  Today's heavy machine learning tasks are fueled by large datasets. Computing
is performed with power hungry processors whose performance is ultimately
limited by the data transfer to and from memory. Optics is one of the powerful
means of communicating and processing information and there is intense current
interest in optical information processing for realizing high-speed
computations. Here we present and experimentally demonstrate an optical
computing framework based on spatiotemporal effects in multimode fibers for a
range of learning tasks from classifying COVID-19 X-ray lung images and speech
recognition to predicting age from face images. The presented framework
overcomes the energy scaling problem of existing systems without compromising
speed. We leveraged simultaneous, linear, and nonlinear interaction of spatial
modes as a computation engine. We numerically and experimentally showed the
ability of the method to execute several different tasks with accuracy
comparable to a digital implementation. Our results indicate that a powerful
supercomputer would be required to duplicate the performance of the multimode
fiber-based computer.
