Resource library

If you were ever amazed at how Google accurately finds the answer to your question just by a few keywords, you’ve witnessed the power of named entity recognition (NER). By quickly and accurately identifying different entities in a sea of unstructured articles, like names of people, places, and organizations, the search engine can figure out each article’s main topics and…

The future of data-centric AI talk series In this talk, Assistant Professor of Biomedical Data Science at Stanford University, James Zou, discusses the work he and his team have been doing from a data-centric perspective to trustworthy and interpretable AI. If you would like to watch James’ presentation, we have included it below, or you can find the entire event…

Gregory Ihrie is the Chief Technology Officer for the FBI, responsible for technology, innovation, and strategy. He also leads the FBI’s efforts in advancing the bureau’s management, policy, and governance of AI systems. Ihrie chairs the FBI’s Scientific Working Group on Artificial Intelligence, as well as the Department of Justice’s AI Committee of Interest. He is one of three officers…

The future of data-centric AI talk series  Don’t miss the opportunity to gain an in-depth understanding of data-centric AI and learn best practices from real-world implementations. Connect with fellow data scientists, machine learning engineers, and AI leaders from academia and industry with over 30 virtual sessions. Save your seat at The Future of Data-Centric AI. Happening on August 3-4, 2022….

30+ sessions by 40+ speakers in 2 action-packed days Last year we organized The Future of Data-Centric AI conference to explore the shift from model-centric to data-centric AI. Speakers included researchers and industry experts such as Andrew Ng (Landing AI), Anima Anandkumar (NVIDIA), Chris Re (Stanford AI Lab), Michael DAndrea (Genentech), Skip McCormick (BNY Mellon), Imen Grida Ben Yahia (Orange)…

Constructing labeling functions (LFs) is at the heart of using weak supervision. We often think of these labeling functions as programmatic expressions of domain expertise or heuristics. Indeed, much of the advantage of weak supervision is that we can save time—writing labeling functions and applying them to data at scale is much more efficient compared to hand-labeling huge numbers of…

Powerful resources to leverage as labeling functions In this post, we’ll use the COVID-FACT dataset to demonstrate how to use existing resources as labeling functions (LFs), to build a fact-checking system. The COVID-FACT dataset contains 4086 claims about the COVID-19 pandemic; it contains claims, evidence for the claims, and contradictory claims refuted by the evidence. The evidence retrieval is formulated…

Browse through these FAQ to find answers to commonly raised questions about Snorkel AI, Snorkel Flow, and data-centric AI development. Have more questions? Contact us. Programmatic labeling Use cases 1. What is a labeling function? A Labeling Function (LF) is an arbitrary function that takes in a data point and outputs a proposed label or abstains. The logic used to…

This post showcases a panel discussion on the academic and industry perspectives of ethical AI, which was moderated by Director of Federal Strategy and Growth, Alexis Zumwalt, Fouts Family Early Career Professor and Lead of Ethical AI (NSF AI Institute AI4OPT), Georgia Institute of Technology, Swati Gupta, Chief Data Officer, Department of the Navy, Thomas Sasalsa, Senior Manager of Responsible…