26 Jul 2021 20:13 IST

Competing in the global market with AI skills

India is an attractive destination for hiring as it offers high-quality resources at a fraction of the cost

Artificial intelligence can be loosely defined as a discipline or a method where a computing agent can learn on its own and perform a task without the requirement of explicit programming. An AI agent is hence also known as a ‘smart system.’ In essence, AI is viewed as a branch of computer science and engineering that deals in developing these smart systems that can perform tasks requiring human intelligence; they mimic human intelligence under specified conditions.

AI is also the superset of knowledge discovery from data (KDD), statistical machine learning, data mining, and deep learning. This interdisciplinary science comes with numerous advancements and approaches, that are possible with the help of Deep Learning, Machine Learning algorithms, Neural Networking, and Natural Language Processing (NLP).

Consumer vs producer of AI tech

When it comes to geographical market opportunities, there are key market differentiators in terms of ‘producing AI technologies’ and ‘consuming AI technologies.’ Producing geographies rely on advanced research, whereas consuming geographies are dependent on implementation and support professionals. Consuming geographies are diverse and we can safely conclude every country/geographies consume AI technologies.

Developmental/production geographies on the other hand are often fragmented based on the domain areas such as defense and military (US, China, Israel, Japan), banking and finance (US, UK, Australia), shipping and logistics (US, China, Scandinavian countries, Germany, Japan, Australia), retail FMCG (US, France, Germany), Healthcare (US, Israel, Japan, China), Space and Autonomous Vehicles (US, Russia, China, India, Israel, France, Japan), agriculture and environment (US, China, France, Israel, Australia).

Innovator vs technician

While there are visible opportunities for skills in AI across the globe, with salaries ranging between $60,000 to $300,000 depending upon the research background and skills, the market is primarily divided into two — technicians and innovators.

To take the example of a car, the driver-mechanic can be defined as a technician, whereas the engineer who has designed the vehicle is the innovator. In the AI world, the innovator develops an IP (intellectual property) such as an algorithm that can solve a few business cases, while the technician builds a model around that algorithm. There is, therefore, a substantial difference in background education, research experience, and skills between an innovator (who would probably work at the top end of the compensation spectrum) and a technician (typically at the lower end of the compensation spectrum).

An innovator would typically come with a high university education, likely a PhD whereas a technician would usually have some training and education in the usage of technology, likely with an undergraduate degree.

Where India stands

The space for AI innovators is increasing and university and research labs are increasing their investments in research related to AI, led by Stanford University, Carnegie Melon University (often called the birthplace of machine learning), Tsinghua University and others where some of the finest research scientists are produced. India is a very attractive destination for companies hiring for users/technicians of AI technologies because it offers a very high-quality resource at a fraction (often 1/5th of that in the US) of the cost.

Interestingly, restrictions on work permits imposed by countries, language barriers, experienced in China, Japan, mainland Europe, Russia and elsewhere, are factors leading to distortion in market economies, where the best talent may not be getting the best pay. While it is likely that the US is going to stay established in their market leadership in this space, China is a rising power, even though Chinese companies have more than 50 per cent of their AI staff who are Americans and an overwhelming majority of which is trained in the US.

(The writer is Director, Data Science Programmes, S P Jain School of Global Management.)