New ways to manage your data in ChatGPT
ChatGPT users can now turn off chat history, allowing you to choose which conversations can be used to train our models.
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ChatGPT users can now turn off chat history, allowing you to choose which conversations can be used to train our models.
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This initiative is essential to our commitment to develop safe and advanced AI. As we create technology and services that are secure, reliable, and trustworthy, we need your help.
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Ensuring that AI systems are built, deployed, and used safely is critical to our mission.
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An update on our findings, the actions we’ve taken, and technical details of the bug.
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We’ve created GPT-4, the latest milestone in OpenAI’s effort in scaling up deep learning. GPT-4 is a large multimodal model (accepting image and text inputs, emitting text outputs) that, while less capable than humans in many real-world scenarios, exhibits human-level performance on various professi...
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Our mission is to ensure that artificial general intelligence—AI systems that are generally smarter than humans—benefits all of humanity.
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We’re clarifying how ChatGPT’s behavior is shaped and our plans for improving that behavior, allowing more user customization, and getting more public input into our decision-making in these areas.
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We’re launching a pilot subscription plan for ChatGPT, a conversational AI that can chat with you, answer follow-up questions, and challenge incorrect assumptions.
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We’re launching a classifier trained to distinguish between AI-written and human-written text.
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We’re happy to announce that OpenAI and Microsoft are extending our partnership.
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OpenAI researchers collaborated with Georgetown University’s Center for Security and Emerging Technology and the Stanford Internet Observatory to investigate how large language models might be misused for disinformation purposes. The collaboration included an October 2021 workshop bringing together...
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Using GPT-3 to create the next generation of AI-powered characters.
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Lilian Weng works on Applied AI Research at OpenAI.
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Christian Gibson is an engineer on the Supercomputing team at OpenAI.
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We’ve trained a model called ChatGPT which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer followup questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests.
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Starting today, developers can begin building apps with the DALL·E API.
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New users can start creating straight away. Lessons learned from deployment and improvements to our safety systems make wider availability possible.
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We’ve trained and are open-sourcing a neural net called Whisper that approaches human level robustness and accuracy on English speech recognition.
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Extend creativity and tell a bigger story with DALL·E images of any size.
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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.
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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.
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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.
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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.
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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...
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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 A...
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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.
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Cohere, OpenAI, and AI21 Labs have developed a preliminary set of best practices applicable to any organization developing or deploying large language models.
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Codex is now powering 70 different applications across a variety of use cases through the OpenAI API.
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