PurposeThis research contributes to current debates on automation and the future of work, a much-hyped but under researched area, in emerging economies through a particular focus on India. It assesses the national strategy on artificial intelligence and explores the impact of automation on the Indian labour market, work and employment to inform policy.Design/methodology/approachThe article critically assesses the National Strategy on AI, promulgated by NITI Aayog (a national policy think tank), supported by the government of India and top industry associations, through a sectoral analysis. The key dimensions of the national strategy are examined against scholarship on the political economy of work in India to better understand the possible impact of automation on work.FindingsThe study shows that technology is not free from the wider dynamics that surround the world of work. The adoption of new technologies is likely to occur in niches in the manufacturing and services sectors, while its impact on employment and the labour market more broadly, and in addressing societal inequalities will be limited. The national strategy, however, does not take into account the nature of capital accumulation and structural inequalities that stem from a large informal economy and surplus labour context with limited upskilling opportunities. This raises doubts about the effectiveness of the current policy.Research limitations/implicationsThe critical assessment of new technologies and work has two implications: first, it underscores the need for situated analyses of social and material relations of work in formulating and assessing strategies and policies; second, it highlights the necessity of qualitative workplace studies that examine the relationship between technology and the future of work.Practical implicationsThe article assesses an influential state policy in a key aspect of future of work–automation.Social implicationsThe policy assessed in this study would have significant social and economic outcomes for labour, work and employment in India. The study highlights the limitations of the state policy in addressing key labour market dimensions and work and employment relations in its formulation and implementation.Originality/valueThis study is the first to examine the impact of automation on work and employment in India. It provides a critical intervention in current debates on future of work from the point of view of an important emerging economy defined by labour surplus and a large informal economy.
Services have become the engine of growth in a large number of economies in the developing world. Additionally, the rapid development of ICT, and emergence of transnational corporations, has not only made cross-border provision of services easier, but has also increased the demand for and trade in services; developing countries today are increasingly emerging as cost efficient providers of key business and professional services, thereby becoming key players in the services supply chain.
In today’s world, computer technologies have advanced a lot. One of its greatest gifts to the world is Artificial Intelligence. Natural Language Processing (NLP) and Machine Learning (ML) are two of its subdomains. In this paper, modified versions of two common NLP and ML algorithms have been used to classify food reviews and provide suitable recommendations from them. Currently, reviews can be classified into positive and negative reviews, but it becomes difficult when one review says positive about item A and negative about item B. Moreover, the current Apriori algorithm doesn’t consider the feedbacks from customers (reviews). Modified classifier algorithm and consequently, modified Apriori algorithm has been used to classify each statement part by part and provide recommendations, not just on previous purchases but also using the reviews about above-mentioned purchases. The algorithms can be used for purposes other than food analysis also – wherever purchases and reviews are involved. For e.g., e-commerce companies can use the algorithms to predict and recommend suitable items a user may be interested in.
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