excited to announce the appointment of Helen Toner to our board of directors.