Isabella's AI-Powered News Aggregator

How should AI systems behave, and who should decide?

We’re clarifying how ChatGPT’s behavior is shaped and our plans for improving that behavior, allowing more user customization, and getting more public input into our decision-making in these areas.

Introducing ChatGPT Plus

We’re launching a pilot subscription plan for ChatGPT, a conversational AI that can chat with you, answer follow-up questions, and challenge incorrect assumptions.

New AI classifier for indicating AI-written text

We’re launching a classifier trained to distinguish between AI-written and human-written text.

OpenAI and Microsoft extend partnership

We’re happy to announce that OpenAI and Microsoft are extending our partnership.

Forecasting potential misuses of language models for disinformation campaigns and how to reduce risk

OpenAI researchers collaborated with Georgetown University’s Center for Security and Emerging Technology and the Stanford Internet Observatory to investigate how large language models might be misused for disinformation purposes. The collaboration included an October 2021 workshop bringing together...

Delivering nuanced insights from customer feedback

Using GPT-3 to deliver fast, nuanced insights from customer feedback.

Fine-tuning GPT-3 to scale video creation

Fine-tuning GPT-3 to power and scale done-for-you video creation.

Creating next-gen characters

Using GPT-3 to create the next generation of AI-powered characters.

The power of continuous learning

Lilian Weng works on Applied AI Research at OpenAI.

Point-E: A system for generating 3D point clouds from complex prompts

New and improved embedding model

We are excited to announce a new embedding model which is significantly more capable, cost effective, and simpler to use.

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.

Scaling laws for reward model overoptimization

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.

Introducing Whisper

We’ve trained and are open-sourcing a neural net called Whisper that approaches human level robustness and accuracy on English speech recognition.

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

A hazard analysis framework for code synthesis large language models

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.

Reducing bias and improving safety in DALL·E 2

Today, we are implementing a new technique so that DALL·E generates images of people that more accurately reflect the diversity of the world’s population.

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.