A deep dive into the internal workings of Large Language Models like GPT, explaining the journey from a text prompt through tokenization, embeddings, and the attention mechanism to generate a response.
The OpenAI team details the creation of a new, powerful AI agent in ChatGPT, achieved by unifying the Deep Research and Operator models. They cover its unified architecture with shared state across tools, the reinforcement learning techniques used for training, and the critical safety measures required for an agent that can take real-world actions.
A discussion on the state of technical infrastructure, focusing on how AI and Large Language Models represent a new, fourth foundational pillar alongside compute, network, and storage. The talk covers how AI is disrupting software itself, the investment landscape, and the future of the developer profession.
Misha Laskin, co-founder of Reflection AI and former researcher at Google DeepMind, discusses the company's mission to build superhuman autonomous systems. He introduces Asimov, a code comprehension agent designed to solve the 80% of an engineer's time spent on understanding complex systems, rather than just code generation. Laskin delves into the intricacies of co-designing product and research, the critical role of customer-driven evaluations, the bottlenecks in scaling reinforcement learning (RL) — particularly the "reward problem" — and why he believes the future is one of "jagged superintelligence" emerging in specific, high-value domains like coding.
Douwe Kiela, CEO of Contextual AI, explains the evolution from basic RAG to "RAG 2.0", an end-to-end, trainable system. He argues that this system-level approach, which integrates optimized document parsing, retrieval, reranking, and grounded models, is superior to relying on massive context windows alone and is a fundamental tool for next-generation AI agents.