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Summarizing books with human feedback
Scaling human oversight of AI systems for tasks that are difficult to evaluate.
TruthfulQA: Measuring how models mimic human falsehoods
Helen Toner joins OpenAI’s board of directors
Today, we’re excited to announce the appointment of Helen Toner to our board of directors.
OpenAI Codex
We’ve created an improved version of OpenAI Codex, our AI system that translates natural language to code, and we are releasing it through our API in private beta starting today.
Introducing Triton: Open-source GPU programming for neural networks
We’re releasing Triton 1.0, an open-source Python-like programming language which enables researchers with no CUDA experience to write highly efficient GPU code—most of the time on par with what an expert would be able to produce.
Evaluating large language models trained on code
Improving language model behavior by training on a curated dataset
Our latest research finds we can improve language model behavior with respect to specific behavioral values by fine-tuning on a small, curated dataset.
OpenAI Scholars 2021: Final projects
We’re proud to announce that the 2021 class of OpenAI Scholars has completed our six-month mentorship program and have produced an open-source research project with stipends and support from OpenAI.
Will Hurd joins OpenAI’s board of directors
OpenAI is committed to developing general-purpose artificial intelligence that benefits all humanity, and we believe that achieving our goal requires expertise in public policy as well as technology. So, we’re delighted to announce that Congressman Will Hurd has joined our board of directors.
GPT-3 powers the next generation of apps
Over 300 applications are delivering GPT-3–powered search, conversation, text completion, and other advanced AI features through our API.
Multimodal neurons in artificial neural networks
We’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.
Understanding the capabilities, limitations, and societal impact of large language models
Scaling Kubernetes to 7,500 nodes
We’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.
DALL·E: Creating images from text
We’ve trained a neural network called DALL·E that creates images from text captions for a wide range of concepts expressible in natural language.
CLIP: Connecting text and images
We’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.
Organizational update from OpenAI
It’s been a year of dramatic change and growth at OpenAI.
OpenAI licenses GPT-3 technology to Microsoft
OpenAI has agreed to license GPT-3 to Microsoft for their own products and services.
Generative language modeling for automated theorem proving
Learning to summarize with human feedback
We’ve applied reinforcement learning from human feedback to train language models that are better at summarization.
OpenAI Scholars 2020: Final projects
Our third class of OpenAI Scholars presented their final projects at virtual Demo Day, showcasing their research results from over the past five months.