  This white paper lays out a vision of research and development in the field
of artificial intelligence for the next decade (and beyond). Its denouement is
a cyber-physical ecosystem of natural and synthetic sense-making, in which
humans are integral participants -- what we call ''shared intelligence''. This
vision is premised on active inference, a formulation of adaptive behavior that
can be read as a physics of intelligence, and which inherits from the physics
of self-organization. In this context, we understand intelligence as the
capacity to accumulate evidence for a generative model of one's sensed world --
also known as self-evidencing. Formally, this corresponds to maximizing
(Bayesian) model evidence, via belief updating over several scales: i.e.,
inference, learning, and model selection. Operationally, this self-evidencing
can be realized via (variational) message passing or belief propagation on a
factor graph. Crucially, active inference foregrounds an existential imperative
of intelligent systems; namely, curiosity or the resolution of uncertainty.
This same imperative underwrites belief sharing in ensembles of agents, in
which certain aspects (i.e., factors) of each agent's generative world model
provide a common ground or frame of reference. Active inference plays a
foundational role in this ecology of belief sharing -- leading to a formal
account of collective intelligence that rests on shared narratives and goals.
We also consider the kinds of communication protocols that must be developed to
enable such an ecosystem of intelligences and motivate the development of a
shared hyper-spatial modeling language and transaction protocol, as a first --
and key -- step towards such an ecology.
