  Despite recent breakthroughs in the field of artificial intelligence (AI) -
or more specifically machine learning (ML) algorithms for object recognition
and natural language processing - it seems to be the majority view that current
AI approaches are still no real match for natural intelligence (NI). More
importantly, philosophers have collected a long catalogue of features which
imply that NI works differently from current AI not only in a gradual sense,
but in a more substantial way: NI is closely related to consciousness,
intentionality and experiential features like qualia (the subjective contents
of mental states) and allows for understanding (e.g., taking insight into
causal relationships instead of 'blindly' relying on correlations), as well as
aesthetical and ethical judgement beyond what we can put into (explicit or
data-induced implicit) rules to program machines with. Additionally,
Psychologists find NI to range from unconscious psychological processes to
focused information processing, and from embodied and implicit cognition to
'true' agency and creativity. NI thus seems to transcend any neurobiological
functionalism by operating on 'bits of meaning' instead of information in the
sense of data, quite unlike both the 'good old fashioned', symbolic AI of the
past, as well as the current wave of deep neural network based, 'sub-symbolic'
AI, which both share the idea of thinking as (only) information processing. In
the following I propose an alternative view of NI as information processing
plus 'bundle pushing', discuss an example which illustrates how bundle pushing
can cut information processing short, and suggest first ideas for scientific
experiments in neuro-biology and information theory as further investigations.
