LIVE WEBINAR
How to align LLMs to enterprise objectives/policies
June 18, 2024
10:00 AM PT / 1:00 PM ET
Chief Scientist
Snorkel AI
Senior Research Scientist
Snorkel AI
Register now
By submitting this form, I agree to the Terms of Use and acknowledge that my information will be used in accordance with the Privacy Policy.
AI in the enterprise often fails to meet expectations because the underlying LLMs are not properly aligned to the business itself.
The biggest challenge enterprise AI teams face is aligning LLMs to domain- and enterprise-specific objectives and policies – and ensuring they’re compliant with organizational standards and ethics.
In this webinar, we'll introduce an enterprise alignment workflow which combines programmatic data development, taxonomy-guided data augmentation and the latest LLM fine-tuning techniques – and show how we applied it at top finance, insurance and healthcare companies to improve the accuracy of their AI assistants and chatbots by 20+ points.
Join us and learn how to:
- Align AI chatbots to professional and ethical standards
- Align AI assistants to industry and enterprise policies
- Improve AI accuracy and compliance by 20+ points
This webinar is for enterprises looking to accelerate the delivery of production AI up to 100X faster.
Date: June 18, 2024
Time: 10:00 AM PST | 1:00 PM EST
Speakers
Fred Sala
Chief Scientist
Snorkel AI
Frederic Sala is Chief Scientist at Snorkel AI and an assistant professor in the Computer Sciences Department at the University of Wisconsin-Madison. His research studies the fundamentals of data-driven systems and machine learning, with a focus on foundation models, automated machine learning, learning with limited data. Previously, he was a postdoctoral researcher at Stanford. He received his Ph.D. in electrical engineering from UCLA.
Tom Walshe
Senior Research Scientist
Snorkel AI
Tom Walshe is a Senior Research Scientist at Snorkel AI. Before Snorkel, Tom worked in LegalTech and finance services, where he focussed on building end-to-end AI systems and researching data-centric AI. Prior to industry, Tom completed a PhD in Computer Science from the University of Oxford.