Pioneering research on the path to AGI

We believe our research will eventually lead to artificial general intelligence, a system that can solve human-level problems. Building safe and beneficial AGI is our mission.

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“Safely aligning powerful AI systems is one of the most important unsolved problems for our mission. Techniques like learning from human feedback are helping us get closer, and we are actively researching new techniques to help us fill the gaps.”

Josh Achiam, Researcher at OpenAI

Focus areas

We build our generative models using a technology called deep learning, which leverages large amounts of data to train an AI system to perform a task.

Text

Our text models are advanced language processing tools that can generate, classify, and summarize text with high levels of coherence and accuracy.

Aligning language models to follow instructions

We’ve trained language models that are much better at following user intentions than GPT-3.

Summarizing books with human feedback

We've trained a model to summarize entire books with human feedback.

Language models are few-shot learners

We trained GPT-3, an autoregressive language model with 175 billion parameters.

Image

Our research on generative modeling for images has led to representation models like CLIP, which makes a map between text and images that an AI can read, and DALL-E, a tool for creating vivid images from text descriptions.

Hierarchical text-conditional image generation with CLIP latents

We show that explicitly generating image representations improves image diversity with minimal loss in photorealism and caption similarity.

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.

Audio

Our research on applying AI to audio processing and audio generation has led to developments in automatic speech recognition and original musical compositions.

Product

Navigating the challenges and opportunities of synthetic voices