OpenAI News

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.

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

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

We’ve created an improved version of OpenAI Codex, our AI system that translates natural language to code, and we are releasing it through our API in private beta starting today.

We’re releasing Triton 1.0, an open-source Python-like programming language which enables researchers with no CUDA experience to write highly efficient GPU code—most of the time on par with what an expert would be able to produce.

Our latest research finds we can improve language model behavior with respect to specific behavioral values by fine-tuning on a small, curated dataset.