12 articles
Hugging Face introduces Decision Transformers, transformer models trained with reinforcement learning for autonomous decision-making. These models can be used for control and planning tasks without requiring custom code.
Profile of Margaret Mitchell, ML expert and co-founder of DAIR (Distributed AI Research Institute). Her work focuses on AI ethics, model interpretability, and societal impacts of machine learning.
Hugging Face announces an AI Research Residency Program to develop emerging talent. The program provides immersion in open-source research projects and practical AI challenges.
Hugging Face publishes a guide to fine-tune a semantic segmentation model on a custom dataset. The method covers data preparation, fine-tuning, and model evaluation.
Hugging Face and AWS optimize BERT inference on AWS Inferentia. Benchmarks demonstrate significant acceleration and cost reduction for production deployments.
Hugging Face integrates image search into its datasets platform. Users can now index and query image collections using vector embeddings, enabling discovery and organization of large-scale visual data.
OpenAI releases new versions of GPT-3 and Codex that can edit or insert content into existing text, beyond simple text completion.
Hugging Face integrates constrained beam search into Transformers to guide text generation according to specified constraints. This technique improves control over model outputs by applying rules during decoding.
OpenAI shares lessons learned on language model safety and misuse prevention. The company outlines its current thinking to help other AI developers address these critical challenges in deployed models.
OpenAI publishes a research agenda to assess economic impacts of code generation models. The study examines how these tools affect developer productivity, employment, and wages, with no definitive conclusions yet.
OpenAI issues call for expressions of interest to study economic impacts of large language models. Research initiative to document macroeconomic and sectoral effects of LLMs.
Explanatory article on BERT, Google's foundational NLP model (2018). Covers bidirectional Transformer architecture, pre-training on massive corpora, and applications in text classification and entity extraction.