Highlighting how AI can be effectively used in execution of government schemes, the ASSOCHAM-PwC report explains how deep learning can be employed to tackle issues of scale.
Instead of waiting for the artificial intelligence technology to reach a level where regulatory intervention becomes necessary, India should take lead in establishing a legal infrastructure on AI application to become a frontrunner, according to a joint study conducted by ASSOCHAM and PwC.
The jointly conducted study by ASSOCHAM-PwC highlighted a range of application for AI techniques in large-scale public endeavours, including Make in India, Skill India and others, such crop insurance schemes, tax fraud detection, and detecting subsidy leakage and defence and security strategy.
“If investments are made in the two initiatives without due cognisance of how Industry 4.0 (the next industrial revolution driven by robotic automation) may evolve with respect to demand for workforce size and skill sets, there is a possibility of ending up with capital-intensive infrastructures and assets that fall short of being optimised for automated operations and a large workforce skilled in areas growing beyond the need for manual intervention only,” noted the study.
The report stated that Make in India initiative which focuses on twin goals of strengthening country’s in-house innovation and production capabilities with added creation of employment opportunities may not end up creating nearly as many jobs as it is poised to at this point in time.
Information technology (IT), manufacturing, agriculture and forestry are certain sectors that are expected to experience shrinkage of employment demand as robotic systems and machine learning algorithms take up several tasks, the report said.
Highlighting how AI can be effectively used in execution of government schemes, the report said that deep learning can be employed to tackle issues of scale often prevalent in such schemes.
“It is essentially a process that can be used for pattern recognition, image analysis and natural language processing (NLP) by modelling high-level abstractions in data which can then be compared with various other recognised contents in a conceptual way rather than using just a rule-based method,” it said.
The study further said that in comparison to the West and frontrunners of AI adoption in Asia, such as China and Korea, the culture and infrastructure needed to develop a base for the adoption of AI in mainstream applications in India is in need of an impetus.
Indian academics, researchers and entrepreneurs face a more acute challenge than corporates do in terms of the less than ideal infrastructure available for an AI revolution in India.
As such, it is imperative in India to foster a culture of innovation and research beyond the organisation as is common in global technology giants. “To encourage the same level of innovation in AI research efforts in India, initiatives to hold events and build user communities in the field of AI will go a long way.”