“𝘠𝘰𝘶 𝘥𝘰𝘯'𝘵 𝘯𝘰𝘳𝘮𝘢𝘭𝘭𝘺 𝘭𝘦𝘢𝘳𝘯 𝘩𝘰𝘸 𝘵𝘰 𝘣𝘶𝘪𝘭𝘥 𝘧𝘰𝘶𝘯𝘥𝘢𝘵𝘪𝘰𝘯 𝘮𝘰𝘥𝘦𝘭𝘴 𝘪𝘯 𝘢𝘤𝘢𝘥𝘦𝘮𝘪𝘢.” In this week’s lecture on 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 𝐟𝐨𝐫 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐲 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 at ETH Zurich, @nzuma0 and @PatrickLangerAI covered large language model (LLM) training and adaptation. One fascinating aspects of foundation models we discussed was 𝐞𝐦𝐞𝐫𝐠𝐞𝐧𝐭 𝐛𝐞𝐡𝐚𝐯𝐢𝐨𝐫. You keep scaling data, model size, and compute, and suddenly models start demonstrating capabilities they weren't explicitly trained to perform. These capabilities are difficult to predict from smaller-scale experiments. But here's the challenge: 𝐈𝐧 𝐚𝐜𝐚𝐝𝐞𝐦𝐢𝐚, 𝐲𝐨𝐮 𝐫𝐚𝐫𝐞𝐥𝐲 𝐡𝐚𝐯𝐞 𝐚𝐜𝐜𝐞𝐬𝐬 𝐭𝐨 𝐭𝐡𝐞 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐜𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬, 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞, 𝐚𝐧𝐝 𝐬𝐜𝐚𝐥𝐞 𝐧𝐞𝐞𝐝𝐞𝐝 𝐭𝐨 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 𝐭𝐡𝐞𝐬𝐞 𝐩𝐡𝐞𝐧𝐨𝐦𝐞𝐧𝐚 𝐟𝐢𝐫𝐬𝐭𝐡𝐚𝐧𝐝. We covered post-training methods for LLMs and explored emergence and scaling laws. These topics help explain how model capabilities develop, how model behavior can be shaped, and what drives recent breakthroughs in LLMs. For anyone building with AI, understanding these foundations helps inform decisions about what to automate, how to evaluate systems, and where human oversight matters. Thanks to Nicolas Zumarraga and Patrick Langer for sharing insights from their research and real-world experience at Aionic Labs and Meta. The Building Agentic AI Systems for Industry Transformation lecture takes place every Wednesday (16:15–18:00) at ETH Zurich and is led by @kev_osull, @PatrickLangerAI, @robertjakob, and Markus Kreft. Lecture link in the comments. 🔗👇