FST Graduate Research Topic

Research Topic: Higher Education and the Fourth Industrial Revolution
Suggested By: Vive Kumar
Last Update: January 13th, 2022

With the ability of AI algorithms to learn information, knowledge and insights exclusively from raw data without specific instructions about information, knowledge and insights is evidence that the inflection point has been reached as cognification evolves. The ability to mimic human intelligence, vision, hearing, physical movements, managing resources, diagnosis, nursing, emotion and social interactions impacts every possible domain, except for domains where data does not matter. Students, across these domains, will learn and use such AI algorithms as core skills. As computers mimic and, when needed, exceed human capabilities, jobs traditionally held by humans will be lost, from programmer to truck driver to lawyer to financial analyst to non-specialist physicians to soldiers to factory workers to non-specialist teachers. During these near-inflection years, students will be forced to learn new cognification skills that supplement traditional domain-specific job skills. Near-inflection will also bring in ‘gig jobs’ that are transient in nature and can only be stop-gap. During the later post-inflection years, students will exclusively study cognification skills. Post-inflection years will see massive retraining to help students attain systems thinking, entrepreneurship, cultural agility and critical thinking. AI algorithms and associated technologies will force students to addressing global issues such as poverty, human existence, environmental engineering, genetic editing, hunger, disease, compassion, resource sharing, weather engineer and global planning. Topics such as computing, physics, mathematics will mesh into courses that target global issues because of the changes in career models necessitated by the disappearance of traditional jobs associated with traditional career models. students will no longer code for a computer application; they simply design solutions for the problem, select appropriate pieces of preexisting code, mash them together, inject cognification algorithms, point to the variety of data that already exist and data that can be supplied in the future, and move on to the next application.