Stanford CS25: Transformers United V6 I From Representation Learning to World Modeling

Stanford Online

The speaker challenges the game industry's outdated assumption that players primarily seek competition, presenting 2024 data showing only 18% of gamers are motivated by competition while 50% seek stress relief and 40% want community. They introduce a framework of nine motivators divided into classic (Fun, Mastery, Competition, Immersion, Meditation, Comfort) and modern (Self-expression, Companionship, Education), arguing that successful games must layer social and creative motivators onto traditional designs to serve contemporary player needs.

Stanford Online

Base 10 CEO Tuhin explains why AI inference is shifting from frontier models to custom post-trained models as companies scale, driven by 70-90% cost savings, latency requirements, and the strategic need to own proprietary data rather than feed it to potential competitors.

Stanford Online

Dan Fu explains how LLM inference serves as the engine converting electricity into intelligence, detailing the lifecycle of requests through modern serving systems and emphasizing that GPU kernel expertise enables full-stack ML innovation.

Stanford Online

Rob Platt argues that modern Vision-Language-Action models discard geometric structure, requiring massive datasets to relearn physical constraints. He proposes hybrid approaches that embed geometric symmetries (equivariance) directly into learning architectures, enabling data-efficient robot policies that respect physical laws.