Learning from Machine Learning Maxime Labonne: Designing beyond Transformers | Learning from Machine Learning #12
25 snips
May 28, 2025 
Maxime Labonne, Head of Post-Training at Liquid AI and author of the LLM Engineers Handbook, dives into the future of AI design. He emphasizes that growth is achieved not just through bigger models, but through smarter, more efficient ones. Maxime explores the critical role of data quality, suggesting accuracy and diversity are key. He also discusses the challenges of deploying AI on edge devices and critiques the current hype surrounding AI technologies, advocating for a more nuanced understanding of their capabilities.
AI Snips
Chapters
Books
Transcript
Episode notes
ML Defines Cyber Attacks Better
- Maxime Labonne started in cybersecurity, using ML to detect cyber attacks, which felt like sci-fi to him.
- He found that ML models can subtly define cyber attacks better than humans can.
Journey from Cybersecurity to NLP
- Maxime transitioned from AI in cybersecurity to NLP and code models at JPMorgan Chase before ChatGPT.
- He enjoyed training internal code completion models tailored to the bank's unique codebase.
Challenges of Edge AI Deployment
- Deploying AI on edge devices faces challenges from limited model capacity and diverse hardware.
- Smaller models need task-specific training and efficient deployment to handle memory and inference constraints.
