A deep dive into the architectural and economic foundations of memory for AI agents. The talk explores the core tradeoffs between classical data storage and dynamic agent behavior, introduces a human-inspired framework for memory, and discusses practical strategies and future directions for building reliable, evolving AI systems.
Jerry from LlamaIndex explains why naive Retrieval-Augmented Generation (RAG) fails in production and dives deep into advanced data quality techniques—from parsing complex documents and hierarchical indexing to chunking best practices—required to build robust, high-quality LLM applications.
Dell's Shirish Gupta and Ish Shah discuss the complexities developers face in leveraging on-device accelerators like NPUs and GPUs. They introduce Dell ProAI Studio, a solution designed to abstract away hardware-specific toolchains, enabling developers to easily run AI workloads locally for benefits like speed, cost, security, and offline capability.
Erik Torenberg, Patrick Hsu (Arc Institute), and Jorge Conde (a16z) discuss Arc's moonshot to create 'virtual cells' using foundation models to simulate biology. They cover why science is slow, how AI can accelerate drug discovery by predicting cellular perturbations, and the remaining bottlenecks in clinical trials and capital intensity that the biotech industry faces.
Ethan Smith, CEO of Graphite, explains Answer Engine Optimization (AEO), the new frontier of getting your product featured in answers from LLMs like ChatGPT. He shares his playbook for ranking in these new systems, revealing why AEO traffic can convert 6x better than traditional search and how even early-stage startups can achieve immediate wins by focusing on landing pages, YouTube videos, and authentic Reddit engagement.
Sergey Levine, co-founder of Physical Intelligence, outlines the path to general-purpose robots, predicting a 'self-improvement flywheel' could lead to fully autonomous household robots by 2030. He discusses the architecture of vision-language-action models, the critical role of embodiment in solving the data problem, and how robotics will scale faster than self-driving cars.