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Backed by a $3M commitment, the program funds open-source datasets, benchmarks, and evaluation artifacts that shape how frontier AI systems are built and evaluated.
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Large language models have enormous potential. But what are they? Where did they come from? And how can you make them work better?
Stanford assistant professor James Zou, presents “Responsible Data-Centric AI for Healthcare and Medicine” at The Future of Data-Centric AI.
Snorkel AI has accepted the first batch of applications for its first annual virtual poster competition. But there’s still time to add yours to the mix.
Join us on June 7-8 to learn how to use your data to build your AI moat at The Future of Data-Centric AI 2023 free virtual conference.
Sharon Li is an assistant professor at the University of Wisconsin-Madison. She presented “Detecting Data Distributional Shift: Challenges and Opportunities” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022. The talk covered a novel approach for handling out-of-distribution objects.
Harvard Professor Vijay Janapa Reddi’s presentation: “DataPerf: Benchmarks for data” from Snorkel AI’s 2022 Future of Data-Centric AI event.
We introduce compositional soft prompting (CSP), a parameter-efficient learning technique to improve the zero-shot compositionality of large-scale pretrained vision-language models (VLMs) like CLIP. We develop CSP for compositional zero-shot learning, the task of predicting unseen attribute-object compositions (e.g., old cat and young tiger). VLMs have a flexible text encoder that can represent arbitrary classes as natural language prompts but they…
A long standing goal of the data management community is to develop general, automated systems that ingest semi-structured documents and output queryable tables without human effort or domain specific customization. Given the sheer variety of potential documents, state-of-the art systems make simplifying assumptions and use domain specific training. In this work, we ask whether we can maintain generality by using…
