A thermodynamic approach to machine learning: How optimal transport theory can improve generative models
Joint research led by Sosuke Ito of the University of Tokyo has shown that nonequilibrium thermodynamics, a branch of physics that deals with constantly changing systems, explains why optimal transport theory, a mathematical framework for the optimal change of distribution to reduce cost, makes generative models optimal. As nonequilibrium thermodynamics has yet to be fully leveraged in designing generative models, the discovery offers a novel thermodynamic approach to machine learning research. The findings were published in the journal Physical Review X.
The increasing capabilities of large generative models and their ever more widespread deployment have raised concerns about their reliability, safety, and potential misuse. To address these issues, recent works have proposed to control model generation by steering model activations in order to effectively induce or prevent the emergence of concepts…
The majority of organizations don’t feel ready for the AI era. In fact, 62% say they don’t have the expertise they need to unlock AI’s full potential.1 As the leader of learning for Google Cloud, the only thing that surprises me about that number is how low it is. I…