Stanford Online

Stanford Online

This Stanford lecture establishes aesthetics and prompt adherence as the dual pillars for evaluating text-to-image models, compares human evaluation methods from noisy absolute ratings to reliable pairwise comparisons, and details the ELO rating system for robust model benchmarking before addressing the scalability crisis that necessitates automated metrics.

Stanford Online

Former federal prosecutor and tech security chief Joe Sullivan recounts his journey from prosecuting cybercrime to leading security at eBay, Facebook, Uber, and Cloudflare, sharing hard-won lessons on the critical importance of transparency in security incidents through the lens of his personal prosecution for the 2016 Uber data breach cover-up.

Stanford Online

This lecture explains why RLHF hits overoptimization limits with learned reward models, and how RLVR (Reinforcement Learning from Verifiable Rewards) enables unlimited compute scaling on verifiable tasks like math and coding through simpler algorithms like GRPO.

Stanford Online

This lecture explains how post-training transforms raw pre-trained models like GPT-3 into instruction-following systems like ChatGPT through supervised fine-tuning and reinforcement learning, emphasizing that high-quality data curation matters more than algorithmic sophistication.

Stanford Online

This lecture details the pre-training data pipeline, covering the transformation of raw HTML and PDFs into linear text and classifier-based filtering strategies to curate domain-specific datasets, while emphasizing the strategic trade-off between data quality and training duration.

Stanford Online

Sachin Katti, OpenAI's head of industrial compute, details the infrastructure economics driving the AI supercycle, explaining how the company plans to scale to 30 gigawatts by 2030 while navigating the shift from training to inference-heavy agentic workloads and managing massive energy and supply chain constraints.

Stanford Online

Google DeepMind researcher Vivek Natarajan discusses the development of Co-Scientist, an AI system designed to act as a collaborative partner for scientific discovery by moving beyond fast System 1 thinking to rigorous System 2 reasoning, emphasizing that true scientific AI requires the generality of human cognition rather than narrow specialization.

Stanford Online

Google's infrastructure lead Amin Vahdat argues that AI's critical constraint is not raw gigawatts but value delivered per unit of power, requiring a shift from five-nines reliability to throughput-optimized systems where 99.9% uptime is acceptable if it doubles usable capacity.

Stanford Online

Former OpenAI researcher Yash Bottle traces AI model evolution from AlexNet to reasoning agents, identifying continual learning as the next bottleneck while explaining why code dominance stems from verifiable rewards and why enterprises must leverage proprietary data to bridge the gap between frontier models and business context.

Stanford Online

Stanford instructor Apur frames generative AI as a supercycle with inverted economics where semiconductor and infrastructure costs dominate revenues while application-layer value remains elusive, questioning whether this structure represents a temporary capex cycle or a new permanent equilibrium.

Stanford Online

This seminar presents a neuro-symbolic approach to robot learning that combines neural visual representations with physics-based constraint optimization to enable one-shot skill acquisition, achieving over 90% success rates on novel objects compared to 0% for standard policy learning methods.

Stanford Online

UC Berkeley's Icon Lab presents game-theoretic frameworks enabling robots to safely interact with humans and other agents by modeling joint prediction as potential games, reducing computational costs by 20x while solving the challenge of multiple social equilibria in real-time navigation.