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, that is, 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.

An approach to implementing variational Bayesian inference in biological systems is considered, under which the thermodynamic free energy of a system directly encodes its variational free energy. In the case of the brain, this assumption places constraints on the neuronal encoding of generative and recognition densities, in particular requiring a stochastic population code. The resulting relationship between thermodynamic and variational free energies is prefigured in mind–brain identity theses in philosophy and in the Gestalt hypothesis of psychophysical isomorphism.

This paper presents a model of consciousness that follows directly from the free- energy principle (FEP). We first rehearse the classical and quantum formulations of the FEP. In particular, we consider the “inner screen hypothesis” that follows from the quantum information theoretic version of the FEP. We then review applications of the FEP to the known sparse (nested and hierarchical) neuro-anatomy of the brain. We focus on the holographic structure of the brain, and how this structure supports (overt and covert) action.

This paper aims to assess whether the recently proposed "inner screen model" of consciousness that follows from the free-energy principle (FEP) can be regarded as a minimal unifying model (MUM) of consciousness, thereby providing a common foundational model for consciousness studies, and integrating approaches to consciousness based on the FEP. We first present the inner screen model, which follows from applying the quantum information theoretic version of the FEP to the known sparse (nested and hierarchical) neuroanatomy of the brain. We then review models of consciousness that are premised on the FEP. Specifically, we review Bayesian versions of the global workspace and attention schema theories, theories premised on world-models and self-models, and models formalizing the computational structure and properties of time-consciousness. We then discuss how extant FEP-theoretic models of consciousness can be situated with respect to the candidate MUM.

“Intrinsic motivation” refers to the capacity for intelligent systems to be motivated endogenously, i.e., by features of agential architecture itself rather than by learned associations between action and reward. This paper views active inference, empowerment, and other formal accounts of intrinsic motivation as variations on the theme of constrained maximum entropy inference, providing a general perspective on intrinsic motivation complementary to existing frameworks. The connection between free energy and empowerment noted in previous literature is further explored, and it is argued that the maximum-occupancy approach in practice incorporates an implicit model-evidence constraint.

Active inference offers a unified theory of perception, learning, and decision-making at computational and neural levels of description. In this article, we address the worry that active inference may be in tension with folk psychology because it does not include terms for desires (or other conative constructs) at the mathematical level of description. To resolve this concern, we first provide a brief review of the historical progression from predictive coding to active inference, enabling us to distinguish between active inference formulations of motor control (which need not have desires under folk psychology) and active inference formulations of decision processes (which do have desires within folk psychology). We then show that, despite a superficial tension when viewed at the mathematical level, the active inference formalism contains terms that are readily identifiable as encoding both the objects of desire and the strength of desire at the psychological level. We demonstrate this with simple simulations of an active inference agent motivated to leave a dark room for different reasons. Despite their consistency, we further show how active inference may increase the granularity of folk-psychological descriptions by highlighting distinctions between drives to seek information vs. reward – and how it may also offer more precise, quantitative folk-psychological predictions. Finally, we consider how the implicitly conative components of active inference may have partial analogues (i.e., “as if” desires) in other systems describable by the broader free energy principle to which it conforms.