✷ The archive · 6,249 dispatches
All dispatches
Everything AgentLensHQ has filed — distilled from across the AI ecosystem.
OpenAI, Cohere, and AI21 Labs Best Practices for Deploying Language Models
OpenAI, Cohere, and AI21 Labs have released a joint set of preliminary best practices for the responsible development and deployment of large language models to mitigate risks and prevent misuse.
OpenAI Research: Teaching Models to Express Their Uncertainty in Words
OpenAI researchers demonstrated that GPT-3 can be trained to express calibrated uncertainty about its answers in natural language without relying on model logits.
Graphcore and Hugging Face Expand IPU-Ready Transformer Models
Graphcore and Hugging Face have expanded the Hugging Face Optimum library to include 10 optimized transformer models across NLP, speech, and computer vision for Graphcore IPUs.
Hugging Face Hub Community Update: Pull Requests and Discussions
Hugging Face has introduced pull requests and discussions to the Hub, enabling community-wide collaboration on models, datasets, and Spaces without the need for repository forks.
OpenAI Codex Applications and Integration
OpenAI Codex is a natural language-to-code system based on GPT-3 that powers over 70 applications, including GitHub Copilot, to increase developer productivity by converting English instructions into code.
TAPEX: Efficient Table Pre-training via Neural SQL Execution
TAPEX is a table pre-training approach that uses synthetic SQL execution pairs to achieve state-of-the-art results on table question answering and fact verification with significantly higher efficiency than previous methods.
Anthropic Research: Scaling Laws and Interpretability of Learning from Repeated Data
Anthropic researchers found that repeating a small fraction of training data can severely degrade LLM performance by consuming model capacity for memorization and damaging generalization structures like induction heads.
Hugging Face Introduction to Q-Learning
Hugging Face provides a technical guide to Q-Learning, an off-policy value-based reinforcement learning method that uses a Q-table to determine optimal state-action pairs.
Hugging Face Multimodal Project Ethical Charter
Hugging Face has established an ethical charter for its multimodal learning project to integrate ethical principles, transparency, and fairness directly into the machine learning research lifecycle.
Sempre Health Case Study: Accelerating NLP Pipelines with Hugging Face Expert Acceleration Program
Sempre Health utilized the Hugging Face Expert Acceleration Program to deploy an NLP pipeline that automatically handles over 20% of inbound patient messages, significantly improving business scalability.
OpenAI DALL·E 2 Research Preview Update
OpenAI is expanding access to DALL·E 2 to waitlisted users at a rate of 1,000 people per week following a limited research preview that generated over 3 million images.
Hugging Face Deep RL Course Unit 2 Part 1: Introduction to Q-Learning Concepts
Hugging Face released the first part of its Deep Reinforcement Learning course, explaining value‑based methods, the Bellman equation, and Monte‑Carlo vs. Temporal‑Difference learning as groundwork for Q‑Learning.
Hugging Face Fellowship Program Announcement
Hugging Face has launched the Fellowship Program to support and empower key contributors to the open-source machine learning ecosystem through resources, recognition, and tailored support.
Machine Learning Experts Interview with Sasha Luccioni – Highlights on Ethical AI and Climate Impact
Hugging Face interviewed research scientist Sasha Luccioni, who discussed her work on ethical AI, carbon‑footprint measurement, and applying machine learning to climate and societal challenges.
Gradio 3.0 Release Notes
Hugging Face has released Gradio 3.0, featuring a complete frontend redesign and the introduction of Gradio Blocks for building complex, custom ML web apps in Python.
Hugging Face Student Ambassador Program 2022
Hugging Face has launched its first Student Ambassador Program to help achieve its goal of teaching machine learning to 5 million people by 2023.
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.