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In evolutionary terms, the form and function of individuals serves the purpose of supporting their survival and reproduction. But all individuals are made of parts that used to be individuals themselves, e.g. the transition from unicellular life to multicellular organisms. Before a transition, components act to serve their individual purposes of survival and reproduction. But after a transition, they act with a unity of purpose at a higher level of organisation (e.g. to serve the development, survival and reproduction of the multicellular organism, even when it conflicts with their own). How does evolution achieve this rescaling of its own purpose? To answer this question, we will build individual-based computational simulations of evolution by natural selection to test specific new hypotheses about the conditions that result in such evolutionary transitions. In particular, we will explore the hypothesis that the conditions that enable evolution to exhibit a transition in individuality are predicted by the conditions that enable learning systems to induce and exploit deep models, a.k.a. deep learning. Elucidating these conditions is vitally important to our fundamental understanding how evolution, development and organismic behaviour interact to rescale the evolutionary process at higher levels of organisation. Outputs include a working model that demonstrates an evolutionary transition (in silico) for the first time, and rigorous characterisation of the necessary and sufficient conditions for this to occur under natural selection. This has potential to impact 1) fundamental theory in evolutionary, developmental and organismic biology, expanding our understanding of how they interact to create evolutionary purpose, 2) new approaches to manipulating developmental goals and organismic function (e.g. for bioengineering), and 3) big questions about how self-interested individuals (including us) work-together to create something bigger than themselves.