Moving from Manual to Programmatic Labeling Labeling training data by hand is exhausting. It’s tedious, slow, and expensive—the de facto bottleneck most AI/ML teams face today 1. Eager to alleviate this pain point of AI development, machine learning practitioners have long sought ways to automate this labor-intensive labeling process (i.e., “automated data labeling”) 2, and have reached for classic approaches…
The Future of Data-Centric AI Talk Series Background Alex Ratner is CEO and co-founder of Snorkel AI and an Assistant Professor of Computer Science at the University of Washington. He recently joined the Future of Data-Centric AI event, where he presented the principles of data-centric AI and where it’s headed. If you would like to watch his presentation in full,…
Machine Learning Whiteboard (MLW) Open-source Series Today, Ryan Smith, machine learning research engineer at Snorkel AI, talks about prompting methods with language models and some applications they have with weak supervision. In this talk, we’re essentially going to be using this paper as a template—this paper is a great survey over some methods in prompting from the last few years…
The Snorkel AI founding team started the Snorkel Research Project at Stanford AI Lab in 2015, where we set out to explore a higher-level interface to machine learning through training data. This project was sponsored by Google, Intel, DARPA, and several other leading organizations and the research was represented in over 40 academic conferences such as ACL, NeurIPS, Nature and…
The Future of Data-Centric AI Talk Series Background Roshni Malani received her PhD in Software Engineering from the University of California, San Diego, and has previously worked on Siri at Apple and as a founding engineer for Google Photos. She gave a presentation at the Future of Data-Centric AI virtual conference in September 2021. Her presentation is below, lightly edited…
Machine Learning Whiteboard (MLW) Open-source Series We launched the machine learning whiteboard series (MLW) was launched earlier this year as an open-invitation forum to brainstorm ideas and discuss the latest papers, techniques, and workflows in artificial intelligence. Everyone interested in learning about machine learning can participate in an informal and open environment. If you are interested in learning about ML,…
ScienceTalks with Abigail See. Diving into the misconceptions of AI, the challenges of natural language generation (NLG), and the path to large-scale NLG deployment In this episode of Science Talks, Snorkel AI’s Braden Hancock chats with Abigail See, an expert natural language processing (NLP) researcher and educator from Stanford University. We discuss Abigail’s path into machine learning (ML), her previous…
Machine Learning Whiteboard (MLW) Open-source Series For our new visitors, we started our machine learning whiteboard (MLW) series earlier this year as an open-invite space to brainstorm ideas and discuss the latest papers, techniques, and workflows in the AI space. In which, we emphasize an informal and open environment to everyone interested in learning about machine learning. So, if you are interested…
Enabling iterative development workflows with Snorkel Flow’s Application Studio. Consider this scenario— we’re AI engineers, and we’re building a social media monitoring application to track the sentiment of Fortune 500 company mentions in the news.
