Google's JAX is a high-performance numerical computing library that incorporates composable function transformations. It lies at the intersection of Scientific Computing and Function Transformations, yielding a wide range of capabilities beyond Deep Learning model training. The key features of JAX include NumPy on Accelerators, XLA (Accelerated Linear Algebra), automatic differentiation tools, and support for general Differentiable Programming Paradigm. It is designed to work with functionally pure programs and has the potential to significantly increase the performance of scientific computing tasks.