Editorial✍ Hindu Edi Prelims cum Mains

The dream of being an AI powerhouse

Artificial Intelligence (AI):
  • AI is the use of computers to make decisions that are normally made by humans.
  • Many forms of AI surround Indians already, including chatbots on retail websites and programs that flag fraudulent bank activity.
NITI’s paper on AI:
  • In a recent discussion paper, NITI Aayog has chalked out an ambitious strategy for India to become an artificial intelligence (AI) powerhouse.
  • The paper focuses on how India can leverage the transformative technologies to ensure social and inclusive growth and serve societal needs
  • NITI envisions AI solutions for India on a scale not seen anywhere in the world today, especially in five key sectors:
    1. Agriculture
    2. Healthcare
    3. Education
    4. Smart cities and infrastructure
    5. Transport
  • Example of agriculture:
    • In agriculture, machines can provide information to farmers on the quality of soil, when to sow, where to spray herbicide, and when to expect pest infestations.
    • India has 30 million farmers with smartphones, but poor extension services. If computers help agricultural universities advise farmers on best practices, India could see a farming revolution.
1. Lack of data:
  • Machine learning, the set of technologies used to create AI, needs to huge amount of data.
  • It takes reams of historical data as input, identifies the relationships among data elements, and makes predictions.
  • More sophisticated forms of machine learning, like “deep learning”, attempt to mimic the human brain.
  • And even though they promise greater accuracy, they also need more data than what is required by traditional machine learning.
  • Unfortunately, India has sparse data in sectors like agriculture, and this is already hampering AI-based businesses today.
  • Examples:
    • To use this technology to grade produce at Agriculture mandis, millions of images of produce need to be collected and analyzed. In this regard government can help in collecting, digitising and annotating images at agricultural mandis.
    • Another example is that AI in solar sector needs data (like time of sunrise and sunset, cloud cover) over many years to predict generation. Unfortunately, such data is available only for a couple of years.
2. Skill shortage in AI:
  • AI firms are also struggling in finding the right people.
  • As per the NITI Aayog’s report, only about 50 Indian scientists carry out serious research and they are concentrated in elite institutions such as IITs and the IISc
  • Meanwhile, only about 4% of AI professionals have worked in emerging technologies like deep learning.
  • A survey of LinkedIn found 386 out of the 22,000 people with PhDs in AI across the world to be Indians.
How does this skill gap impact companies?
  • Open libraries help to some extent:
    • To some extent, open libraries of machine learning code, which can be customised to solve Indian problems, help.
    • This means that companies need not write code from scratch, and even computer science graduates can carry out the customisation.
  • But greater knowledge needed to solve problems:
    • Open libraries can only go so far.
    • For some technical problems, such libraries don’t exist.
    • Solving them requires some knowledge of mathematics as well as deep learning.
    • But finding people with such knowledge is proving hard.
Can India build AI work force?
  • NITI Aayog envisions that India can become an “AI garage” for 40% of the world.
  • The discussion paper, however, mentions no timeline for this goal.
Changes needed for it to happen: 
  • For any reasonable time frame for execution, much needs to change immediately.
  • 1. Need more data:
    • If the government is serious about AI solutions powering agriculture or healthcare, it must collect and digitise data better under its existing programs.
  • 2. Networks of AI research:
  • To close the skill gap, NITI Aayog suggests setting up a network of basic and applied AI research institutes.
  • AI is a collaborative process in which scientists developing solutions for certain sectors need an intimate knowledge of those sectors.
  • To find solutions in the sectors highlighted, they must collaborate closely with agricultural universities, medical colleges and infrastructure planners.
GS Paper III: Science & Technology

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