The covid19 pandemic is a global emergency that badly impacted the economies of various countries. Covid19 hit India when the growth rate of the country was at the lowest in the last 10 years. To semantically analyze the impact of this pandemic on the economy, it is curial to have an ontology. CIDO ontology is a well standardized ontology that is specially designed to assess the impact of coronavirus disease and utilize its results for future decision forecasting for the government, industry experts, and professionals in the field of various domains like research, medical advancement, technical innovative adoptions, and so on. However, this ontology does not analyze the impact of the Covid19 pandemic on the Indian banking sector. On the other side, Covid19IBO ontology has been developed to analyze the impact of the Covid19 pandemic on the Indian banking sector but this ontology does not reflect complete information of Covid19 data. Resultantly, users cannot get all the relevant information about Covid19 and its impact on the Indian economy. This article aims to extend the CIDO ontology to show the impact of Covid19 on the Indian economy sector by reusing the concepts from other data sources. We also provide a simplified schema matching approach that detects the overlapping information among the ontologies. The experimental analysis proves that the proposed approach has reasonable results.

Ever since the advent of AI and it's entry in India, its escalation has been directly proportional to that of Indian economy. AI and economy in India are in immense competition with that of other nations and the'Make in India' drive is one such move to make it climb the progression ladder. Launched in 2014, Make in India is an initiative by Prime Minister Narendra Modi to transform India into a global designing and manufacturing hub. Back in 2013, the year that witnessed aggressive decline in technology and economy that somewhat resonated with America's great depression; Make in India drive was a response to handle this critical situation. The idea is to boost India's progress in terms of productions and widen the chances of businesses.

The RAISE 2020 summit (Responsible AI for Social Empowerment) has brought issues around artificial intelligence (AI) to the centre of policy discussions. Countries across the world are making efforts to be part of the AI-led digital economy, which is estimated to contribute around $15.7 trillion to the global economy by 2030. India, with its "AI for All" strategy, a vast pool of AI-trained workforce and an emerging startup ecosystem, has a unique opportunity to be a major contributor to AI-driven solutions that can revolutionise healthcare, agriculture, manufacturing, education and skilling. AI is the branch of computer science concerned with developing machines that can complete tasks that typically require human intelligence. With the explosion of available data expansion of computing capacity, the world is witnessing rapid advancements in AI, machine learning and deep learning, transforming almost all sectors of the economy.

AI’s true potential emerges from its ability to drive transformation across multiple sectors through diverse range of applications. Research suggests that we can best understand the implications of AI by viewing AI as a General Purpose Technology (GPT). AI as a GPT implies, that AI led innovations will be reflected not only as direct contribution in the sectors it is applied to, but also drive complementary innovations and spillover benefits across the economy. Source: Implications of AI on Indian Economy “For more than 250 years the fundamental drivers of economic growth have been technological innovations…… The most important general-purpose technology of our era is artificial intelligence, particularly machine learning.” —Erik Brynjolfsson and Andrew McAfee, 2018 Three critical features characterizing GPTs that AI fulfils: GPTs are pervasive, evolve and improve with time, and play role in enabling innovation. Pervasiveness: AI pans across various “application sectors” including automotive, banking, consumer goods, healthcare, insurance, pharmaceuticals, retail, telecommunications, and transport and logistics sectors, etc., making it pervasive Technical Improvements: Field of AI continues to undergo significant transformations, not just in terms of performance and applicability but also changing trends in various AI techniques like the rise of game theory, machine learning and natural language processing Enabling Innovations: Diffusion of AI has enabled a wide range of activities that were unimaginable before. AI’s predictive capabilities are reducing costs and altering organizational costs across verticals Like other GPTs in the past, the effects of AI will not be fully realized until waves of complementary innovative solutions are developed, implemented and deploying. GPTs have unlocked the growth potential and played a significant role in driving economies. AI fulfils certain fundamental characteristics of GPTS as highlighted above, and thus to is expected to validate the future promise of AI-driven economic growth. When one thinks of AI as a GPT, the implications for output, welfare and productivity gains are huge. Conceptualising AI as a GPT and its adoption will: Drive innovation across sectors Generate social benefits and improve welfare/productivity Result in spillover benefits throughout economy NASSCOM, ICRIER and Google conducted a joint study on Implications of AI on Indian Economy. The report traces the impact of AI on the Indian economy using an econometric model to estimate the impact of GPTs, such as AI, on firm productivity. Watch out for more interesting articles on AI ! References [1] Implications of AI on Indian Economy [2] Facebook AI Research [3] Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics [4] http://ide.mit.edu/sites/default/files/publications/2019-04JCurvebrief.final2_.pdf

Conceptualizing AI as a GPT and estimating AI’s impact on the Indian economy provides us with an understanding of how the economic impact of AI-led innovations is not only reflected as direct contribution to different sectors but also as indirect effects on productivity. While the econometric model provides us with adequate evidence for policy to support wider adoption, actionable measures are derived using evaluation of capabilities of both AI developing and utilizing firms. The recently launched report by ICRIER, NASSCOM and Google on Implications of AI on the Indian Economy, uses a case study based approach to assess the growth potential of AI and organizations are actually realizing it through the analysis of 13 firms (largely small-scale startups) developing and testing AI technologies in India. The framework guiding the case study analysis is based on the ‘capability theory of the firm’. Image source: Pinterest The fundamental pursuit of this framework is to understand what it takes – what are the requisite capabilities for an organization that enables it to successfully perform the tasks of innovating and adopting/building complex technologies like AI. There are two parts to this story – the first, that captures capabilities being exercised or lacking among firms that develop applications, while the second part is the one that focuses on the analysis of the impact of AI on firms that have adopted AI-based solutions. I. Analysis to capture capabilities among AI developing firms in India Investment Capabilities: Enable firms in the AI ecosystem to identify a problem that can solved, the extent to which AI-based solution can be deployed and the choice of AI technology. Building these capabilities requires the right AI talent. Production Capabilities: For a firm supplying AI solutions, production capabilities could possibly include: Organizing and generating data Training and improving AI algorithm Producing research in AI-based tools being developed by firm Patentability of AI-based applications Linkage Capabilities: Firms supplying AI-based solutions in India can network with research institutions, user industries, consumers and other well-established suppliers of AI technologies through varied modes of interaction II. Analysis of impact of AI on firms adopting AI-based solutions Economic Implications for businesses adopting AI: Economic impact of AI-based applications can be felt at both core and supporting functions in activities of production and organization. AI capabilities can be leveraged to improve quality testing and control methods across a range of industries like agriculture, BFSI, education, etc. For instance, Nebulaa uses data from various agricultural markets in India in a convolutional neural net-based grain analyser for quick, accurate and cheap quality testing. Their current efforts have reduced the testing time of grains from 30 minutes to almost instantaneous results and lowered costs by 75%. Social Implications of AI Applications: AI’s growing presence and applications are observed in areas that are crucial for a better society in areas like healthcare, energy and utilities, etc. For instance, Artelus demonstrates an example of AI generating positive impacts on healthcare. The product developed by them captures product captures and analyzes retinal images of patients within 15 seconds, and can detect an occurrence of Diabetic Retinopathy within 3 minutes, with 93% accuracy. Implications on labor market: Existing AI applications do not have potential to replace all tasks, but only those that are low in skill content. Automation of tasks will result in labor displacement requiring reskilling/upskilling for reappointment For instance, vPhrase developed an AI-based application for presentation of data analytics and generating humanized narratives, which noted that the user firms of their application (belonging largely to the services sector) saw the potential to replace tasks of those involved in making and giving presentations. This article talks about using a capabilities framework as a guide to understand the causes that underline success or failure of India’s ecosystem in conducting AI-based innovation. The study highlights that although AI applications are visible across sectors; the impacts exist only in pockets, within these sectors and are yet to become widespread. Download our full report on Implications of AI on Indian Economy for more details on the capabilities and linked case studies. Watch out for my next article on the challenges that caution against AI’s unhindered deployment and development and the crucial role that government can play.

AI’s transformational potential stems from its ability to lend itself to diverse range of applications across a range of sectors. The study is based on the understanding that the implications of AI can be best understood by viewing AI as a General Purpose Technology (GPT). AI as a GPT implies that AI led innovations will be reflected not only as direct contribution in any given sector, but also inspire complementary innovations and spill over benefits across the economy. The objective of this report is to estimate the impact of AI on Indian economy. The report also provides policy recommendations that can enable the sustainable growth of the AI ecosystem in India. These include actionable policy steps that are individually important to the implementation of a large-scale AI program. Report Overview AI as a General Purpose Technology (GPT) AI’s ability to lend itself to diverse range of applications across a range of sectors, resembles that of GPT The economic impact of AI led innovations are not only reflected as direct contribution to sectors but also as indirect effects on productivity that GPTs trigger Key Results from Econometric Estimation The report results find a positive and significant relation between AI using firms and total factor productivity growth The estimate suggests that a unit increase in AI intensity will increase Total Factor Productivity (TFP) growth by 0.05% On average, a unit increase in AI intensity by AI-using firms can return $67.25 Billion or 2.5% of GDP to the Indian economy in the near term Micro-Evidence from Case Studies The report uses a capabilities framework as a guide to understand the causes that underline success or failure of India’s ecosystem in conducting AI-based innovation Capabilities of firms developing AI applications in India Investment Capabilities Production Capabilities Linkage Capabilities Impact of AI on firms adopting AI-based solutions Economic Implications for business adopting AI Social Implications of AI Applications Implications on labor market Policy Recommendations Government has an active role to play in creating institutions and enabling an AI ecosystem, while also encouraging private players to innovate and thrive. Action-oriented policy recommendations are critical for the implementation of a large-scale AI program

Artificial intelligence (AI) has reached new heights. The mix of the innovation, information, and talent that make intelligent frameworks possible has arrived at a critical stage, driving phenomenal development in AI investment. The time of AI has arrived. Established organizations are now moving past experimentation. Money is flowing into AI advances and applications at large organizations.

We have entered the era of Big Data, Cloud Computing and Artificial Intelligence (AI). It is currently in a swift development phase and continuously making technological advances. India has been a growing hub for business and ranks among the most attractive investment destinations for technology transactions in the world. In recent times, the country has focused on technology, realising that it is a key component of economic development. The government has been extensively promoting research, business incubators and parks, and has been improving on the Global Innovation Index position since 2016. This year the Ministry of Science and Technology has been allotted its largest budget till date by the Government of India.

Commerce and industry minister Piyush Goyal on Monday said artificial intelligence (AI) and machine learning can help India achieve the $5 trillion economy benchmark over the next five years. "Our government believes artificial intelligence, in different forms, can help us achieve the $5 trillion benchmark over the next five years, but also help us do it effectively and efficiently," Goyal said while inaugurating the National Stock Exchange (NSE) Knowledge Hub. The hub is an AI-powered learning ecosystem for the banking, financial services and insurance (BFSI) sector. The minister referred to an Accenture report that said AI and machine learning have the potential to contribute nearly $1 trillion to the Indian economy by 2035 and said the Knowledge Hub created by NSE will fill in the gaps and help the financial sector to move into the future. Goyal said Prime Minister Narendra Modi sat through a whole day with officials from various ministries to get the sense of urgency to understand the importance of AI and how it can help put the Indian economy on the fast track.

NEW DELHI: Commerce and industry minister Piyush Goyal on Monday said artificial intelligence (AI) and machine learning can help India achieve the $5 trillion economy benchmark over the next five years. "Our government believes artificial intelligence, in different forms, can help us achieve the $5 trillion benchmark over the next five years, but also help us do it effectively and efficiently," Goyal said while inaugurating the National Stock Exchange (NSE) Knowledge Hub here. The hub is an AI-powered learning ecosystem for the banking, financial services and insurance (BFSI) sector. The minister referred to an Accenture report that said AI and machine learning have the potential to contribute nearly $1 trillion to the Indian economy by 2035 and said the Knowledge Hub created by NSE will fill in the gaps and help the financial sector to move into the future. Goyal said Prime Minister Narendra Modi sat through a whole day with officials from various ministries to get the sense of urgency to understand the importance of AI and how it can help put the Indian economy on the fast track.