# Machine Learning Media Theory **Opening (not a real abstract):** In this paper, I outline a groundwork for a media theory of machine learning by introducing two new concepts, compute-computing and compute-computed, and a framework for their interaction. 1 Compute-computing (computing as generative) is here understood as the “active” learning component of a system, whereas compute-computed (computing as generated) is understood as the “passive”, coded, imprinted or inscribed aspect of a system. I introduce these two concepts to help us to think through the specificity of algorithmic systems that are more than just the operative, sequential or parallel systems of computational processing to which we have become accustomed. Indeed, in the case of machine learning systems, these systems have the capacity to be self-positing in the sense of generating models and data structures that **Authors:** [[David M. Berry]]