  Randomized artificial neural networks such as extreme learning machines
provide an attractive and efficient method for supervised learning under
limited computing ressources and green machine learning. This especially
applies when equipping mobile devices (sensors) with weak artificial
intelligence. Results are discussed about supervised learning with such
networks and regression methods in terms of consistency and bounds for the
generalization and prediction error. Especially, some recent results are
reviewed addressing learning with data sampled by moving sensors leading to
non-stationary and dependent samples.
  As randomized networks lead to random out-of-sample performance measures, we
study a cross-validation approach to handle the randomness and make use of it
to improve out-of-sample performance. Additionally, a computationally efficient
approach to determine the resulting uncertainty in terms of a confidence
interval for the mean out-of-sample prediction error is discussed based on
two-stage estimation. The approach is applied to a prediction problem arising
in vehicle integrated photovoltaics.
