  The yielding of dry foams is enabled by small elementary yield events on the
bubble scale, "T1"s. We study the large scale detection of these in an
expanding 2D flow geometry using artificial intelligence (AI) and nearest
neighbour analysis. A good level of accuracy is reached by the AI approach
using only a single frame, with the maximum score for vertex centered images
highlighting the important role the vertices play in the local yielding of
foams. We study the predictability of T1s ahead of time and show that this is
possible on a timescale related to the waiting time statistics of T1s in local
neighborhoods. The local T1 event predictability development is asymmetric in
time, and measures the variation of the local property to yielding and
similarly the existence of a relaxation timescale post local yielding.
