  We present recent results from the initial testing of an Artificial Neural
Network (ANN) based tomographic reconstructor Complex Atmospheric Reconstructor
based on Machine lEarNing (CARMEN) on Canary, an Adaptive Optics demonstrator
operated on the 4.2m William Herschel Telescope, La Palma. The reconstructor
was compared with contemporaneous data using the Learn and Apply (L&A)
tomographic reconstructor. We find that the fully optimised L&A tomographic
reconstructor outperforms CARMEN by approximately 5% in Strehl ratio or 15nm
rms in wavefront error. We also present results for Canary in Ground Layer
Adaptive Optics mode to show that the reconstructors are tomographic. The
results are comparable and this small deficit is attributed to limitations in
the training data used to build the ANN. Laboratory bench tests show that the
ANN can out perform L&A under certain conditions, e.g. if the higher layer of a
model two layer atmosphere was to change in altitude by ~300~m (equivalent to a
shift of approximately one tenth of a subaperture).
