✷ The archive · 11 labs · 3,433 dispatches
The labs
No more opening a dozen official blogs every morning. First-hand releases from OpenAI, Anthropic, DeepMind and the rest, each with its substance pulled out.
Hugging Face Director of Machine Learning Insights: SaaS Edition
Hugging Face interviews four ML directors from Salesforce, Amplitude, ZoomInfo, and Repustate to discuss the practical challenges, common pitfalls, and future opportunities of integrating machine learning into SaaS products.
Optimum 1.2 Inference Release Adds ONNX Runtime Accelerated Pipelines
Hugging Face announced Optimum 1.2, which adds inference support and Transformers pipeline integration via ONNX Runtime, enabling faster, quantized, and optimized model serving.
Hugging Face Series C Funding Announcement
Hugging Face raised $100 million in Series C funding to accelerate the democratization of AI through open-source research, products, and collaborative machine learning.
fastai Integration with Hugging Face Hub
Hugging Face has integrated the fastai library, allowing practitioners to upload and download fastai Learner objects directly to and from the Hugging Face Hub using a single line of Python.
OpenAI Leadership Team Update
OpenAI announced executive role changes for Greg Brockman, Brad Lightcap, Mira Murati, and Chris Clark to align leadership with the company's growth and strategic milestones.
Hugging Face Deep Reinforcement Learning Introduction
Hugging Face released an introductory tutorial for a free Deep Reinforcement Learning class, covering core RL concepts, the RL framework, exploration‑exploitation trade‑off, policy‑based vs value‑based methods, and how deep neural networks integrate into RL.
Accelerate Large Model Training using PyTorch Fully Sharded Data Parallel
Hugging Face integrates PyTorch Fully Sharded Data Parallel (FSDP) into the Accelerate library, enabling the training of massive models by sharding optimizer states, gradients, and parameters across GPUs with optional CPU offloading.
Anthropic Series B Funding for AI Safety and Research
Anthropic has raised $580 million in Series B funding to build large-scale experimental infrastructure aimed at improving the steerability, interpretability, and robustness of computationally intensive AI models.
Opinion Classification with Kili and HuggingFace AutoTrain
Hugging Face demonstrates how to build an active learning pipeline for text classification using Kili for data annotation and AutoTrain for automated model training.
Hugging Face Machine Learning Director Insights – Industry Perspectives
Hugging Face published a series of interviews with six Machine Learning Directors who share how ML is transforming media, pharmaceuticals, science, logistics, marketing, and energy, while highlighting common challenges and future opportunities.
Getting Started with Transformers on Habana Gaudi
Hugging Face and Habana Labs have partnered to accelerate Transformer model training, offering up to 40% better price-performance on Habana Gaudi accelerators compared to GPU-based Amazon EC2 instances.
Supercharging Customer Service with Machine Learning
Hugging Face demonstrates how to automate the filtering of unsatisfied customer feedback using a fine-tuned DeBERTa-v3-base model and the Transformers and Datasets libraries.
Hugging Face Education Initiative 2022 Launch
Hugging Face announced a multi‑track education program in April 2022 aimed at teaching machine learning to 5 million people by the end of 2023, providing free resources for learners, instructors, and the broader public.
Hugging Face Hub Carbon Emissions Tracking
Hugging Face has introduced tools to track, report, and filter machine learning models based on their CO2 emissions to improve environmental transparency in AI.
Measuring Goodhart's Law in AI Alignment
OpenAI explores how optimizing proxy objectives in AI models can lead to a decline in true objective performance, a phenomenon known as Goodhart's Law, and proposes best-of-n sampling as a method for analysis.
Hierarchical Text-Conditional Image Generation with CLIP Latents
OpenAI introduces a two-stage generative model that uses a prior to create CLIP image embeddings from text and a decoder to generate images from those embeddings, improving diversity and enabling zero-shot image manipulation.
Lewis Tunstall interview – Hugging Face Machine Learning Experts
Hugging Face announced a detailed interview with Machine Learning Engineer Lewis Tunstall, highlighting his work on the Transformers library, the new “NLP with Transformers” book, and the expanding Hugging Face Course.
Habana Labs and Hugging Face Partnership for Transformer Model Training
Habana Labs and Hugging Face have integrated Habana's SynapseAI software suite with the Hugging Face Optimum library to accelerate transformer model training on Habana Gaudi processors.
Anthropic: Training a Helpful and Harmless Assistant with RLHF
Anthropic demonstrates that Reinforcement Learning from Human Feedback (RLHF) improves language model performance across most NLP evaluations while ensuring the assistant remains helpful and harmless.
Hugging Face Transformers Design Philosophy
Hugging Face utilizes a "single model file" policy for the Transformers library, intentionally eschewing the DRY (Don't Repeat Yourself) principle to prioritize readability, community contributions, and the stability of static ML models.
Decision Transformer Integration in Hugging Face Transformers
Hugging Face has integrated the Decision Transformer, an offline reinforcement learning method that treats RL as a conditional-sequence modeling problem, into the transformers library and Hugging Face Hub.
Machine Learning Experts: An Interview with Margaret Mitchell
Dr. Margaret Mitchell discusses the critical role of ethical AI, the development of Model Cards for transparency, and the necessity of diversity and inclusion in machine learning development.
Hugging Face AI Research Residency Program Announcement
Hugging Face has launched a 9-month AI Research Residency Program designed to help aspiring researchers develop machine learning techniques and publish open-source work alongside the Hugging Face Science Team.
Fine-Tuning SegFormer for Semantic Segmentation with Custom Datasets
Hugging Face provides a technical guide on fine-tuning the SegFormer model using the Transformers library to perform pixel-level image classification for custom use cases like autonomous sidewalk navigation.
Image search with Hugging Face datasets
Hugging Face demonstrates how to build an image search application by combining the datasets library, CLIP embeddings, and FAISS for efficient similarity search.
Accelerate BERT Inference with Hugging Face Transformers and AWS Inferentia
Hugging Face provides a guide on using AWS Inferentia and the Neuron SDK to accelerate BERT inference, achieving 5-6ms latency on Amazon SageMaker.
GPT-3 and Codex Edit and Insert Capabilities
OpenAI has released new versions of GPT-3 and Codex that can edit existing text and insert content into the middle of a document, moving beyond simple text completion.
Guiding Text Generation with Constrained Beam Search in Hugging Face Transformers
Hugging Face introduced constrained beam search to the Transformers library, allowing users to force specific words or phrases into generated text while maintaining linguistic coherence.
Anthropic In-context Learning and Induction Heads
Anthropic announced a research post titled “In-context Learning and Induction Heads” on March 8 2022, but the page contains only related links and no substantive technical details.
OpenAI Economic Impacts Research Initiative
OpenAI has launched a call for expressions of interest from researchers and companies to study the economic impacts of large language models like GPT-3, ChatGPT, and DALL-E 2.
OpenAI Research Agenda for Economic Impacts of Code Generation Models
OpenAI has proposed a research agenda to study how code generation models like Codex affect productivity, employment, and economic inequality to inform deployment, system design, and public policy.
OpenAI Lessons Learned on Language Model Safety and Misuse
OpenAI shares insights from deploying GPT-3 and Codex, highlighting that real-world misuse often differs from theoretical risks and that safety research frequently enhances commercial utility.
BERT 101: Understanding the Bidirectional Encoder Representations from Transformers
BERT is a bidirectional Transformer-based model developed by Google AI Language that achieves state-of-the-art performance across 11+ common NLP tasks by leveraging unsupervised pre-training on massive datasets.
Anthropic Research: Predictability and Surprise in Large Generative Models
Anthropic identifies a tension between the predictable scaling of loss in large generative models and the unpredictable emergence of specific capabilities and outputs, creating challenges for AI safety and policy.
Fine-Tuning Vision Transformer (ViT) for Image Classification with Hugging Face Transformers
Hugging Face provides a guide on fine-tuning the Vision Transformer (ViT) model for image classification using the datasets and transformers libraries, demonstrating the process with the beans dataset.
OpenAI Formal Math Olympiad Problem Solving
OpenAI demonstrated the ability of its models to solve formal math olympiad problems from competitions like AMC12, AIME, and IMO using formal proof assistants.
Getting Started with Sentiment Analysis using Python
Hugging Face provides a comprehensive guide on implementing sentiment analysis using Python, leveraging pre-trained models from the Hugging Face Hub, the Trainer API for fine-tuning, and the no-code AutoNLP tool.
Automatic Speech Recognition for Large Files with Wav2Vec2 in Transformers
Hugging Face implements a striding technique leveraging the Connectionist Temporal Classification (CTC) architecture of Wav2Vec2 to enable high-quality ASR on arbitrarily long audio files and live inference.
OpenAI InstructGPT: Aligning Language Models to Follow Instructions
OpenAI introduced InstructGPT, a series of models trained using reinforcement learning from human feedback (RLHF) to better follow user intentions, increase truthfulness, and reduce toxicity compared to GPT-3.
OpenAI Text and Code Embeddings Release
OpenAI has released a new set of embedding models based on GPT-3 descendants that provide numerical representations of text and code to improve semantic search, clustering, and code retrieval.
Hugging Face Hub Search API Updates
Hugging Face has introduced new programmatic search features to the huggingface_hub library, including ModelSearchArguments and ModelFilter, to simplify how users find models and datasets without leaving their IDE.
OpenAI Text and Code Embeddings by Contrastive Pre-training
OpenAI has developed a method for creating high-quality text and code embeddings using contrastive pre-training on unsupervised data at scale, achieving state-of-the-art results in linear-probe classification and semantic search.
Stable-Baselines3 Integration with Hugging Face Hub
Hugging Face has integrated Stable-Baselines3, allowing users to host, share, and load PyTorch Deep Reinforcement Learning models directly from the Hugging Face Hub.
Hugging Face Infinity CPU Performance Case Study
Hugging Face Infinity achieves up to 800% higher throughput and millisecond latency on Intel Ice Lake Xeon CPUs compared to vanilla Transformers, enabling cost-effective, real-time Transformer deployments on CPU infrastructure.
Boosting Wav2Vec2 with n-grams in Hugging Face Transformers
Hugging Face has integrated the pyctcdecode library into the Transformers library, enabling Wav2Vec2 models to be boosted with n-gram language models to significantly reduce spelling errors and Word Error Rate (WER).
Deploying GPT-J 6B on Amazon SageMaker with Hugging Face Transformers
Hugging Face provides a method to deploy EleutherAI's GPT-J 6B model on Amazon SageMaker, reducing model load times from over three minutes to under eight seconds using torch.save.
Active Learning with AutoNLP and Prodigy
Hugging Face demonstrates how to build an active learning pipeline for Named Entity Recognition (NER) by combining the AutoNLP automated training framework with the Prodigy annotation tool.
Anthropic Announces Mathematical Framework for Transformer Circuits
Anthropic released a brief announcement of a new mathematical framework for transformer circuits, highlighting its potential to deepen understanding of model behavior but providing no technical details in the post.
Hugging Face Acquires Gradio
Hugging Face has acquired Gradio to integrate easy-to-build machine learning demos and GUIs into its ecosystem, expanding ML accessibility to non-technical users.
WebGPT: Improving GPT-3 Factual Accuracy via Web Browsing
OpenAI has fine-tuned GPT-3 into WebGPT, a prototype that uses a text-based web browser to research and cite sources, reducing hallucinations in open-ended question answering.