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 Background01:47 Journey from Cybersecurity to Machine Learning03:30 The Fascination with AI and Cyber Attacks06:15 Transitioning to Post-Training at Liquid AI08:17 Liquid AI's Vision and Mission10:08 Challenges of Deploying AI on Edge Devices13:06 Techniques for Efficient Edge Model Training15:44 The State of AI Hype and Reality19:19 Evaluating AI Models and Benchmarks24:09 Future of AI Architectures Beyond Transformers31:05 Innovations in Model Architecture36:28 The Importance of Iteration in AI Development39:24 Understanding State Space Models42:53 Advice for Aspiring Machine Learning Professionals48:53 The Quest for Quality Data52:56 Integrating User Feedback into AI Systems58:13 Lessons from Machine Learning for Life

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