Resource library
Explore our complete library of resources including blogs, benchmarks, research papers, and more.
Data 2.0 and the research era of AI data
Announcing Snorkel’s $350M fundraise to build the frontier lab for AI data.
Terminal-Bench 4.0: Why Continuous Benchmarks Require Continuous QA
Grok 4.5 Testing Results: How SpaceXAI’s New Model Performs on Real Professional Work
Why coding agents need better data, evals, and environments
Closing the Evaluation Gap in Agentic AI
Announcing a $3M commitment to launch Open Benchmarks Grants
Why Frontier Agents Fail Real Engineering Work: Two Terminal-Bench 3.0 Task Deep Dives
Building FinQA: An Open RL Environment for Financial Reasoning Agents
The science of rubric design
Join the live discussion. Learn how to unlock data-centric AI and make AI development practical in your organization Working with vast unstructured and unlabeled data is one of the bottlenecks in the machine learning lifecycle. Machine learning models can only get as reliable and accurate as the data being fed to them. With a data-centric approach 1, your data science…
We started the Snorkel project at the Stanford AI lab in 2015 around two core hypotheses:
Machine Learning Whiteboard (MLW) Open-source Series Earlier this year, we started our machine learning whiteboard (MLW) series, an open-invite space to brainstorm ideas and discuss the latest papers, techniques, and workflows in the AI space. We emphasize an informal and open environment to everyone interested in learning about machine learning.In this episode, Manan Shah dives into “Glean: Structured Extractions from…
This work shows a rigorous technique for efficiently selecting small subsets of the labelers so that a majority vote from such subsets has a provably low error rate.
The how, what, and why of Snorkel’s programmatic data labeling approach and the state-of-the-art Snorkel Flow platform. The year was 2015. For the first time, machine learning (ML) had outperformed humans in the annual ImageNet challenge.
In this workshop, we want to advance theory, methods and tools for allowing experts to express prior coded knowledge for automatic data annotations that can be used to train arbitrary deep neural networks for prediction. The ICLR 2021 Workshop on Weak Supervision aims at advancing methods that help modern machine-learning methods to generalize from knowledge provided by experts, in interaction…
In many practical few-shot learning problems, even though labeled examples are scarce, there are abundant auxiliary datasets that potentially contain useful information. We propose the problem of extended few-shot learning to study these scenarios. We then introduce a framework to address the challenges of efficiently selecting and effectively using auxiliary data in few-shot image classification. Given a large auxiliary dataset…
Machine learning models are often deployed in different settings than they were trained and validated on, posing a challenge to practitioners who wish to predict how well the deployed model will perform on a target distribution. If an unlabeled sample from the target distribution is available, along with a labeled sample from a possibly different source distribution, standard approaches such…
Machine Learning Whiteboard (MLW) Open-source Series Our machine learning whiteboard (MLW) is an open-invite space to brainstorm ideas and discuss the latest papers, techniques, and workflows in the AI space. We emphasize an informal and open environment to everyone interested in discovering more about machine learning.In this episode, Hiromu Hota, Vincent Sunn Chen, Daniel Y. Fu, and Frederic Sala dive…
