The importance of Artificial Intelligence (AI) and Machine Learning (ML) is exponentially increasing as a number of organisations are using these technologies to improve their products & services, evaluate their business models, and enhance their decision-making process. Machine Learning is referred as one of eight new AI-based technology in “Gartner’s Hype Cycle- 2019” with predictions to increase popularity in 2-5 years.
“Google Translate” is probably the most complicated example of Machine Learning. Today, all the major market players – such as Google, Apple, Microsoft, Facebook etc. are already leveraging Machine Learning in many interesting ways. Machines are maturing to meet the human intelligence. It is expected that by 2025, machines will start performing more current work tasks than humans, compared to 71% being performed by humans as of now. This transformation due to ML applications has started impacting the global workforce in almost every sector/ industry – from education to IT, healthcare to retail and finance etc.
The Industries/ Businesses have already started preventive actions to support their existing workforces through re-skilling and up-skilling. Individuals are also taking interest in a pro-active approach to their own lifelong learning. To achieve the required results, the government is also taking interest to create an enabling environment to facilitate this workforce transformation.
The education sector plays a dual role in this transformation journey – on one side, the skilled faculty with effective teaching & well-designed course curriculum produces the workforce with required skills to support the changing business needs. On the other side, it adopts Machine Learning centric data-science approach as an effective tool for administrators and faculties to be a game changer for higher education.
A range of innovative educational institutions often adopt disruptive technologies in novel ways and are therefore in a good position to use Machine Learning to improve higher education which ultimately results in producing skilled workforce for the future needs. Apart from faculty & students involved in lecturing and taking notes, there exists numerous digital resources that are used to make the lessons more interactive and interesting. Some such examples are given below:
- Personalize the learning process of specially-abled students according to his/her capabilities with assistance from a personal robot.
- Assess the student quickly in order to design his/her personalized learning plan.
- Predict student performance and prepare the performance improvement plan.
- Assist in augmented and virtual reality technology to help students to interact with various topics giving a highly reliable subject oriented experience etc.
So, it is important to upskill yourself on Machine Learning concept to deliver best in class if you are a faculty, teaching this as a subject. Or otherwise also, let Machine Learning Technologies (like: Google Socratic, Google Home, Microsoft Cortana, Apple Siri, Amazon Alexa, etc.) be a part of day-to-day learning process to effectively interact with the students to help them with their lessons, real-time transcription services and many more.