The power of continuous learning
OpenAI research on continuous learning, fine-tuning, and long-term memory for models. Focus on improving adaptation and performance.
OpenAI News·23 Dec 2022
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OpenAI research on continuous learning, fine-tuning, and long-term memory for models. Focus on improving adaptation and performance.
OpenAI announced a new, more capable, and cost-effective embedding model, claiming improved performance and ease of use.
Hugging Face published its second 'Ethics and Society' newsletter, discussing biases in machine learning. No new technical details or G-SIB specific content.
Hugging Face blog post compares Habana Gaudi2 vs Nvidia A100 80GB for faster training and inference on open-source models.
Hugging Face is expanding its machine learning ecosystem support to the Elixir programming language, enabling direct integration of models.
OpenAI engineer Christian Gibson discussed 'discovering the minutiae of backend systems' within the company's frontier lab.
Hugging Face published a blog post discussing deep learning applications in protein science, covering foundational models for biological sequences.
Hugging Face hosted a live event on diffusion models, likely showcasing new advancements, tools, or applications in the generative AI space.
Hugging Face published 'Director of Machine Learning Insights [Part 4]', focusing on enterprise AI challenges and strategies.
Hugging Face blog post discusses advancements in document AI, likely focusing on open-source model capabilities for document processing.
Hugging Face published an overview of its inference solutions, including Inference Endpoints, TGI, and the Accelerate library.
Hugging Face is now hosting interactive machine learning demos directly on arXiv paper pages, integrating execution environments with research.
Hugging Face introduced 🤗 Evaluate, a new platform feature for evaluating language model bias using various datasets and metrics.
Hugging Face detailed its Inference Endpoints offering, providing managed compute for deploying models from its platform with autoscaling and security features.
Hugging Face released a framework for evaluating large language models focusing on benchmarks, data quality, and responsible AI.
OpenAI open-sources Whisper, a neural network achieving near-human level accuracy for English speech recognition.
Hugging Face published a tutorial on training large language models using NVIDIA's Megatron-LM framework for distributed training.
Eugene Yan argues for fewer integration tests and more unit/data tests in ML pipelines to reduce brittleness and accelerate development cycles.
Hugging Face proposes OpenRAIL, a licensing framework for responsible AI development and usage, aiming to balance openness with safety.
OpenAI published an overview of its alignment research, focusing on improving human feedback learning and AI-assisted evaluation.
Hugging Face Spaces now supports interactive 3D visualization of protein structures, integrating scientific data and tools.
Hugging Face details pre-training BERT on Habana Gaudi hardware, indicating an alternative for large-scale model training infrastructure.
Hugging Face provided guidance on deploying Vision Transformer (ViT) models on Google Cloud's Vertex AI platform for MLOps.
Hugging Face detailed optimizing Vision Transformers on Graphcore IPUs, showcasing performance improvements for vision models.
Hugging Face outlined its strategic shift in supporting TensorFlow, prioritizing Keras 3 and native TF/Keras for future integrations.
OpenAI launched an improved, free Moderation endpoint for API developers to filter unsafe content from their applications.
Hugging Face launched 'Private Hub' offering dedicated, secure spaces for enterprises to host models and datasets with granular access controls.
Hugging Face provided comments on the U.S. National AI Research Resource (NAIRR) Interim Report, advocating for open-source AI.
OpenAI research details efficient training of 'fill-in-the-middle' (FIM) language models, improving code generation and contextual completion.
OpenAI's Frontier Lab released a hazard analysis framework for LLM-based code synthesis, focusing on security and reliability risks.
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