We’ve trained and are open-sourcing a neural net called Whisper that approaches human level robustness and accuracy on English speech recognition.
Extend creativity and tell a bigger story with DALL·E images of any size.
A new scholarship programme at Oxford will increase the number of women from low- and middle-income countries who take up offers to study science, technology, engineering and mathematics (STEM) subjects at postgraduate level. The programme, which will provide support for 30 taught master’s students over a period of five years, has been made possible by a donation from the Optiver Foundation.
By Jonathan WoodsAugust 24, 2022 Vector’s Industry Innovation team has released the Computer Vision: Applications in Manufacturing, Surgery, Traffic, Satellites, and Unlabelled Data Recognition Technical Report. It details experiments and […] The post RBC joins Vector for our Computer Vision Symposium and shares how RBC’s new AI engine can accurately recognize building boundaries in satellite imag
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.
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.
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.
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.
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’s features.
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.
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 over 20 minutes (24,000 actions). Our model uses the native human interface of keypresses and mouse
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 AI systems to assist human supervision of AI systems on difficult tasks.
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.
Cohere, OpenAI, and AI21 Labs have developed a preliminary set of best practices applicable to any organization developing or deploying large language models.
Codex is now powering 70 different applications across a variety of use cases through the OpenAI API.
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.
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