AI Explained: Graph Neural Networks and Generative AI
August 24, 2023
10AM PT / 1PM ET
Registration is now closed. Please check back later for the recording.
Graph neural networks (GNNs) are gaining popularity in the AI community, helping ML teams build advanced AI applications that provide deep insights to tackle real-world problems. Stanford professor and co-founder at Kumo.AI, Jure Leskovec, whose work is at the intersection of graph neural networks, knowledge graphs, and generative AI, will explore how organizations can incorporate GNNs in their generative AI initiatives.
Watch this AI Explained to learn:
- Advancements in GNNs for generative AI
- Considerations of incorporating GNNs with generative AI model and LLM workflows
- Examples of real-world AI applications using GNNs, ie. drug discovery, social networks, and product recommendations, and more
AI Explained is our AMA series featuring experts on the most pressing issues facing AI and ML teams.
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Featured Speakers
Jure Leskovec
Professor of Computer Science at Stanford University and Co-founder at Kumo.AI
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Jure Leskovec is Professor of Computer Science at Stanford University, and Co-founder at Kumo.AI. He is affiliated with the Stanford AI Lab, Machine Learning Group and the Center for Research on Foundation Models. In the past, he served as a Chief Scientist at Pinterest and was an investigator at Chan Zuckerberg BioHub. Leskovec pioneered the field of Graph Neural Networks and co-authored PyG, the most widely-used graph neural network library. Research from his group has been used by many countries to fight COVID-19 pandemic, and has been incorporated into products at Facebook, Pinterest, Uber, YouTube, Amazon, and more.
Krishna Gade
Founder and CEO
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Fiddler AI
Krishna Gade is the Founder and CEO of Fiddler AI, where he is building the control plane for AI systems — helping enterprises monitor, evaluate, and control models and autonomous agents in production. Previously, he time at Microsoft working on search ranking for Bing, and later led data and machine learning teams at Twitter and Pinterest. He is most known for his work on AI Explainability at Facebook/Meta and these days he is now focused on making AI systems trustworthy, scalable, and production-ready for the enterprise.