Resource library
Evaluating Coding Agent Capabilities with Terminal-Bench: Snorkel’s Role in Building the Next Generation Benchmark
Closing the Evaluation Gap in Agentic AI
Announcing a $3M commitment to launch Open Benchmarks Grants
Benchtalks #1: Alex Shaw (Terminal-Bench, Harbor) – Building the Benchmark Factory
Building FinQA: An Open RL Environment for Financial Reasoning Agents
The paper proposes a statistical label model called FABLE that incorporates instance features to improve the accuracy of inferred truth in Programmatic Weak Supervision (PWS). FABLE is built on a mixture of Bayesian label models, where the coefficients of the mixture components are predicted by a Gaussian Process classifier based on instance features.
Generative AI is at peak hype and poised to dive into the “trough of despair,” according to the 2023 Gartner® Hype Cycle™ for AI.
We used weak supervision to programmatically curate instruction tuning data for open-source LLMs to build a better GenAI.
Snorkel AI announced a strategic partnership with Together AI to enable organizations to build their own proprietary LLMs on their data.
This release eases Snorkel Flow application creation process and tightens the iteration loop. It also upgrades our security certifications.
NVIDIA’s Nyla Worker presented “Leveraging Synthetic Data to Train Perception Models Using NVIDIA Omniverse Replicator” in 2022.
Google experts Abhishek Ratna and Robert Crowe discuss practical paths to data-centricity in applied AI at The Future of Data-Centric AI ’22.
State Farm senior data scientist Jason Goldfarb presented “Reusable Data Cleaning Pipelines in Python” at the Future of Data-Centric AI 2022.
Jack Zhou, product manager at Arize, on “How to Apply Machine Learning Observability to Your ML System” from The Future of Data-Centric AI
