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- OpenAIDALL·E 2 pre-training mitigationsCompanies: OpenAIIn 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.
- OpenAILearning to play Minecraft with Video PreTrainingCompanies: OpenAIWe 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 movements, making it quite general, and represents a step towards general computer-using agents.
- OpenAIEvolution through large modelsCompanies: OpenAIOpenAI Abstract 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 genetic programming (GP). Because such LLMs benefit from training data that includes sequential changes and modifications, they can approximate likely changes that humans would make. To highlight the breadth of implications of such
- OpenAIAI-written critiques help humans notice flawsCompanies: OpenAIWe 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.
- OpenAITechniques for training large neural networksCompanies: OpenAILarge 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.
- OpenAIBest practices for deploying language modelsCompanies: OpenAICohere, OpenAI, and AI21 Labs have developed a preliminary set of best practices applicable to any organization developing or deploying large language models.
- OpenAITeaching models to express their uncertainty in wordsCompanies: OpenAIAbstract 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, the model generates both an answer and a level of confidence (e.g. "90% confidence" or "high confidence"). These levels map to probabilities that are well calibrated. The model also remains moderately calibrated under distribution shift, and is sensitive to
- OpenAIPowering next generation applications with OpenAI CodexCompanies: OpenAICodex is now powering 70 different applications across a variety of use cases through the OpenAI API.
- OpenAIDALL·E 2 research preview updateCompanies: OpenAIEarly 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.
- OpenAIOpenAI leadership team updateCompanies: OpenAIWe’re happy to announce several executive role changes that reflect our recent progress and will ensure continued momentum toward our next major milestones.
- OpenAIMeasuring Goodhart’s lawCompanies: OpenAIGoodhart’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 grapple with at OpenAI when figuring out how to optimize objectives that are difficult or costly to measure.
- OpenAIHierarchical text-conditional image generation with CLIP latentsCompanies: OpenAIAbstract Contrastive models like CLIP have been shown to learn robust representations of images that capture both semantics and style. To leverage these representations for image generation, we propose a two-stage model: a prior that generates a CLIP image embedding given a text caption, and a decoder that generates an image conditioned on the image embedding. We show that explicitly generating image representations