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Virtual fireside chat

AI Explained: The Ins and Outs of Foundation Models

Wednesday, February 15, 2023
10 AM PT / 1 PM ET
Registration is now closed. Please check back later for the recording.

Foundation models, from GPT-3 to DALL-E 2, are revolutionizing AI applications, but using these large, complex models brings a number of engineering challenges. So how can teams build their own generative AI?

Watch this AI Explained to learn:

  • How foundation models are shaping AI
  • Engineering challenges in building generative AI
  • Real-world applications for these models

AI Explained is our new AMA series featuring experts on the most pressing issues facing AI and ML teams.

Can’t attend live? Recordings will be available to all registrants after the event.

Speakers
Alex Ratner
Co-founder and CEO
at
Snorkel AI
Alex Ratner is the Co-founder and CEO of Snorkel AI and an Assistant Professor of Computer Science at the University of Washington. Prior to Snorkel AI and UW, he completed his Ph.D. in CS advised by Christopher Ré at Stanford, where he started and led the Snorkel open-source project, and where his research focused on applying data management and statistical learning techniques to emerging machine learning workflows such as creating and managing training data and applying this to real-world problems in medicine, knowledge base construction, and more. Previously, he earned his A.B. in Physics from Harvard University.
Krishnaram Kenthapadi
Chief AI Officer & Scientist
at
Fiddler AI
Prior to Fiddler, he was a Principal Scientist at Amazon AWS AI and LinkedIn AI, where he led the fairness, explainability, privacy, and model understanding initiatives. Krishnaram received his Ph.D. in Computer Science from Stanford University in 2006. He serves regularly on the program committees of KDD, WWW, WSDM, and related conferences. His work has been recognized through awards at NAACL, WWW, SODA, CIKM, ICML AutoML workshop, and Microsoft’s AI/ML conference (MLADS). He has published 50+ papers, with 4500+ citations and filed 150+ patents (70 granted).

Foundation models, from GPT-3 to DALL-E 2, are revolutionizing AI applications, but using these large, complex models brings a number of engineering challenges. So how can teams build their own generative AI?

Watch this AI Explained to learn:

  • How foundation models are shaping AI
  • Engineering challenges in building generative AI
  • Real-world applications for these models

AI Explained is our new AMA series featuring experts on the most pressing issues facing AI and ML teams.

Speakers
Alex Ratner
Co-founder and CEO
at
Snorkel AI
Alex Ratner is the Co-founder and CEO of Snorkel AI and an Assistant Professor of Computer Science at the University of Washington. Prior to Snorkel AI and UW, he completed his Ph.D. in CS advised by Christopher Ré at Stanford, where he started and led the Snorkel open-source project, and where his research focused on applying data management and statistical learning techniques to emerging machine learning workflows such as creating and managing training data and applying this to real-world problems in medicine, knowledge base construction, and more. Previously, he earned his A.B. in Physics from Harvard University.
Krishnaram Kenthapadi
Chief AI Officer & Scientist
at
Fiddler AI
Prior to Fiddler, he was a Principal Scientist at Amazon AWS AI and LinkedIn AI, where he led the fairness, explainability, privacy, and model understanding initiatives. Krishnaram received his Ph.D. in Computer Science from Stanford University in 2006. He serves regularly on the program committees of KDD, WWW, WSDM, and related conferences. His work has been recognized through awards at NAACL, WWW, SODA, CIKM, ICML AutoML workshop, and Microsoft’s AI/ML conference (MLADS). He has published 50+ papers, with 4500+ citations and filed 150+ patents (70 granted).
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