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20 years ago, i left my country, my culture, my food, and family to embark on a journey that started with the release of my very first body of music! So many of you were a part of my life and career since the very beginning, and some of you have joined the adventure along the way. I’m forever grateful to all of you. Each of you played a very crucial role in where this journey has taken me thus far! I just wanted to take this moment to say thank you! Thank you for the greatest first 20 years ever! 20 years of the most loyal, die-hard fans that don’t play about me whatsoever, 20 years of hard work and hardworking teams around me, 20 years of lessons, 20 years of unforgettable experiences and accomplishments, 20 years of my family being my number one support system, and to all the people who said yes to me and gave me a chance before it was “cool” to (Execs, DJ’s, writers, producers, dancers, choreographers, directors, fashion designers, photographers, glam, journalists, brands, mentors, etc etc) 20 years worth of thanks to you!! I thank God, He’s been very generous to me and the Glory belongs to Him! #R20# 🙏🏿⚓️

🌍 "When can self-supervised learning recover the world's true structure?" This theory paper from LeCun and colleagues proves the answer is: only when the latent variables are Gaussian. Title: When Does LeJEPA Learn a World Model? URL: 💡 Overview The paper pins down when LeJEPA (JEPA + Gaussian regularization SIGReg + alignment) can recover the world's latent variables linearly, up to rotation, from nonlinear observations. The key condition: the latents are Gaussian and evolve under an OU process. ⚠️ The problem If a representation distorts the world's true degrees of freedom, reliable planning and compositional generalization break down. It was unclear when self-supervised learning provably recovers world structure. 🛠 Approach and core insight ・The optimal representation extracts the "slowest features" of the latent process, ordered by eigenvalue ・Via Hermite polynomials and Mehler's formula, cross-view correlation decays as ρ^d for degree-d nonlinearity ・So alignment penalizes every degree of nonlinearity, making the linear map the unique optimum ・With linear identifiability, planning in latent space yields the same optimal actions as the true world (directly usable for control) ・Conversely, demanding the optimum always be linear forces the latent distribution to be Gaussian (uniqueness) 📊 Results ・SIGReg and VICReg keep R² > 0.999 for linear recovery up to 1024 dimensions ・Sweeping the generalized-normal family, R² peaks sharply at α=2 (Gaussian) ・In pixel-based robot control, Gaussian OU pairs hit R²=0.95, while non-Gaussian real trajectories stay at R²≤0.5 ・Control cost tracks R² monotonically, and the Gaussian encoder is oracle-level #WorldModels# #SelfSupervisedLearning#