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- OpenAIMultimodal neurons in artificial neural networksCompanies: OpenAIWe’ve discovered neurons in CLIP that respond to the same concept whether presented literally, symbolically, or conceptually. This may explain CLIP’s accuracy in classifying surprising visual renditions of concepts, and is also an important step toward understanding the associations and biases that CLIP and similar models learn.
- OpenAIUnderstanding the capabilities, limitations, and societal impact of large language modelsCompanies: OpenAIAbstract On October 14th, 2020, researchers from OpenAI, the Stanford Institute for Human-Centered Artificial Intelligence, and other universities convened to discuss open research questions surrounding GPT‑3, the largest publicly-disclosed dense language model at the time. The meeting took place under Chatham House Rules. Discussants came from a variety of research backgrounds including computer science, linguistics
- OpenAIScaling Kubernetes to 7,500 nodesCompanies: OpenAIWe’ve scaled Kubernetes clusters to 7,500 nodes, producing a scalable infrastructure for large models like GPT-3, CLIP, and DALL·E, but also for rapid small-scale iterative research such as Scaling Laws for Neural Language Models.
- OpenAIDALL·E: Creating images from textCompanies: OpenAIWe’ve trained a neural network called DALL·E that creates images from text captions for a wide range of concepts expressible in natural language.
- OpenAICLIP: Connecting text and imagesCompanies: OpenAIWe’re introducing a neural network called CLIP which efficiently learns visual concepts from natural language supervision. CLIP can be applied to any visual classification benchmark by simply providing the names of the visual categories to be recognized, similar to the “zero-shot” capabilities of GPT-2 and GPT-3.
- OpenAIOrganizational update from OpenAICompanies: OpenAIIt’s been a year of dramatic change and growth at OpenAI.
- OpenAIOpenAI licenses GPT-3 technology to MicrosoftCompanies: OpenAIMicrosoftOpenAI has agreed to license GPT-3 to Microsoft for their own products and services.
- OpenAIGenerative language modeling for automated theorem provingCompanies: OpenAIOpenAI Abstract We explore the application of transformer-based language models to automated theorem proving. This work is motivated by the possibility that a major limitation of automated theorem provers compared to humans -- the generation of original mathematical terms -- might be addressable via generation from language models. We present an automated prover and proof assistant, GPT‑f, for the Metamath formalizat
- OpenAILearning to summarize with human feedbackCompanies: OpenAIWe’ve applied reinforcement learning from human feedback to train language models that are better at summarization.
- OpenAIOpenAI Scholars 2020: Final projectsCompanies: OpenAIOur third class of OpenAI Scholars presented their final projects at virtual Demo Day, showcasing their research results from over the past five months.
- OpenAIProcgen and MineRL CompetitionsCompanies: OpenAIWe’re excited to announce that OpenAI is co-organizing two NeurIPS 2020 competitions with AIcrowd, Carnegie Mellon University, and DeepMind, using Procgen Benchmark and MineRL.
- OpenAIImage GPTCompanies: OpenAIWe find that, just as a large transformer model trained on language can generate coherent text, the same exact model trained on pixel sequences can generate coherent image completions and samples. By establishing a correlation between sample quality and image classification accuracy, we show that our best generative model also contains features competitive with top convolutional nets in the unsupervised setting.