India’s mess of complexity is just what AI needs
In 2010, I hired two engineers from an Indian college to help me develop a product that could automatically grade the spoken English ability of job applicants. About a year later, they knocked on my office door with concern etched on their brows. “We are doing machine learning here, but all our friends are doing software engineering,” they explained. “Do we have a future?”
Things have changed dramatically. In India today, every engineer claims some type of machine-learning project. Most businesses have a top-down mandate to incorporate AI into their processes and products. The excitement has reached all the way to the government: in this year’s speech on the federal budget, Indian finance minister Arun Jaitley announced that the country will launch a national program to promote AI research and development.
This newly awakened interest in AI borders on euphoria, but little of it is realistic. India has a relatively small body of researchers and research output in the field of machine learning. From 2015 to 2017, the contribution of Indian researchers to top AI conferences constituted one-15th of the US contribution and one-eighth of China’s. At the most recent conference of the Association for the Advancement of Artificial Intelligence, Indian researchers presented 20 papers, compared with 307 presented by our US colleagues and 235 from China. Most Indian research institutions have, at best, a rudimentary AI research program. India contributes little new knowledge in machine learning and lacks local expertise in the knowledge that is being created every day by others.
All this could be catastrophic for India. The country needs to develop and commercialize AI if its businesses are to be globally competitive. AI could also help address the country’s social problems, particularly corruption and lack of infrastructure.
Likewise, the AI revolution needs India. The country’s diversity of languages, dialects, accents, scripts, dress, and culture presents a rich set of challenging problems for artificial intelligence. Current AI techniques are limited in their ability to handle complexity, and they’ll have to mature to deal with the diversity of life in India. The needs of India’s population also pose interesting challenges for AI. For instance, where researchers in the US hope AI can make doctors more efficient, in India the question is how AI can do the job of a doctor in rural areas that currently have no medical care at all. Investment in India can help move the whole field ahead.
India’s global business activities have typically revolved around IT services and business process outsourcing. These businesses depend on India’s demographic dividend: the large population of English speakers trained in basic numbers, computers, and programming. This skilled workforce, along with India’s low costs, has been powering growth in these service sectors for the last three decades.
The business process outsourcing industry is composed mostly of tasks that boil down to transcribing speech, digitizing handwritten forms, and tagging images—which can now be done very accurately by machines. Machine learning is also good at tasks that require some basic analytical skills, such as classifying documents, scoring them, and deriving structured data from them. Bots can now handle simple chat and e-mail requests while directing more complex ones to human operators. Even then, machine learning helps generate possible responses that human operators can select or modify.
The situation isn’t quite so dire for the IT industry, which still requires people to write programs. But even there, automation is playing a role in services beyond hard-core programming, such as network monitoring, testing, and infrastructure maintenance. The big opportunity for the Indian IT industry is to provide data science services to the world. IT companies have started to build AI practices, but the country lacks trained talent.
India wants to revive its manufacturing through a much publicized “Make in India” initiative, but there is little interest in using automation toward that end—in contrast to China, which has made robotization a priority.
There is also little capability. The first crucial step in improving efficiency through robotics and AI is identifying a business problem and converting it to a machine-learning problem, but few Indian companies have risen to that challenge. Despite their mandate to employ machine learning, they do not know how to do it. Most data scientists in India falter on basic concepts required to make machine learning work.
In this second machine age, a massive population is not the competitive advantage it has been for India over the past 30 years. Indian service companies will find themselves competing with international companies that augment—or even replace—human workers with sophisticated algorithms.
India says it wants to make its people more prosperous. If that’s the case, it will have to adopt AI in a big way. How? The first thing it can do is try to attract a critical mass of AI experts: people with PhDs from world-class universities. I believe the government could help assemble a team of 500 AI researchers in India’s public institutions over the next five years by instituting an attractive AI fellowship program for faculty and PhD students. Done in parallel with private research initiatives, this could provide the catalyst that India needs.
That is just part of the improved technology ecosystem India must build to realize the potential of new tools for addressing its huge challenges in areas like health care, banking, sanitation, agriculture, and education. AI gives India the opportunity to leapfrog some of these issues, including the corruption plaguing all these areas, via cheap diagnostic methods, automatic processing of applications, or learning and teaching aids.
For example, one young entrepreneur from Jaipur recently showed me a system that can analyze images of certain grains to ascertain their quality and estimate the price they are likely to fetch at market. A system such as this can help level the playing field between farmers and wholesale buyers. Another example is an automated teaching assistant for programming skills, a project my team is currently working on.
Industry and the research community need to do a better job on each side of their symbiotic relationship, in which industry provides problems and data while the research community develops algorithms and solutions. Indian companies helped drive the country’s progress over the last three decades by creating the demand for basic programmers and supporting undergraduate programs in the universities and institutes. They need to shift gears—start building teams of PhDs, and help university PhD programs deliver good candidates. India’s future depends on it.
Varun Aggarwal is the cofounder of Aspiring Minds, a company that uses artificial intelligence to match talent with jobs. He is the author of a book on India’s need to reform its innovation ecosystem,Leading Science and Technology: India Next?
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