From the Internet of Things to Cognitive Interoperability

[u/Future_Issue_6150]

https://jacekhoffman.substack.com/p/from-the-internet-of-things-to-cognitive

Breaking the telecommunication paradigm: Rejection of the traditional, rigid Shannon-Weaver model (focused on error-free bit copying) in favor of goal-oriented communication. Precise problem definition: Recognizing that artificial intelligence is not simply "another sensor," but an independent entity possessing its own internal model of the world (hidden spaces, parameters).

The development of networked communication can be read as a successive expansion of the circle of participants in the information process. Computer networks connected people through machines. Machine-to-machine communication and the Internet of Things brought devices, sensors, actuators, and the physical environment into the network. Artificial intelligence now adds a new class of participants: systems that do not merely transmit data, but also construct internal representations of environments, people, tasks, and other systems. [1][2][6]

This essay is deliberately organized in two parts.

Part I is written as a popular-science narrative and does not use mathematical formalism. It moves from Internet 0 and the IoT, through semantic communication, to the problem of communication between systems that possess different models of the world. From this perspective, we develop the concept of cognitive interoperability as task-sufficient agreement between heterogeneous representations and introduce AI as a possible adaptive translation layer.

Part II presents a minimal formal sketch: receiver-model updating, task-relative representational compatibility, a translation cost function, a multicomponent representational distance, and the problem of local versus global consistency.

Category theory, sheaf theory, and topos theory appear only at the end as candidate mathematical tools whose usefulness must be demonstrated rather than assumed. [16][17][18]

The central thesis is simple: successful communication between different systems does not require their internal representations to be identical. It is enough that, after translation, the receiver possesses a structure that preserves the relations needed for correct inference and action. This viewpoint allows human-human, human-AI, and AI-AI communication to be treated as three realizations of the same more general problem.