India’s AI Readiness | Current Affairs & Hindu Analysis: Daily, Weekly & Monthly - UPSC PDF Download

India's AI Market

Why in News?

• India's GDP has surged to USD 3.5 trillion, nearly doubling in a decade, showcasing impressive economic growth. The emphasis on Artificial Intelligence (AI) is crucial for driving transformative changes.

Key Highlights of India's AI Market

• Growing Adoption Across Sectors: AI is being integrated into various sectors across India, propelled by initiatives like the National AI Strategy and the National AI Portal.
• Emphasis on Data Analytics: Companies are leveraging analytics to derive insights, improve operations, and foster innovation, supported by efforts like NASSCOM's AI for All program.
• Emerging AI Clusters: Major cities such as Bengaluru, Hyderabad, Mumbai, Chennai, Pune, and the National Capital Region (NCR) are developing robust AI clusters, driven by conducive policies and academic institutions.
• Bengaluru: Often dubbed the "Silicon Valley of India," Bengaluru is home to over 2,000 startups and boasts significant AI research, with more than 400 patents filed annually.
• Research and Development: Institutions like IITs, ISI, and IISc are at the forefront of AI research in India, contributing significantly to global knowledge.
• Investment Opportunities: There are vast opportunities in India's AI market, including applications in IoT for precision farming, fraud detection in banking, and AI-driven predictive diagnostics in healthcare.

What is Artificial Intelligence (AI)?

• About: Artificial Intelligence (AI) refers to the capability of machines to perform tasks that typically require human intelligence, such as learning, reasoning, and understanding language. Common applications of AI include virtual assistants, predictive analytics, and robotics, which enhance the efficiency of devices by enabling them to learn from data.
• The term "Artificial Intelligence" was coined by John McCarthy, a pioneering American computer scientist and cognitive scientist who played a crucial role in establishing the field of AI.
• Characteristics and Components: A key characteristic of AI is its ability to rationalize and take actions to achieve specific goals effectively. Within the realm of AI, Machine Learning (ML) is a subset that focuses on enabling systems to learn from data. Deep Learning (DL) techniques, a further subset of ML, facilitate the analysis of vast amounts of unstructured data, such as text, images, and videos.

Types of AI:

• Reactive AI: These systems optimize outputs based on inputs but do not learn from past experiences. An example is chess-playing AI.
• Limited Memory AI: These systems adapt based on past experiences but have a limited memory span. Autonomous vehicles that learn from their environment are an example.
• Theory-of-Mind AI: These are fully adaptive systems capable of extensive learning and retention, exemplified by advanced chatbots that could pass the Turing Test.
• Self-aware AI: This is a hypothetical concept where systems would be sentient and aware of their existence, a notion largely found in science fiction.

Differences Between AI, ML, and DL:

• Artificial Intelligence (AI): AI involves the simulation of human intelligence by machines to perform tasks that typically require human cognitive functions.
• Machine Learning (ML): ML is a subset of AI focused on algorithms that enable computers to learn from data without being explicitly programmed for each task.
• Deep Learning (DL): DL is a subset of ML that uses artificial neural networks to learn from data, mimicking the way the human brain processes information.

Current Governance of Global AI

• India: NITI Aayog has developed guiding documents including the National Strategy for AI and the Responsible AI for All report, emphasizing social and economic inclusion, innovation, and trustworthiness in AI development.
• United Kingdom: The UK has adopted a light-touch regulatory approach, encouraging existing laws to be applied to AI. A white paper outlines five principles for companies, including safety, transparency, fairness, accountability, and contestability.
• United States: The US introduced a Blueprint for an AI Bill of Rights (AIBoR), highlighting the potential risks AI poses to economic and civil rights and offering governance strategies for specific sectors such as health and education.
• China: In 2022, China implemented some of the first nationally binding regulations targeting algorithms, specifically regulating recommendation algorithms to control information dissemination and influence.

Role of AI in India’s Economic Growth

• Banking and Finance: AI is streamlining operations in the banking sector through Robotic Process Automation (RPA) for tasks like data entry and compliance checks, potentially lowering operational costs by up to 25%. AI algorithms are also crucial in fraud detection and customer support, with AI chatbots expected to save the sector USD 7.3 billion annually.
• Healthcare: AI is revolutionizing diagnostics and treatment in India, with algorithms capable of analyzing medical images for conditions like tuberculosis and diabetic retinopathy more accurately and rapidly than traditional methods. Initiatives such as the Ayushman Bharat scheme are enhanced by AI, improving healthcare delivery and management efficiency.
• Agriculture: AI plays a vital role in food security and rural income enhancement through precision farming, which utilizes AI to analyze satellite imagery and IoT data for optimized crop management and irrigation. AI also aids in predicting pest outbreaks and improving crop yield forecasts.
• E-Commerce: AI is driving growth in India's booming e-commerce sector by enhancing personalized shopping experiences and optimizing supply chains through demand prediction and logistics automation. Targeted AI-driven marketing strategies are also improving customer engagement and conversion rates.
• Driving Innovation: AI is fostering innovation across industries, with a surge in AI-driven startups in sectors such as healthcare and fintech. The NASSCOM report indicates that the AI sector is poised to make significant contributions to the economy, creating jobs and promoting diversification in economic activities.

India's Initiatives Related to Artificial Intelligence

• Building India’s Own AI Stack: Efforts are underway to create a robust AI infrastructure and ecosystem within the country.
• INDIAai: A national initiative aimed at promoting and facilitating the use of AI across various sectors.
• Global Partnership on Artificial Intelligence (GPAI): India is actively participating in international collaborations to advance AI research and applications.
• US India Artificial Intelligence Initiative: Strengthening ties with the US to foster AI development and innovation.
• Responsible Artificial Intelligence (AI) for Youth: Promoting ethical and responsible AI practices among the youth.
• Artificial Intelligence Research, Analytics and Knowledge Assimilation Platform: A platform to support AI research and knowledge sharing.
• Artificial Intelligence Mission: A dedicated mission to drive AI advancements and applications in the country.

Challenges Associated with AI in the Indian Economy

• Skilled Workforce Shortage: Despite efforts to enhance education and training in AI, there is a considerable gap in AI talent in India. The demand for skilled professionals surpasses supply, limiting the capacity to innovate and deploy AI solutions across various sectors.
• Data Access and Quality: Effective AI models necessitate diverse and high-quality datasets. Currently, datasets, especially for Indian languages, are often inadequate, hindering the development of robust indigenous AI solutions. The lack of comprehensive data hampers the effectiveness and scalability of machine learning applications.
• High Implementation Costs: The expenses associated with deploying AI technologies, particularly in sectors like manufacturing and healthcare, can be prohibitive. These costs encompass infrastructure investments and the integration of AI systems, which may deter widespread adoption.
• Infrastructure Deficiencies: Advanced cloud computing infrastructure is vital for effective AI deployment. While initiatives like AIRAWAT represent progress, India still lacks the comprehensive facilities necessary to scale AI applications efficiently.
• Geopolitical and Regulatory Challenges: Tensions in global geopolitics and export control regulations can limit access to essential AI technologies. Such restrictions impact India’s ability to develop and implement AI solutions effectively, potentially isolating it from significant advancements in the field.

Way Forward

• Developing AI Ecosystem: Despite significant digitization, India's compute penetration remains low. While the nation has excelled in IT services, these contribute only 1% of the global USD 30 trillion technology industry. In contrast, countries like China have rapidly invested heavily in AI research, infrastructure, and talent. India must leverage its strengths in data, computing, and algorithms to establish its own AI stack.
• Data Sovereignty: Data colonization refers to the control and exploitation of data resources by foreign entities, raising concerns about data sovereignty and national security. Although India generates 20% of the world’s data, a staggering 80% is stored offshore, processed into AI, and then imported back in monetary form. Building on successes such as UPI, UIDAI, and ONDC, India can develop the world’s largest open-source AI platform rooted in its ethos.
• Data Quality and Accessibility: High-quality, diverse datasets are essential for effective AI training. Efforts should focus on enhancing data collection, cleaning, and labeling processes, along with promoting data sharing to foster collaboration across various fields.
• Continued Education and Workforce Development: Preparing the workforce for an AI-driven future is crucial. Initiatives aimed at AI education and upskilling can equip individuals with the necessary skills for the evolving job market. Encouraging collaboration between academia, industry, and government can further enhance these efforts.
• International Collaboration and Standards: Global collaboration is vital for sharing knowledge and best practices in AI. Establishing international standards and frameworks can promote interoperability, fairness, and security in AI development and deployment.