  Extensive research on formal verification of machine learning (ML) systems
indicates that learning from data alone often fails to capture underlying
background knowledge. A variety of verifiers have been developed to ensure that
a machine-learnt model satisfies correctness and safety properties, however,
these verifiers typically assume a trained network with fixed weights.
ML-enabled autonomous systems are required to not only detect incorrect
predictions, but should also possess the ability to self-correct, continuously
improving and adapting. A promising approach for creating ML models that
inherently satisfy constraints is to encode background knowledge as logical
constraints that guide the learning process via so-called differentiable
logics. In this research preview, we compare and evaluate various logics from
the literature in weakly-supervised contexts, presenting our findings and
highlighting open problems for future work. Our experimental results are
broadly consistent with results reported previously in literature; however,
learning with differentiable logics introduces a new hyperparameter that is
difficult to tune and has significant influence on the effectiveness of the
logics.
