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A research agenda for assessing the economic impacts of code generation models

Lessons learned on language model safety and misuse

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

Economic impacts research at OpenAI

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

Solving (some) formal math olympiad problems

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.

Aligning language models to follow instructions

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 defa...

Introducing text and code embeddings

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.

Text and code embeddings by contrastive pre-training

WebGPT: Improving the factual accuracy of language models through web browsing

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

Customizing GPT-3 for your application

Fine-tune with a single command.

OpenAI Residency

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

OpenAI’s API now available with no waitlist

Wider availability made possible by safety progress.

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.

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.

Introducing Triton: Open-source GPU programming for neural networks

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.

Evaluating large language models trained on code

Improving language model behavior by training on a curated dataset

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

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...

Understanding the capabilities, limitations, and societal impact of large language models

Scaling Kubernetes to 7,500 nodes

We’ve scaled Kubernetes clusters to 7,500 nodes, producing a scalable infrastructure for large models like GPT-3, CLIP, and DALL·E, but also for rapid small-scale iterative research such as Scaling Laws for Neural Language Models.

DALL·E: Creating images from text

We’ve trained a neural network called DALL·E that creates images from text captions for a wide range of concepts expressible in natural language.

CLIP: Connecting text and images

We’re introducing a neural network called CLIP which efficiently learns visual concepts from natural language supervision. CLIP can be applied to any visual classification benchmark by simply providing the names of the visual categories to be recognized, similar to the “zero-shot” capabilities of GP...

Organizational update from OpenAI

It’s been a year of dramatic change and growth at OpenAI.

OpenAI licenses GPT-3 technology to Microsoft

OpenAI has agreed to license GPT-3 to Microsoft for their own products and services.

Generative language modeling for automated theorem proving