Whereas traditional AI focused on modelling the cognition of isolated individuals, Social AI is a relatively new discipline that seeks to model the way in which in groups of agents or actors perceive, analyze, and respond to events in their collective environment. Each agent or actor has its own local perspective on that environment. Furthermore, there is, typically, no global perspective that organizes all of the local perspectives into a single coherent whole. As such, Social AI divides into a collection of sub-disciplines.
First, there is the distributed translation of event streams into a distributed model of objects laid out in a collection of interlinked space-time frameworks. What is critical to Social AI is that there is no need for there to be a single space-time framework within which all objects are placed. It is only necessary that whenever a subset of agents or actors are in communication, it is possible to construct or deconstruct a chain of links between the frameworks relevant to the communicating subgroup.
Second, there is the analysis of the changes that take place in the spatio-temporal arrangements of these objects into a network of causal paths. Once again, there is no guarantee that a single causal network will coherently unite all changes. It is only necessary that, from the perspective of any given agent or actor, that a causal analysis can be performed that reaches all objects appearing within that agent’s local framework and the frameworks of the agents communicating with it.
Third, with the local causal paths in place, Social AI explores how distributed agents or actors can ensure that the causally analyzed objects satisfy a pre-established set of constraints while only communicating locally and there being no guarantee that any fact about the adherence to constraints or lack thereof is globally communicated across the entire agent/actor population.
Fourth, if constraint satisfaction is concerned with whether or not objects satisfy a given list of properties, optimization is about what properties a set of objects must have in order that a particular goal be satisfied and just as there is a Social AI of constraint satisfaction, there is a Social AI of distributed optimization.
Fifth, and finally, Social AI recognizes that goals themselves may be subject of decision and agents and actors can compete in order to establish which goals a distributed system may seek to obtain.
Now, as long as the IT stacks relevant to delivery of applications and services resided within the logical confines of single enterprise, it was possible, at least in theory, to assume that one could construct infrastructure and application management (IAM) systems that had a global view of the infrastructure, services and applications being managed.
Over the last five years, however, applications and services have increasingly been stretched across trans-enterprise continuously evolving infrastructures and the assumption that a global view can be obtained is less and less plausible. For this reason, I believe that the traditional global perspective presupposing AI engines underlying many of today’s popular IAM products are rapidly becoming obsolete and will need to be replaced by Social AI equivalents.
This is not just a change in technology, however. It is also a change in IAM philosophy. Truly distributed systems will require a truly distributed management. There will be no master view, no single source of truth. Instead, there will be a series of shifting, opportunistic perspectives that will evolve as rapidly as the systems targeted for management.