Customizing GPT-3 for your application
Fine-tune with a single command.
人工智能
围绕「人工智能」分类汇集的全球近期技术动态与前沿报道。
Introducing Snowball Fight ☃️, our first ML-Agents environment
AI 速览 Unity推出首个ML-Agents环境Snowball Fight ☃️。
OpenAI Residency
As part of our effort to support and develop AI talent, we’re excited to announce the OpenAI Residency.
Scaling up BERT-like model Inference on modern CPU - Part 2
AI 速览 现代CPU上扩展类BERT模型推理（第二部分）。
Solving math word problems
We’ve trained a system that solves grade school math problems with nearly twice the accuracy of a fine-tuned GPT-3 model. It solves about 90% as many problems as real kids: a small sample of 9-12 year olds scored 60% on a test from our dataset, while our system scored 55% on those same problems.
Large Language Models: A New Moore's Law?
AI 速览 大语言模型发展是否遵循新的摩尔定律引发讨论。
Train a Sentence Embedding Model with 1B Training Pairs
AI 速览 使用1B训练对训练句子嵌入模型。
Hosting your Models and Datasets on Hugging Face Spaces using Streamlit
AI 速览 Hugging Face Spaces支持通过Streamlit托管模型和数据集。
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
AI 速览 TruthfulQA基准用于衡量语言模型模仿人类虚假陈述的倾向。
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.
Deep Learning over the Internet: Training Language Models Collaboratively
AI 速览 Deep Learning over the Internet提出通过互联网协作训练语言模型的方法。
Deploy Hugging Face models easily with Amazon SageMaker
AI 速览 Hugging Face模型现可通过Amazon SageMaker便捷部署。
Evaluating large language models trained on code
AI 速览 评估基于代码训练的大型语言模型。
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.
Few-shot learning in practice: GPT-Neo and the 🤗 Accelerated Inference API
AI 速览 Hugging Face发布关于GPT-Neo与🤗加速推理API在少样本学习实践中的应用内容。
Using & Mixing Hugging Face Models with Gradio 2.0
AI 速览 Hugging Face发布Gradio 2.0，支持混合使用多个Hugging Face模型。
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
AI 速览 大型语言模型的能力、局限及社会影响研究。
Faster TensorFlow models in Hugging Face Transformers
AI 速览 Hugging Face Transformers优化TensorFlow模型，提升推理速度。
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.
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.
Leveraging Pre-trained Language Model Checkpoints for Encoder-Decoder Models
AI 速览 利用预训练语言模型检查点构建编码器-解码器模型。
Transformer-based Encoder-Decoder Models
AI 速览 基于Transformer的编码器-解码器模型。
OpenAI licenses GPT-3 technology to Microsoft
OpenAI has agreed to license GPT-3 to Microsoft for their own products and services.
Block Sparse Matrices for Smaller and Faster Language Models
AI 速览 Block Sparse Matrices技术用于构建更小更快的语言模型。
Generative language modeling for automated theorem proving
AI 速览 生成式语言建模可用于自动定理证明。
Learning to summarize with human feedback
We’ve applied reinforcement learning from human feedback to train language models that are better at summarization.