<p>On this episode of <strong>Learning from Machine Learning</strong>, 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.</p><p>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.</p><p>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.</p><p>Thank you for listening. Be sure to subscribe and share with a friend or colleague. Until next time... <strong>keep on learning.</strong></p><p></p><p>00:46 Introduction and Maxime's Background</p><p>01:47 Journey from Cybersecurity to Machine Learning</p><p>03:30 The Fascination with AI and Cyber Attacks</p><p>06:15 Transitioning to Post-Training at Liquid AI</p><p>08:17 Liquid AI's Vision and Mission</p><p>10:08 Challenges of Deploying AI on Edge Devices</p><p>13:06 Techniques for Efficient Edge Model Training</p><p>15:44 The State of AI Hype and Reality</p><p>19:19 Evaluating AI Models and Benchmarks</p><p>24:09 Future of AI Architectures Beyond Transformers</p><p>31:05 Innovations in Model Architecture</p><p>36:28 The Importance of Iteration in AI Development</p><p>39:24 Understanding State Space Models</p><p>42:53 Advice for Aspiring Machine Learning Professionals</p><p>48:53 The Quest for Quality Data</p><p>52:56 Integrating User Feedback into AI Systems</p><p>58:13 Lessons from Machine Learning for Life</p>