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$\unicode{x2014}$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$\unicode{x2014}$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$\unicode{x2014}$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$\unicode{x2014}$and
key$\unicode{x2014}$step towards such an ecology.

该研究报告提出了未来十年（甚至更长时间）人工智能领域的视野，通过主动推理实现智能系统的自我感知和共享感知，从而带来了共享智能和集体智能的新生态，该生态需要建立通信协议和共享空间建模语言。