Applying machine learning to the universe's mysteries
Scientists teach machines to analyze simulations of exotic subatomic 'soup' The colored lines represent calculated particle tracks from particle collisions occurring within Brookhaven National Laboratory's STAR detector at the Relativistic Heavy Ion Collider, and an illustration of a digital brain. The yellow-red glow at center shows a hydrodynamic simulation of quark-gluon plasma created in particle collisions. Summary: Physicists have demonstrated that computers are ready to tackle the universe's greatest mysteries -- they used neural networks to perform a deep dive into data simulating the subatomic particle soup that may have existed just microseconds after the big bang. Computers can beat chess champions, simulate star explosions, and forecast global climate. We are even teaching them to be infallible problem-solvers and fast learners.