As big enterprises and consumers communicate, collaborate and conduct commerce almost at the speed of light using voice, data and video, information explosion (a term first used in 1941, according to the Oxford English Dictionary) has created a need for its accumulation, processing and integration to create “knowledge.” Knowledge processing, in turn, allows us to use the information to make strategic decisions and improve the efficiency of the processes involved. Therefore, knowledge processing systems, their theory and practice are receiving renewed focus. These systems include processes and activities such as cognition, knowledge production, learning, knowledge acquisition, reasoning, management and application. In this paper we discuss how knowledge processing can be viewed as manipulation of various knowledge structures and their transformation. We argue that efficient organization of knowledge processing has to be based on structure transformations of data represented in a symbolic form.

The International Summit is4si-2017, Digitalisation for a Sustainable Society: Embodied, Embedded, Networked, Empowered through Information, Computation & Cognition, was held 12–16 June 2017 at Chalmers University of Technology Conference Center in Gothenburg, Sweden.

As the scale of computations become large and as both people and machines demand communication, collaboration and commerce at the speed of light, rapid fluctuations in the demand for computing performance and fluctuations in available resource pools, both make it necessary to respond fast and readjust the computation structures and associated resources so as to not disrupt the user experience or the service transaction. Current Information Technologies from their memory-starved, server-centric, low-bandwidth origins from von Neumann’s stored program control implementation of the Turing Machine are evolving with new architectures to meet the demand for scale and speed. In this paper we discuss the evolution of current IT to cognitive IT, where computing processes become self-aware of their resource requirements in real-time and seek to adjust them from a global knowledge of available resource pools and their provisioning processes. This is transforming the current state of the IT as we know it to a cognitive IT.

In this paper we analyze our current understanding of genes, neurons and the neocortex and draw a parallel to current implementations of cognitive computing in Silicon. We argue that current information technologies have evolved from the original stored program control architecture implementing the Turing machines which allowed us to model, configure, monitor and control any physical system using a Universal Turing Machine model. However, large scale of distributed computations and their tolerance requirement to fluctuations have created new challenges. We suggest that a new agent based computing model extension to the current Turing machine meets this challenge and provides a path to cognitive self-learning and self-managing systems.