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The evolution of AI has been an interesting exploration in discovery. AI generally takes on many areas of human capabilities including: knowledge representation and articulation, learning and adaption, deliberate planning and acting, speech and language processing, image understanding, manipulation and locomotion, robotics, multi-agent systems, cognitive modeling and mathematical functions (Dean, 1997).

The practical applications of AI continue to expand every year. Fueled by the incremental availability of digital data in all fields of endeavor, models of process and processing have caused major shifts in linguistics, psychology, philosophy and organizational theory. Chatbots and vocally activated technologies have found their way to help desks, consumer service websites and service centers. These technologies are having immediate commercial impact. The new systems learn quickly from experiences and adapt to changes in the new environment. To interact with human collaborators these systems must be able to engage in dialogue and model rationality. Robots, chatbots, and vocally activated smartbots are all examples of the continuing evolution of integrating computer interactions to support human life.  We are recognizing the power of the Internet of Things (IOT) of connecting AI driven processes to physical machines to replace humans in the “turning of a knob or pushing a button”.  The extension of this will be the network of AI processes and objects interacting, refining, and learning our human habits and needs and anticipating and acting on them.

All five major areas of AI that currently exist, can be expected to grow. The categories include: deep learning, robots, dematerialization, the Gig Economy and autonomous driving. Each will be disruptive and each will accompany the next industrial revolution of the Internet of Things (IOT). Deep learning refers to a series of connected machines and algorithms modeling and extracting data. Connected machines, learning from each other and designed to outperform human experts. Robots can range in design and size and shape but basically they are designed to replace human workers by doing a task or tasks. Dematerialization replaces back office activities by recording and processing data. It is the idea of replacing traditional services, like getting tickets to a show with technologies. The Gig Economy is about more self-employment and more independent contractors. Autonomous driving is taxis, cars and trucks that, with the help of intelligent sensors, drive themselves (Gerlind Wisskirchen, 2017).

The more AI situates artificial agents and people in a common environment the more opportunities there are for collaboration, sharing knowledge, and discovering new knowledge. Rationality is another way that AI supports intelligent processes and economic problem solving.  This application has been extended to performance and learning and adaption, especially in the areas of learning process, preference and utility. With collaborative systems on the horizon and approaching quickly, areas of research including planning, multi-agent learning, language, speech, understanding and communications interact to form collaborative agents (Growth Stage Podcast, 2018).