India is gradually going forward in establishing artificial intelligence in its various sectors.

The IT sector of India has always been a hub of great minds and it has played a vital role in the economic growth of the country. Around 2018,  the government think-tank, National Institution for Transforming India (NITI) Aayog emphasized the development of AI research in the IT sectors. This development came on the heels of the launch of a Task Force on Artificial Intelligence for India’s economic transformation by the Commerce and Industry Department of the Government of India in 2017. Since then, AI is gradually being adopted in every sector of India. Experts’ reviews show that healthcare and agriculture are so far among the most important sectors of focus to improve living conditions for India’s citizens.

The government lends a hand

With the government’s inclination towards initialization, more AI initiatives are taking place and a pool of funds are being created for relevant startups. Mayank Kapur, CTO of Indian AI startup Gramener, explained in an interview that the government is still the largest potential customer for data science services in the country. There have been several public sector initiatives encouraging the spread of AI. The government has initiated a proof of concept pilot in 15 districts (counties) in India to use artificial intelligence-based real-time advisory based on satellite imagery, weather data, etc. to increase farm yields where the farm production levels are low. Another long-term project to build a complete natural language processing platform for Indian languages is in operation. This would aid in the development of several applications, like conversational general and career counseling through chatbots and assistants, conversing in 22 Indian languages.

An interest in AI

The level of interest in learning about AI and implementing it in the business is gradually growing in India. Industries have started working to skill their manpower to enable themselves to compete with other global players. Educational institutions have started working on their curricula to include courses on machine learning and other relevant areas. Individuals and professionals have started acquiring these skills and are comfortable investing in upgrading their skills.

“Indian society is not as forgiving to failure in entrepreneurship as the US or Europe”

Dr Nishant Chandra, the Data Science Leader of the Science group at AIG started so in an interview while talking about the high stakes of failure in India. The established startup leaders like Mr Professor Manish Gupta, CEO of VideoKen has described that there is a trend of copying the ideas in the AI market of India, which prevents the Indian potential from flourishing and participating in the global market.

What the future holds

The learning phase of the AI sector in India is evident given the state of AI adoption in the Western markets and it may last longer in India’s relatively underdeveloped economy. Aakrit Vaish, CEO of Haptik, Inc. also seems to suggest that in the next 10 years, the understanding of AI and how it works will potentially be more commonplace among most technical industry executives. He thinks that India may go in the direction that China has gone, become their economies. There are probably going to be pockets, Bangalore might be good at deep tech like robotics or research / Hyderabad being good at data/ AI training, Mumbai being good at BFSI and  Delhi for agriculture and government. Like China, most solutions will probably be applied to the local economy.

Komal Talwar from the Government of India’s AI Task Force has stated “We think AI could have a great impact in the health sector. There is a scarcity of good doctors and nurses, with AI the machine can do the first round of diagnostics. Staff can carry machines with them to help cut down on the physical presence needed for doctors.” According to her, the government is encouraging startups to have AI applications that have a social impact (AI in health, AI in education, etc), where startups compete to solve social problems.

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Commentary: We like to imagine an AI-driven future, but it’s machine learning that will actually help us to progress, argues expert Michael I. Jordan.

We bandy about the term “artificial intelligence,” evoking ideas of creative machines anticipating our every whim, though the reality is more banal: “For the foreseeable future, computers will not be able to match humans in their ability to reason abstractly about real-world situations.” This is from Michael I. Jordan, one of the foremost authorities on AI and machine learning, who wants us to get real about AI.

Augmenting people

“People are getting confused about the meaning of AI in discussions of technology trends—that there is some kind of intelligent thought in computers that is responsible for the progress and which is competing with humans. We don’t have that, but people are talking as if we do,” he noted in the IEEE Spectrum article.

Instead, he wrote in an article for Harvard Data Science Review, we should be talking about ML and its possibilities to augment, not replace, human cognition. Jordan calls this “Intelligence Augmentation,” and uses examples like search engines to showcase the possibilities for assisting humans with creative thought.

And, to be clear, machines are much better at some things. For instance, people could do low-level pattern-matching but at a significant cost, whereas machines are able to perform such mundane tasks at relatively little cost. Another example is that ML is broadly used for fraud detection in financial services. We could have people poring over millions upon billions of transactions, but it makes more sense to point computers at the problem.

We know that most AI projects fail. In Jordan’s emphasis on ML over AI, there’s perhaps a clue as to why AI projects fail (inflated expectations) and how to make ML projects succeed (tightly define projects to augment, not supplant, human actors). 

The more we get “real” with AI, in other words, the more likely we’ll find success. Fortunately, Jordan wrote, most of the time when we’re talking about AI, we really mean ML. “ML is an algorithmic field that blends ideas from statistics, computer science and many other disciplines to design algorithms that process data, make predictions and help make decisions,” he wrote in the Harvard Data Science Review. ML is essential to “any company in which decisions could be tied to large-scale data,” he added. 

So…the first rule for success in AI is to stop doing AI, and instead consider data science problems as fundamentally about ML, about finding patterns in large quantities of data. It’s not Jetsons, but it’s real.

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Over the last years, AI applications have come to play a role in many security‐related fields. CSS Director Andreas Wenger and Sophie-​Charlotte Fischer show in this article that scholars who want to study AI’s link to power and security should widen their perspective to include conceptual approaches from science and technology studies (STS).

In this article, the authors introduce two STS‐inspired concepts and show how the study of AI and security could benefit from them. Finally, they focus on AI in the context of Switzerland to underscore what aspects the two previously introduced concepts help to highlight, which might remain invisible for traditional approaches.

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