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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.
This lecture explains how Bayesian networks compactly represent joint probability distributions through local conditional probabilities, then contrasts inefficient rejection sampling with Gibbs sampling—an MCMC method that iteratively modifies existing samples to satisfy evidence, enabling efficient approximate inference even with rare events.
This lecture transitions from model-free and model-based reinforcement learning to probabilistic reasoning, introducing Bayesian networks as a framework for representing uncertain world states. It establishes probability fundamentals—joint distributions, marginalization, and conditioning—using tensor operations (einops) to provide the mathematical foundation for efficient inference in complex domains.
Veteran AI researcher Terry Winograd argues that rather than focusing on apocalyptic futures or utopian promises, we should recognize AI as an accelerant of existing social problems—particularly in employment, resource allocation, and information integrity—that demand immediate societal attention.
Rio Wang introduces 𝚿0 (Psi-0), an open foundation model for universal humanoid locomotion-manipulation that moves beyond fixed 18-DoF manipulation systems by leveraging scalable egocentric human data collection and unified whole-body control to address the integration challenges of mobility, dexterity, and reasoning.
Andrew Ng explains Principal Component Analysis (PCA) as a classic non-probabilistic algorithm for linear dimensionality reduction that identifies principal axes of variation through eigenvector decomposition of the covariance matrix, while cautioning that practitioners often apply it unnecessarily.