Sample Reading Comprehension - 3 | HPSC Preparation: All subjects - HPSC (Haryana) PDF Download

Directions: Read the passage and answer the questions that follow:
A recent discussion paper from NITI Aayog outlines an ambitious strategy for India to establish itself as a powerhouse in artificial intelligence (AI). AI involves computer systems making decisions typically carried out by humans. India already encounters various forms of AI, such as chatbots on retail websites and programs identifying fraudulent bank activities. However, NITI Aayog envisions AI solutions for India on an unprecedented scale, particularly in agriculture, healthcare, education, smart cities and infrastructure, and transport.
For instance, in agriculture, machines are envisioned to provide farmers with information on soil quality, optimal sowing times, herbicide application areas, and potential pest infestations. This idea holds significant potential, especially considering India has 30 million farmers with smartphones but lacks efficient extension services. If computers assist agricultural universities in advising farmers on best practices, it could lead to a revolutionary transformation in farming practices.
Nonetheless, significant challenges lie ahead. While AI startups offer some solutions, the real challenge lies in scaling these solutions to cover the entire value chain, as envisaged by NITI Aayog. The primary hurdle is data. Machine learning, a set of technologies used to create AI, demands substantial amounts of historical data to identify relationships among data elements and make predictions. More advanced forms of machine learning, like "deep learning," attempt to replicate the human brain, requiring even more data than traditional machine learning. Unfortunately, India lacks sufficient data in crucial sectors like agriculture, hindering the progress of AI-based businesses.
In fact, the scarcity of data means that deep learning is ineffective for many companies in India. Climate-Connect, a Delhi-based firm, uses AI to predict solar power generation, but the lack of historical data limits the application of deep learning. For example, India's plan to install 100 GW of solar power by 2022 requires accurate predictions, but Climate-Connect relies on traditional machine learning due to the limited historical data.
Another challenge for AI firms is the scarcity of skilled professionals. According to NITI Aayog's report, only around 50 Indian scientists are engaged in "serious research," primarily concentrated in elite institutions such as the Indian Institutes of Technology and the Indian Institutes of Science. Moreover, only about 4% of AI professionals have experience in emerging technologies like deep learning. The skill gap affects companies, but open libraries of machine learning code and their customization to address Indian problems mitigate this to some extent, allowing even computer science graduates to handle the customization without starting from scratch.