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In this post, we will show you a specialized benchmark dataset we developed with our expert network of Chartered Property and Casualty Underwriters (CPCUs). The benchmark uncovers several model-specific and actionable error modes, including basic tool use errors and a surprising number of insidious hallucinations from one provider. This is part of an ongoing series of benchmarks we are releasing across verticals

Discover how enterprises can leverage LLM-as-Judge systems to evaluate generative AI outputs at scale, improve model alignment, reduce costs, and tackle challenges like bias and interpretability.

We aim to help our customers get GenAI into production. In our 2024.R3 release, we’ve delivered some exciting GenAI evaluation results.

Snorkel AI has made building production-ready, high-value enterprise AI applications faster and easier than ever. The 2024.R3 update to our Snorkel Flow AI data development platform streamlines data-centric workflows, from easier-than-ever generative AI evaluation to multi-schema annotation.

Discover new NLP features in Snorkel Flow\’s 2024.R3 release, including named entity recognition for PDFs + advanced sequence tagging tools.

“Task Me Anything” empowers data scientists to generate bespoke benchmarks to assess and choose the right multimodal model for their needs.

Introducing Alfred: an open-source tool for combining foundation models with weak supervision for faster development of academic data sets.

We need more labeled data than ever, so we have explored weak supervision for non-categorical applications—with notable results.

Snorkel AI placed a model at the top of the AlpacaEval leaderboard. Here’s how we built it, and how it changed AlpacaEval’s metrics.

Training large language models is a multi-layered stack of processes, each with its unique role and contribution to the model’s performance.

New unified prompting UI + RAG features, PDF annotation, Databricks MLflow integration, Snorkel Flow Studio, and datasets load 2x faster!

A brief guide on how financial institutions could use Google Dialogflow with Snorkel Flow to build better chatbots for retail banking

A proof-of-concept project that combines predictive AI + generative AI to minimize LLM’s risks while keeping their advantages.

Insurance claims processing has long required a lot of tedious and expensive human labor, but artificial intelligence (AI) can help.

GPT-3 unlocked additional capacity by automating first drafts of internal updates—including blog summaries and sample tweets.

Generative AI can write poems, recite common knowledge, and extract information. GenAI can also help quickly build predictive pipelines.

Getting better performance from foundation models (with less 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.

State Farm senior data scientist Jason Goldfarb presented “Reusable Data Cleaning Pipelines in Python” at the Future of Data-Centric AI 2022.

Claypot AI CEO Chip Huyen presented “Platform for Real-Time Machine Learning” at Snorkel AI’s Future of Data-Centric AI 2022.

Peter Davio, CTO at Black Swan Data, presented “Petabyte-Level Learning” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022.

Grammarly’s Timo Mertens presents “Toward Superhuman Communication Assistance” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022.

The Future of Data-Centric AI showcased customer to successes, took a deep look at Snorkel Flow, and announced two new solutions.

William Huang, senior data scientist at Capital One, discussed “dataset cartography” and its value at the Future of Data-Centric AI 2022.

To empower our enterprise customers, we redesigned the ML systems behind Snorkel Flow to make sure we were meeting customer needs.

Simran Arora is a machine learning researcher at Stanford University. She presented “Ask Me Anything: How are Foundation Models Changing the Way We Build Software” at Snorkel AI’s Foundation Model Virtual Summit 2023.

Foundation Models (FMs), such as GPT-3 and Stable Diffusion, mark the beginning of a new era in machine learning and artificial intelligence. What are they and how will they impact your business? Find out in our guide.

Cleanlab Co-Founder and CEO Curtis Northcutt presents his company’s automatic, universal and open-source tools to quickly clean data sets.

Understanding and quantifying people’s opinions has become increasingly important to businesses, but the way people can express multiple thoughts in the same sentence has frustrated practitioners’ efforts to extract those opinions cleanly—a problem we can solve through aspect-based sentiment analysis (ABSA).