#adversarial-attack — quidproquo

ai guide Reading CMU 10-423 Generative AISep 30, 2026

Operational Risks and Alignment Theory: Tests and Assumptions | CMU 10-423 Topic Guide

Hallucination, leakage, and prompt injection are observable operational risks. Alignment theory asks which guarantees hold under specific formal assumptions. They require different evidence. Lectures 22 and 26 therefore have separate teaching guides, without turning one theorem into a universal impossibility claim.

#cmu-10423 #ai-course #cmu #ai-safety #alignment #hallucination #adversarial-attack #copyright

ai deep-dive Reading Stanford CS230 Deep LearningAug 16, 2026

Adversarial Robustness and Generative Models: It's Not Nonlinearity, It's Linearity | Stanford CS230 Lecture 4

Researchers initially assumed neural networks are easy to fool because they're nonlinear. That was wrong — Goodfellow's 2014 paper argues the primary cause is their linear nature, and high dimensionality lets every tiny perturbation compound. The second half covers generative models: GANs' three pathologies, and why diffusion sidesteps two of them by adding noise and learning to remove it.

#adversarial-attack #prompt-injection #gan #diffusion-models #stanford-cs230