Teaching models to express their uncertainty in words
OpenAI develops techniques enabling models to express uncertainty in natural language rather than confidence scores. The approach improves reliability and transparency of model responses.
19 articles
OpenAI develops techniques enabling models to express uncertainty in natural language rather than confidence scores. The approach improves reliability and transparency of model responses.
Graphcore and Hugging Face launch a new lineup of transformers optimized for IPU (Intelligent Processing Units). These pre-trained models are ready for deployment on Graphcore infrastructure, streamlining production use.
Hugging Face introduces Pull Requests and Discussions features. These enable users to collaborate on models, datasets, and spaces, streamlining community contributions and iterative feedback.
Codex now powers 70 different applications across various use cases through the OpenAI API.
Hugging Face introduces TAPEX, an efficient pre-training method for table understanding models without real data. The technique uses data synthesis and reinforcement learning to improve performance on table-based question-answering tasks.
Part 2 of an introduction to Q-Learning. Covers reinforcement learning algorithms, Q-value update mechanisms and practical applications.
Sempre Health leverages Hugging Face's Expert Acceleration Program to speed up its ML roadmap. The company gains technical expertise and resources to deploy models to production faster.
Hugging Face embeds ethical principles throughout its research lifecycle. The approach covers model evaluation, data documentation, and transparency on limitations. The goal is to align AI research with accountability and inclusivity values.
OpenAI expands DALL·E 2 access by adding 1,000 new users weekly. Current users have generated over 3 million images and contributed to improving safety processes.
Introduction to Q-Learning fundamentals, a reinforcement learning algorithm. Covers core concepts and iterative learning mechanisms for agent policy optimization.
Hugging Face launches a fellowship program to support researchers and developers working on open-source AI projects. The program provides resources, mentorship, and community visibility to selected participants.
Gradio 3.0 released. The ML model web interface platform introduces new architecture, redesigned components, and improved performance for rapid AI application deployment.
Hugging Face releases part two of a series on machine learning insights, focused on the SaaS model. The article explores trends, challenges, and opportunities for companies leveraging machine learning as a service.
Hugging Face opens applications for its Student Ambassador Program. Students can apply to represent the open-source AI community and promote Hugging Face tools within their institutions.
Hugging Face introduces inference acceleration through Optimum and Transformers Pipelines, reducing latency and memory consumption for language models in production.
Hugging Face raises $100 million to accelerate open-source and collaborative machine learning development. The funding supports platform expansion, model development, and infrastructure tools.
fastai joins the Hugging Face Hub. The popular machine learning platform integrates its ecosystem with Hugging Face infrastructure to streamline model and dataset sharing.
Hugging Face publishes an introductory guide to deep reinforcement learning (DRL). The article covers fundamental concepts, key algorithms, and practical applications of DRL in modern AI.
Hugging Face releases guidance on PyTorch Fully Sharded Data Parallel (FSDP) for accelerating large model training. FSDP distributes model parameters across multiple GPUs, reducing per-device memory and improving scalability.