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The power of continuous learning

Lilian Weng works on Applied AI Research at OpenAI.

Discovering the minutiae of backend systems

Christian Gibson is an engineer on the Supercomputing team at OpenAI.

Introducing ChatGPT

We’ve trained a model called ChatGPT which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer followup questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests.

DALL·E API now available in public beta

Starting today, developers can begin building apps with the DALL·E API.

DALL·E now available without waitlist

New users can start creating straight away. Lessons learned from deployment and improvements to our safety systems make wider availability possible.

DALL·E: Introducing outpainting

Extend creativity and tell a bigger story with DALL·E images of any size.

Our approach to alignment research

We are improving our AI systems’ ability to learn from human feedback and to assist humans at evaluating AI. Our goal is to build a sufficiently aligned AI system that can help us solve all other alignment problems.

New and improved content moderation tooling

We are introducing a new and improved content moderation tool. The Moderation endpoint improves upon our previous content filter, and is available for free today to OpenAI API developers.

Efficient training of language models to fill in the middle

DALL·E now available in beta

We’ll invite 1 million people from our waitlist over the coming weeks. Users can create with DALL·E using free credits that refill every month, and buy additional credits in 115-generation increments for $15.

DALL·E 2 pre-training mitigations

In order to share the magic of DALL·E 2 with a broad audience, we needed to reduce the risks associated with powerful image generation models. To this end, we put various guardrails in place to prevent generated images from violating our content policy.

Learning to play Minecraft with Video PreTraining

We trained a neural network to play Minecraft by Video PreTraining (VPT) on a massive unlabeled video dataset of human Minecraft play, while using only a small amount of labeled contractor data. With fine-tuning, our model can learn to craft diamond tools, a task that usually takes proficient humans...

AI-written critiques help humans notice flaws

We trained “critique-writing” models to describe flaws in summaries. Human evaluators find flaws in summaries much more often when shown our model’s critiques. Larger models are better at self-critiquing, with scale improving critique-writing more than summary-writing. This shows promise for using A...

Techniques for training large neural networks

Large neural networks are at the core of many recent advances in AI, but training them is a difficult engineering and research challenge which requires orchestrating a cluster of GPUs to perform a single synchronized calculation.

Best practices for deploying language models

Cohere, OpenAI, and AI21 Labs have developed a preliminary set of best practices applicable to any organization developing or deploying large language models.

Teaching models to express their uncertainty in words

Powering next generation applications with OpenAI Codex

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

DALL·E 2 research preview update

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. 

OpenAI leadership team update

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

Measuring Goodhart’s law

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.

Economic impacts research at OpenAI

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

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.

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

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.