The important moves in artificial intelligence.

Introducing the Data Measurements Tool: an Interactive Tool for Looking at Datasets

Solving math word problems

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.

Large Language Models: A New Moore's Law?

Summarizing books with human feedback

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

Helen Toner joins OpenAI’s board of directors

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

TruthfulQA: Measuring how models mimic human falsehoods

OpenAI Codex

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.

Evaluating large language models trained on code

OpenAI Scholars 2021: Final projects

We’re proud to announce that the 2021 class of OpenAI Scholars has completed our six-month mentorship program and have produced an open-source research project with stipends and support from OpenAI.

Will Hurd joins OpenAI’s board of directors

OpenAI is committed to developing general-purpose artificial intelligence that benefits all humanity, and we believe that achieving our goal requires expertise in public policy as well as technology. So, we’re delighted to announce that Congressman Will Hurd has joined our board of directors.

GPT-3 powers the next generation of apps

Over 300 applications are delivering GPT-3–powered search, conversation, text completion, and other advanced AI features through our API.

Multimodal neurons in artificial neural networks

We’ve discovered neurons in CLIP that respond to the same concept whether presented literally, symbolically, or conceptually. This may explain CLIP’s accuracy in classifying surprising visual renditions of concepts, and is also an important step toward understanding the associations and biases that CLIP and similar models learn.