  For deep learning, size is power. Massive neural nets trained on broad data
for a spectrum of tasks are at the forefront of artificial intelligence. These
large pre-trained models or Jacks of All Trades (JATs), when fine-tuned for
downstream tasks, are gaining importance in driving deep learning advancements.
However, environments with tight resource constraints, changing objectives and
intentions, or varied task requirements, could limit the real-world utility of
a singular JAT. Hence, in tandem with current trends towards building
increasingly large JATs, this paper conducts an initial exploration into
concepts underlying the creation of a diverse set of compact machine learning
model sets. Composed of many smaller and specialized models, the Set of Sets is
formulated to simultaneously fulfil many task settings and environmental
conditions. A means to arrive at such a set tractably in one pass of a
neuroevolutionary multitasking algorithm is presented for the first time,
bringing us closer to models that are collectively Masters of All Trades.
