Machine Learning Square and Software Diagnostics Institute Roadmap

When researching ML from the point of view of sociology and humanities*, we came upon an idea of Machine Learning Machine Learning … sequence. Then we realised that there is a distinction between Machine-Machine Learning (Machine Learning of Machine structure and behaviour) and Machine-Human Learning (Machine Learning of Human state and behaviour, ML approaches to medical diagnostics). This naturally extends to a learning square where we add Human-Machine Learning (Human Learning of machine diagnostics) and Human-Human learning (Human Learning of Human state and behaviour, medicine, humanities, and sociology):

What we were mainly doing before 2018 is devising a set of Human-Machine Learning pattern languages. Recently we moved towards ML approaches, and this activity occupies Machine-Machine Learning quadrant. Since analysis patterns developed for Human-Machine Learning are sufficiently rich to be used in other domains than software results can be applied to Human-Human Learning (for example, narratology), and together with additional results from Machine-Machine Learning can be applied to Machine-Human Learning (for example, space-like narratology for image analysis):

* Machine Learners: Archaeology of a Data Practice, by Adrian Mackenzie (ISBN: 978-0262036825)