Non-Euclidean Therapy for AI Trauma [video]

PATIENT ALICE: An Artificial Intelligence suffering from hallucinations of a lost puppet show. These hallucinations need to be erased.

GENERATIVE MODEL TYPE: Diffusion-based.

PRESCRIBED TREATMENT: A Latent Space Editing method that involves the Pullback, the Jacobian Matrix, Eigenfaces and SVD.

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This video experimented with a different approach, combining a science fiction mystery story told by analog VHS effects with an explanation of a very recent paper about latent space editing in diffusion models. This is my only entry to #some3

Link to the paper this was based on: "Unsupervised Discovery of Semantic Latent Directions in Diffusion Models" https://arxiv.org/abs/2302.12469

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Timestamps: [spoilers ahead] 00:00 - Patient Introduction -- [No Math] 02:08 - Manifolds and Pushforwards 06:38 - The Three Functions 09:55 - The Lost Show -- [No Math] 12:32 - Diffusion Models and the U-Net 14:13 - Matrix Multiplication and the Change of Basis Neurons 20:01 - The Jacobian Matrix 26:17 - The Pullback and the Dot Product 28:42 - A treat before treatment -- [No Math] 31:09 - The Treatment -- [No Math] 36:13 - Finding the Error 39:31 - Correlations in Matrices 42:13 - Superposition 45:23 - W^T W 47:24 - Eigenvectors of W^T W 49:38 - The Trauma -- [No Math] 51:43 - Singular Value Decomposition 54:30 - Reunion -- [No Math]