Large Language Models

We're bringing people together to explore what's being created with machine learning.

Learn about bi-encoder and cross-encoder machine learning models, and why combining them could improve the vector search experience.

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.

The basics of using the API to create your own images from text.

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 artis

Inspired by model cards, we've created templates for documenting models on Replicate.

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 p

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

Learn about the vision of the AI-First Database Ecosystem, which drives the R&D of the databases of the future.

The post Microsoft’s framework for building AI systems responsibly appeared first on The AI Blog .

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.

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 orchestr

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

The post The opportunity at home – can AI drive innovation in personal assistant devices and sign language? appeared first on The AI Blog .

An introduction to differentiable programming and the process of refining generative art models.

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

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 g

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.

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

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

This is part 2 of what to do when facing a limited amount of labeled data for supervised learning tasks. This time we will get some amount of human labeling wor

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 AI

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

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,