  The applicability of computational models to the biological world is an
active topic of debate. We argue that a useful path forward results from
abandoning hard boundaries between categories and adopting an
observer-dependent, pragmatic view. Such a view dissolves the contingent
dichotomies driven by human cognitive biases (e.g., tendency to oversimplify)
and prior technological limitations in favor of a more continuous, gradualist
view necessitated by the study of evolution, developmental biology, and
intelligent machines. Efforts to re-shape living systems for biomedical or
bioengineering purposes require prediction and control of their function at
multiple scales. This is challenging for many reasons, one of which is that
living systems perform multiple functions in the same place at the same time.
We refer to this as "polycomputing" - the ability of the same substrate to
simultaneously compute different things. This ability is an important way in
which living things are a kind of computer, but not the familiar, linear,
deterministic kind; rather, living things are computers in the broad sense of
computational materials as reported in the rapidly-growing physical computing
literature. We argue that an observer-centered framework for the computations
performed by evolved and designed systems will improve the understanding of
meso-scale events, as it has already done at quantum and relativistic scales.
Here, we review examples of biological and technological polycomputing, and
develop the idea that overloading of different functions on the same hardware
is an important design principle that helps understand and build both evolved
and designed systems. Learning to hack existing polycomputing substrates, as
well as evolve and design new ones, will have massive impacts on regenerative
medicine, robotics, and computer engineering.
