Latest posts
- Auto LF generation: Lots of little models, big benefits
- 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… ... - Building a COVID fact-checking system with external knowledge
- 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… ... - Snorkel AI FAQ
- 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 What is a labeling function? How does the labeling function know what the label should be? What is the output of a labeling… ... - Panel discussion: Academic and industry perspectives on ethical AI - 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,… ...
- Event recap: Adopting trustworthy AI for government
- We're currently experiencing such a rapid AI revolution and adoption of technologies, ranging from autonomous cars to virtual assistants and robotic surgeries and so much more, making it challenging for our government agencies to keep up. Especially when adding AI technologies to the mix, it can be even harder to… ... - Data extraction from SEC filings (10-Ks) with Snorkel Flow
- Leveraging Snorkel Flow to extract critical data from annual quarterly reports (10-Ks) Introduction It can surprise those who have never logged into EDGAR how much information is available in annual reports from public companies. You can find tactical details like the names of senior leadership, top shareholders, and more strategic information like… ... - Liger: Fusing foundation model embeddings & weak supervision
- Showcasing Liger—a combination of foundation model embeddings to improve weak supervision techniques. Machine learning whiteboard (MLW) open-source series In this talk, Mayee Chen, a PhD student in Computer Science at Stanford University focuses on her work combining weak supervision and foundation model embeddings that improve two essential aspects of current… ... - AI in cybersecurity an introduction and case studies
- An introduction to AI in cybersecurity with real-world case studies in a Fortune 500 organization and a government agency Despite all the recent advances in artificial intelligence and machine learning (AI/ML) applied to a vast array of application areas and use cases, success in AI in cybersecurity remains elusive. The… ...
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