47:55Liquid AI Head of Post Training Maxime Labonne, PhD: Edge AI and the Future of Localized Intelligence with Private, offline LLMs
In this episode of the Masters of Automation podcast, host Alp Uguray interviews Maxime Labonne, discussing the challenges and innovations in running large language models (LLMs) on edge devices. They explore the importance of post-training techniques for enhancing small models, the future of local AI models, and the integration of AI into everyday applications. The conversation also touches on the role of context in AI performance, architectural considerations, and the dual paths of AI development. Maxim shares his journey from cybersecurity to AI, the use of AI in spam detection, and the potential of agent-to-agent communication. The episode concludes with insights on the future of AI in gaming and the importance of community in AI development. Takeaways Running LLMs on edge devices presents challenges like latency and model quality. Post-training techniques are crucial for enhancing small models' performance. Local AI models can provide privacy and customization for users. Agentic workflows can enhance AI's functionality in applications. Context windows are vital for AI reasoning and performance. Model architecture significantly impacts AI capabilities and efficiency. There are two paths in AI development: AGI and interpretable models. Maxime transitioned from cybersecurity to AI due to the open community. AI can be effectively used in cybersecurity for spam detection. Agent-to-agent communication in AI is still in its infancy.