Isabella's AI-Powered News Aggregator

DALL·E 2: Extending creativity

As part of our DALL·E 2 research preview, more than 3,000 artists from more than 118 countries have incorporated DALL·E into their creative workflows. The artists in our early access group have helped us discover new uses for DALL·E and have served as key voices as we’ve made decisions about DALL·E’...

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...

Evolution through large models

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.

Hierarchical text-conditional image generation with CLIP latents

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.

New GPT-3 capabilities: Edit & insert

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.

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.