On this episode of Learning from Machine Learning, I had the privilege of speaking with Maxime Labonne, Head of Post-Training at Liquid AI. We traced his journey from cybersecurity to the cutting edge of model architecture. Maxime shared how the future of AI isn't just about making models bigger—it's about making them smarter and more efficient.
Maxime's work demonstrates that challenging established paradigms requires taking steps backward to leap forward. His framework for data quality—accuracy, diversity, and complexity—offers a blueprint for anyone working with machine learning systems.
Most importantly, Maxime's perspective on learning itself—treating knowledge acquisition like training data exposure—reminds us that growth comes from diverse, high-quality experiences across different contexts. Whether you're training a model or developing yourself, the principles remain remarkably similar.
Thank you for listening. Be sure to subscribe and share with a friend or colleague. Until next time... keep on learning.
00:46 Introduction and Maxime's Background
01:47 Journey from Cybersecurity to Machine Learning
03:30 The Fascination with AI and Cyber Attacks
06:15 Transitioning to Post-Training at Liquid AI
08:17 Liquid AI's Vision and Mission
10:08 Challenges of Deploying AI on Edge Devices
13:06 Techniques for Efficient Edge Model Training
15:44 The State of AI Hype and Reality
19:19 Evaluating AI Models and Benchmarks
24:09 Future of AI Architectures Beyond Transformers
31:05 Innovations in Model Architecture
36:28 The Importance of Iteration in AI Development
39:24 Understanding State Space Models
42:53 Advice for Aspiring Machine Learning Professionals
48:53 The Quest for Quality Data
52:56 Integrating User Feedback into AI Systems
58:13 Lessons from Machine Learning for Life
