Removing the AI Bottleneck to Roll Out the “Model T”

artificial intelligence
12/6/2021

​​As Young has discussed with previous guests, he and Alex share perspectives on how data, specifically trained data, is the new oil, but that companies were getting bottlenecked because they couldn’t label this data for training. That is where Snorkel’s programmatic approach to labelling comes into play, helping companies to refine their data and run the e​​ngine of their “Model T,” the first step in AI applications before we get to the flying cars. 

removing-the-ai-bottleneck-to-roll-out-the-model-t

Alex Ratner

Co-Founder & CEO at Snorkel AI and an Assistant Professor at the University of Washington

​​While pursuing his Ph.D. in Computer Science at Stanford University in 2019, Alex Ratner led the development of Snorkel, an open source library for programmatic training data labeling, enabling deployment of large-scale AI applications in hours or days rather than months or years. After deployment at companies like Google, Apple, and Intel, academic labs and government agencies, Ratner co-founded Snorkel AI, which supports a commercial platform version of Snorkel.

Ratner joined the University of Washington’s Allen School as an Assistant Professor in 2018, where he works on systems and techniques for making machine learning more practical for use in real-world settings ranging from medicine to industry. Alex focuses especially on data management and weak supervision techniques for training machine learning models in settings where large expert-labeled "training datasets" are not available or practical to create.

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