Artificial intelligence has become a technological buzzword, often solely referred to AI rather than depicting the possibly infinite amount of practical applications that artificial intelligence can actually provide, or the intricacies involved from industry to industry, and region to region. To discuss some of the many applications for artificial intelligence, as well as some of the considerations to be taken into account to create more accurate and less biased machine learning systems, I had the pleasure of speaking with Nitendra Rajput, VP and Head of Mastercard's AI Garage. Nitendra Rajput is the Vice President and Head of Mastercard's AI Garage, setting up the centre to enable it to solve problems across various business verticals globally with machine learning processes, increasing efficiencies across the business as well as mitigating instances of fraud. Nitendra has over 20 years experience working in the fields artificial intelligence, machine learning, and mobile interactions, after realising a gap in the market for developing speech recognition systems for vocally-led countries, such as India. Prior to Mastercard's AI Garage, he spent 18 years at IBM Research, working on different aspects of machine learning, human-computer interaction, software engineering and mobile sensing.

Compared to a physical factory, software production is cheap to change over and doesn't require capital investment to be ripped out and replaced. And turning developers into high velocity code assembly lines wastes a huge opportunity to cultivate highly trained, creative, innovative people. An alternative is to approach AI development similar to the way my dad approached woodworking in his garage. A developer is not an executor of code but a creator. Tools exist to affect the creator's vision, and the vision adapts based on the productive experience.

The NITI Aayog has published an ambitious discussion paper on kickstarting the artificial intelligence (AI) ecosystem in India. AI is the use of computers to mimic human cognitive processes for decision-making. It highlights the potential for India to become an AI'garage', or solutions provider, for 40% of the world. To pull this off, India would have to develop AI tools for a range of applications: reading cancer pathology reports, rerouting traffic in smart cities, telling farmers where to store their produce, and picking students at high risk of dropping out from school, among them. It is a tall order, but several countries have similar ambitions.