You can gain access to a world of education through Stanford Online, the Stanford School of Engineering’s portal for academic and professional education offered by schools and units throughout Stanford University. https://online.stanford.edu/ Our robust catalog of degree programs, credit-bearing education, professional certificate programs, and free and open content is developed by Stanford faculty, enabling you to expand your knowledge, advance your career, and enhance your life. Stanford Online is operated and managed by the Stanford Engineering Center for Global & Online Education (CGOE). CGOE expands access to Stanford teaching and research, working in collaboration with faculty in the School of Engineering and throughout Stanford University to design and deliver extensive global, online, and enterprise education to a global audience.
This lecture introduces score matching as an alternative to DDPM for generative modeling, where instead of predicting noise directly, models estimate the gradient of log probability density (the 'score') to guide sampling from noise toward data distributions using Langevin dynamics.
This seminar reframes Moravec's Paradox through control theory, demonstrating why robot learning suffers from exponential compounding errors that symbolic tasks avoid, and identifies action chunking and generative control policies as the essential algorithmic breakthroughs that enabled the 2023 inflection point in robotic manipulation capabilities.
The speaker argues that to solve persistent human problems in HCI, designers must move beyond building better tools and instead critically reimagine entire socio-technical ecosystems. Through examples in event planning, crowdsourcing, social connection, and education, he demonstrates how redesigning human practices—what he terms "critical technical practice"—can unlock values that pure technological advancement has failed to address.
This lecture covers Project 3 results on reachability analysis before introducing explainability methods for safety-critical AI systems, focusing on how to attribute failures to specific time steps using Shapley values from game theory when simple ablation studies fail due to correlated noise patterns.
Healthcare AI adoption is consolidating around Epic's EHR platform while patients increasingly turn to consumer AI tools due to access shortages, creating tension between democratized information and safety risks as laypeople lack expertise to evaluate medical advice.
Percy Liang reflects on AI's transformation from academic curiosity to global infrastructure, debunking sci-fi misconceptions about capabilities while arguing that academia's role in long-term research and critical evaluation remains essential as the job market shifts away from traditional entry-level software engineering.
This lecture examines AI's economic impact through the lens of supply chains and organizational strategy, demonstrating why understanding compute monopolies, labor market shifts, and corporate decision-making is as critical as tracking algorithmic capabilities.
This lecture argues that AI developers bear unique ethical responsibility for societal outcomes, framing AI as a dual-use technology that requires active steering toward beneficial applications while preventing misuse and accidental harms through rigorous auditing and an ecosystem-aware approach.
This lecture introduces modern language models as industrial-scale systems requiring millions of dollars and trillions of tokens to train, explaining their fundamental operation as auto-regressive next-token predictors that encode language structure through massive statistical modeling.
This lecture introduces First Order Logic as a powerful extension of propositional logic that uses objects, predicates, functions, and quantifiers to compactly represent complex relationships and generalizations without enumerating every possible instance.
This lecture introduces logic as a formal language for knowledge representation and reasoning, contrasting it with probabilistic methods and natural language. It establishes the foundational framework of syntax, semantics, and inference rules, then dives into propositional logic's mechanics including formulas, models, and interpretation functions.
This lecture explains how to learn Bayesian network parameters from fully observed data through simple counting and normalization, while reviewing probabilistic inference methods and d-separation rules for determining conditional independence.