  We train a model atom to recognize hand-written digits between 0 and 9,
employing intense light--matter interaction as a computational resource. For
training, individual images of hand-written digits in the range 0-9 are
converted into shaped laser pulses (data input pulses). Simultaneously with an
input pulse, another shaped pulse (program pulse), polarized in the orthogonal
direction, is applied to the atom and the system evolves quantum mechanically
according to the time-dependent Schr\"odinger equation. The purpose of the
optimal program pulse is to direct the system into specific atomic final states
that correspond to the input digits. A success rate of about 40\% is
demonstrated here for a basic optimization scheme, so far limited by the
computational power to find the optimal program pulse in a high-dimensional
search space. This atomic-intelligence image-recognition scheme is scalable
towards larger (e.g. molecular) systems, is readily reprogrammable towards
other learning/classification tasks and operates on time scales down to tens of
femtoseconds. It has the potential to outpace other currently implemented
machine-learning approaches, including the fastest optical on-chip neuromorphic
systems and optical accelerators, by orders of magnitude.
