Sharing of personal data with organisations can in return give something of value as opposed to the popular notion of intrusion of privacy. Dr. Anindya Ghose, Heinz Riehl Chair Professor of Business, Professor of Technology, Operations, and Statistics, New York University (NYU) Stern School spoke to BE’s Aritra Mitra on the Artificial Intelligence (AI) ecosystem.
Q. Can you please elaborate on the concepts of ‘stalking’ and ‘butlering’ with reference to the apps mining data?
A. For years, we have been told only about the pitfalls and downsides of sharing our personal data with firms, how all firms are apparently incentivised to violate our data privacy, how all brands act as stalkers that are only out to abuse our data and so on. However, this is extreme rhetoric. Drawing AI-enabled examples from my best-selling and award-winning book, “TAP: Unlocking the Mobile Economy”, I have argued that sharing our personal data with firms can give us more control over our lives and present several upsides when firms act as our butlers and concierges who are trying to remove frictions from our lives. My intention is to make the average person recognise all the benefits when they share their data with organisations. This AI-driven ecosystem is already emerging and I want people to get more comfortable with the data-driven world we are getting into and embrace the inevitable future that they are about to walk into. People need to know that increasing digitalisation of interactions between consumers and firms is an inevitable fact and urge them to start considering their digital data as an asset that can be exchanged with firms in return for something of value such as convenience and economic benefits.
Q. Why is China ahead of others in the use of AI?
A. The AI battles are now being fought between the US and China. While the US is the world’s leader in AI discoveries, China is the leader in AI implementation. AI is usually more improved by more data rather than through better AI research. What matters in AI implementation is speed, execution, product quality, data, and government support. Chinese companies are equal to or ahead of their American counterparts in most of the AI areas. China’s data edge is three times the US based on mobile user ratio, 10 times the US in food delivery, 50 times in mobile payment, and 300 times in shared bicycle rides. All this rich data makes Chinese companies’ AI work better.
Q. To what extent is India successfully using AI? What are the bottlenecks?
A. India is significantly behind other big nations when it comes to vision, execution, and expenditure in AI. The good news is that the NITI Aayog has been mandated by the Finance Ministry to establish the National Programme on AI, with a view to guide research and development in this space. NITI Aayog is undertaking exploratory AI projects in various areas, creating a country wide strategy for building an AI ecosystem in India and collaborating with experts. My understanding is that they are also partnering with leading tech players to implement AI projects in areas like agriculture and health. The finance department has approved the expenditure of Rs. 7,000 crore ($1 billion) for NITI Aayog’s AI programme. This one billion dollars AI budget announced by the Indian government has to be used till 2024-25. This implies that the annual spend on the country’s AI programme is quite small compared to the US and China. The US is spending about $1 billion per year in non-defense AI. China wants to become the global leader in AI by 2030, setting aside a budget of $5 billion.
Q. Do you think sufficient emphasis is given on AI in Indian universities? What are the bottlenecks?
A. I don’t think, in India, we have fully internalised the potential of AI and the impact that it can have on various aspects of business and society, including the number of jobs and the types of jobs. We need to revamp our educational curriculum in universities and teach new skills given that according to the World Bank, 69% of jobs that exist today in India are under a credible threat of being automated in the next few years.