Today, we’re releasing the Assistants API, our first step towards helping developers build agent-like experiences within their own applications. An assistant is a purpose-built AI that...
To minimize these risks as AI models continue to improve, we are building a new team called Preparedness. Led by Aleksander Madry, the Preparedness team will...
Over the past year, industry has driven significant advances in the capabilities of AI. As those advances have accelerated, new academic research into AI safety is...
We use a multi-tiered safety system to limit DALL·E 3’s ability to generate potentially harmful imagery, including violent, adult or hateful content. Safety checks run over...
We show that a GPT-3 model can learn to express uncertainty about its own answers in natural language—without use of model logits. When given a question,...
Pipeline parallelism splits a model “vertically” by layer. It’s also possible to “horizontally” split certain operations within a layer, which is usually called Tensor Parallel training. For many...
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...
This paper pursues the insight that large language models (LLMs) trained to generate code can vastly improve the effectiveness of mutation operators applied to programs in...
The internet contains an enormous amount of publicly available videos that we can learn from. You can watch a person make a gorgeous presentation, a digital...
We observed that our internal predecessors to DALL·E 2 would sometimes reproduce training images verbatim. This behavior was undesirable, since we would like DALL·E 2 to...