  Machine Intelligence (MI) technologies have revolutionized the design and
applications of computational intelligence systems, by introducing remarkable
scientific and technological enhancements across domains. MI can improve
Internet of Things (IoT) in several ways, such as optimizing the management of
large volumes of data or improving automation and transmission in large-scale
IoT deployments. When considering MI in the IoT context, MI services deployment
must account for the latency demands and network bandwidth requirements. To
this extent, moving the intelligence towards the IoT end-device aims to address
such requirements and introduces the notion of Distributed MI (D-MI) also in
the IoT context. However, current D-MI deployments are limited by the lack of
MI interoperability. Currently, the intelligence is tightly bound to the
application that exploits it, limiting the provisioning of that specific
intelligence service to additional applications. The objective of this article
is to propose a novel approach to cope with such constraints. It focuses on
decoupling the intelligence from the application by revising the traditional
device's stack and introducing an intelligence layer that provides services to
the overlying application layer. This paradigm aims to provide final users with
more control and accessibility of intelligence services by boosting providers'
incentives to develop solutions that could theoretically reach any device.
Based on the definition of this emerging paradigm, we explore several aspects
related to the intelligence distribution and its impact in the whole MI
ecosystem.
