Today organizations have to deal with so many emergent
behaviors that the notion of central control as the only coping mechanism seems
to be receding as a dominant management model.
Freedom must be doled out further from the centrist idea by creating
goals, constraints, boundaries, and allowable edge behaviors. Someday, software
and hardware agents will negotiate their contribution to business outcomes on
their own, but until then, organizations will have to prepare themselves by
managing coordinated autonomy.
Learning About "The potential" at the Edge
Edge
computing is a form of distributed computing that brings computation and data
storage closer to the location where it is needed, to improve response times
and provide better actions. Now, AI on Edge can
offer a whole lot of new possibilities. In Edge AI, the AI and
other algorithms are processed locally on a hardware device or a distributed
software agent. It uses data that is generated from the device/agent and
processes it to give real-time insights in less than a few milliseconds and
allows for pattern recognition, fast decisions, and better actions to deal with
emergent conditions. We have seen practical applications in smart buildings,
smart cities, and intelligent industry 4.0 supply chains. While most of the
visible examples are in and around physical infrastructure, AI at the edge is
starting to work at the customer, partner, and employee edge interfaces as well.
This is leading to more utilization of software bots, assistants, and agents.
Trusting AI &
Algorithms to Increase Freedom Levels
Today machines and software are programmed with rules that
are preplanned and inflexible for changing conditions. Somebody needs to
program those rules, decisions, and actions ahead of time. Low code or no code
shortens the time to change for emergent conditions. Another great approach is
to learn for the emergence and adapt the rules, decisions, and actions
inflight. This requires a different trust level than in the past, particularly
with unsupervised learning. By giving hardware and software goals and constraints,
these freedom levels can broaden to deal with faster emergence. Organizations
will have to learn new freedom and trust levels to take advantage of the speeds
necessary to compete.
Enabling Digital
Twins for Manage Adaptability
Every physical device and software agent will have a digital
form of an interactive model that represents its logical self (twin). These
models can be watched in interaction with other digital twins to observe,
manage, and change their behavior. Mining the behavior of the digital twin will
create an observable behavior that can be overlaid on timelines or other forms
of observation. The digital twin is a practical way to understand if a hardware
or software agent is behaving well or badly so that managers can take appropriate
actions.
Net; Net:
Organizations will have to deal with emergent behavior whose
footprints will likely be in big, fast, and dark data, events, or content. This
will require new competencies, skills, and coordinated sets of digital
technologies. Organizations that embrace and take advantage of emergence will
likely leverage AI at the edge and manage it well for their competitive
differentiation.
KEEL Technology for "small" autonomous 100% explainable and auditable behavior. Adaptive operational control with embedded expertise. IoAT (The Internet of Autonomous Things).
ReplyDeleteThis comment has been removed by a blog administrator.
ReplyDelete