The article discusses how machine learning is being used to calculate the macroscopic world that would emerge from string theory, a theory that posits the existence of tiny, invisible extra dimensions. These calculations have been difficult due to the enormous number of possibilities, but recent advances in artificial intelligence have made it possible to approximate the shapes of the Calabi-Yau manifolds, the objects that resemble loofahs and host quantum fields in string theory. The calculations have been able to reproduce the number of particles in the standard model, but not their specific masses or interactions. The long-term goal is to use these calculations to predict new physical phenomena beyond the standard model. The article also mentions that some physicists are skeptical of the usefulness of string theory and the role that machine learning will play in it.
The second law of thermodynamics is among the most sacred in all of science, but it has always rested on 19th century arguments about probability. New arguments trace its true source to the flows of quantum information.
Driven and dissipative systems far from thermodynamic equilibrium show a rich variety of patterns, such as the distribution of earthquakes and the patchwork of tree clusters in a forest. Sustained by a permanent input of energy, these phenomena do not follow the same energy minimization principle that organizes systems in equilibrium. Instead, driven and dissipative systems appear to spontaneously develop order through self-organized criticality—a balancing act between energy loss and energy gain.
