Antagonising explanation and revealing bias directly through sequencing
  and multimodal inference
Luís Arandas Mick Grierson Miguel Carvalhais
Deep generative models produce data according to a learned representation,
e.g. diffusion models, through a process of approximation computing possible
samples. Approximation can be understood as reconstruction and the large
datasets used to train models as sets of records in which we represent the
physical world with some data structure (photographs, audio recordings,
manuscripts). During the process of reconstruction, e.g., image frames develop
each timestep towards a textual input description. While moving forward in
time, frame sets are shaped according to learned bias and their production, we
argue here, can be considered as going back in time; not by inspiration on the
backward diffusion process but acknowledging culture is specifically marked in
the records. Futures of generative modelling, namely in film and audiovisual
arts, can benefit by dealing with diffusion systems as a process to compute the
future by inevitably being tied to the past, if acknowledging the records as to
capture fields of view at a specific time, and to correlate with our own finite
memory ideals. Models generating new data distributions can target video
production as signal processors and by developing sequences through timelines
we ourselves also go back to decade-old algorithmic and multi-track
methodologies revealing the actual predictive failure of contemporary
approaches to synthesis in moving image, both as relevant to composition and
not explanatory.