06-15, 12:00–12:40 (Europe/London), Minories
As the field of natural language processing advances and new ideas develop, we’re seeing more and more ways to use compute efficiently, producing AI systems that are cheaper to run and easier to control. Large Language Models (LLMs) have enormous potential, but also challenge existing workflows in industry that require modularity, transparency and data privacy. In this talk, I'll show some practical solutions for using the latest state-of-the-art models in real-world applications and distilling their knowledge into smaller and faster components that you can run and maintain in-house.
I'll share some real-world case studies and approaches for using large generative models at development time instead of runtime, curate their structured predictions with an efficient human-in-the-loop workflow and distill task-specific components as small as 10mb that run cheaply, privately and reliably, and that you can compose into larger NLP systems.
If you’re trying to build a system that does a particular thing, you don’t need to transform your request into arbitrary language and call into the largest model that understands arbitrary language the best. The people developing those models are telling that story, but the rest of us aren’t obliged to believe them.
No previous knowledge expected
Ines Montani is a developer specializing in tools for AI and NLP technology. She’s the co-founder and CEO of Explosion and a core developer of spaCy, a popular open-source library for Natural Language Processing in Python, and Prodigy, a modern annotation tool for creating training data for machine learning models.