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Latest AI tool releases, research breakthroughs, and industry news.
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Point-E: A system for generating 3D point clouds from complex prompts
Scaling laws for reward model overoptimization
Our approach to alignment research
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.
Efficient training of language models to fill in the middle
A hazard analysis framework for code synthesis large language models
DALL·E 2: Extending creativity
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.
DALL·E 2 pre-training mitigations
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.
Learning to play Minecraft with Video PreTraining
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 movements, making it quite general, and represents a step towards general computer-using agents.
AI-written critiques help humans notice flaws
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.
Techniques for training large neural networks
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.
Best practices for deploying language models
Cohere, OpenAI, and AI21 Labs have developed a preliminary set of best practices applicable to any organization developing or deploying large language models.
Teaching models to express their uncertainty in words
Hierarchical text-conditional image generation with CLIP latents
Lessons learned on language model safety and misuse
We describe our latest thinking in the hope of helping other AI developers address safety and misuse of deployed models.
Economic impacts research at OpenAI
Call for expressions of interest to study the economic impacts of large language models.
A research agenda for assessing the economic impacts of code generation models
Solving (some) formal math olympiad problems
We built a neural theorem prover for Lean that learned to solve a variety of challenging high-school olympiad problems, including problems from the AMC12 and AIME competitions, as well as two problems adapted from the IMO.
Aligning language models to follow instructions
Text and code embeddings by contrastive pre-training
WebGPT: Improving the factual accuracy of language models through web browsing
We’ve fine-tuned GPT-3 to more accurately answer open-ended questions using a text-based web browser.