Should Faculty learn Machine Learning in the Digital Age?

Machine Learning (ML) and Artificial Intelligence (AI) in this current digital era is everywhere. We all use this Machine Learning (ML) systems in our day to day life such as language translation systems, chatbots, digital assistants, search engines, cybersecurity systems and many more. In future, ML will be used in diagnosing and treating severe illnesses and innumerable use-cases.

The leap of ML & AI in educational systems is going to bring a drastic change in the functional, research and learning processes. The whole education system will be relying on these artificial intelligence programs to teach, guide, monitor, assess and improve the performance of each and every student.

It is very vital that students are aware of all these developing technologies and their usage around them. The best way for the students to understand the capabilities and usage of ML & AI is by able to build themselves. Once the young minds are exposed to the ML & AI concepts and usage, they can come up with infinite ideas benefiting themselves and the whole community. This will not only improve their confidence and boost self-esteem but also will increase passion of learning new things.

In order to achieve this, the teachers and faculties in the educational institutions starting from schools to colleges and universities need to be exposed and trained with ML & AI programs. The staff in educational system who are involved in mathematical, analytical and computer science subjects can be easily trained with the ML & AI curriculum.

The academic curriculum evolves as the need in the industry changes. The industry is already embracing the demands of ML & AI and in no time the academics are expected to gear up and support those growing demands. The faculties who are at the forefront of supporting this transformation through academics is expected to upgrade themselves first.

This opportunity is not to be missed. Those who understand and on-board the ML & AI skills will have a competitive edge over those who wait and watch.

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