Search

Countries across the world are grappling with how to deal with the rapid developments in Artificial Intelligence (AI) and its uses. In this article we analyse three such jurisdictions: the UK, Singapore and India. A common theme that prevails across these jurisdictions is the focus on principles and guidelines instead of straitjacketed regulations that tend to be more inflexible. Another common theme is the reluctance to adopt AI tools that serve to replace human decision makers. Some of these approaches are still evolving – particularly in a country like India where the burgeoning case load may yet make way for automated resolution for small value claims. [ABSTRACT FROM AUTHOR]

In the modern world, modern medicine and complementary systems dominate the healthcare worldwide. Many developing countries which suffer with resources can improvise the healthcare at their retention by leveraging the extent of traditional medicine. A traditional Indian Atharva Veda implemented system of medicine called the Ayurveda, has gigantic benefits to human kind. This way of medicine deals majorly with naturally available herbs and methodologies to treat human health and promotes healthy practices for a better lifestyle. Ayurveda works on the idea of balancing biological systems. Ayurveda is primarily accomplished in India and being recognized internationally due to the effectiveness and use of naturally available herbs and associated methodologies to cure the diseases. Ayurveda as a system is successful, yet not widely practiced, there are range of reasons that has limited Ayurveda to reach its potential, such as less importance is given to Ayurvedic education and its practices, very few or no advancement is seen, resources limitations due to funds, limited technical support, and unstructured database. Therefore there is a constant need of improvising the field with technology and effectively creating knowledge based systems to improvise the practice of Ayurveda and to optimize its benefits. In this paper, a survey on the benefits of Ayurveda for women hormonal imbalance, Ayurveda for Skin and Hair health is done along with the Artificial intelligence technology advancements in the field of Ayurveda. The intend of the paper is to layout the benefits this 5000 years old approach towards medicine has to offer and the use of Artificial Intelligence to promote and influence its growth. [ABSTRACT FROM AUTHOR]

Artificial Intelligence is the most advance technology of our time. It is a smart device capable of performing tasks that normally require human intelligence. We are just at the beginning to see the possibilities of artificial intelligence in education. There is a prediction that in future participation of human teacher in classrooms would up to maintain discipline and assist Artificial Intelligence devices while machines will do the real job of teaching. In India, Gross Enrolment Ratio (GER) in schools and colleges is increasing but there is a problem of poor learning outcome and low retention rate (dropout of students). There are many factors that pose challenges to improving the quality of education like there is a single teacher for multi-grade classroom in small and remote villages where students of different grade sit in same classroom, large teacher vacancies due to uneven distribution across location, low adoption and lack of knowledge about how to use these existing technologies. Artificial Intelligence is the solution for problems like quality of education, prediction of dropouts, assessment of student needs and access issues etc. observed in Indian schools and colleges but there are some challenges in adoption of Artificial Intelligence in India, if we replace men with artificial man then there is a situation of mass unemployment because one artificial man equals to hundred men. Overall, we conclude that, today India is not ready for Artificial Intelligence because India is a developing nation and mostly depend on its human resource but future holds a lot of possibilities for Artificial Intelligence in India. [ABSTRACT FROM AUTHOR]

Artificial intelligence is being reconfigured as a set of technologies that can address poverty with precision. The impacts of AI will both exacerbate and ameliorate the conditions of uneven development. Recent debates focus on the disruptive effects of AI, particularly to replication of development trajectories that have had success in reducing poverty. In this paper we further these debates by analysing the backroads of AI. The backroads serve as a metaphor for understanding the ways AI will travel from the sites of incubation to the frontlines of uneven development. We explore dialogues between AI and development in two arenas: the World Bank's Development Impact Evaluation initiative (DIME) and the Government of India's national AI strategy, #AIforAll. We argue that both these arenas serve as hubs from which AI will travel out along the backroads to remote, poor, and fragmented polities. While the World Bank utilises AI as technology to empower experts and mobilise a techno‐political authority, what we refer to as precision AI, India seeks to function as a second‐tier AI hub, making AI cheaper and more accessible domestically and for other developing countries, what we refer to as populist AI. We conclude by discussing the interrelations of precision and populist AI along the backroads, and the potential of backroads research for mapping AI, uneven geographies of development and technology and the impacts of AI's disruptions at different scales. Artificial intelligence is being reconfigured as a set of technologies that can address poverty with precision. We further these debates by analysing the backroads of AI in two arenas: the World Bank's Development Impact Evaluation initiative (DIME) and the Government of India's national AI strategy #AIforAll. While the World Bank utilises AI as technology to empower experts and mobilise a techno‐political authority, what we refer to as precision AI, India seeks to function as a second‐tier AI hub, making AI cheaper and more accessible domestically and for other developing countries, what we refer to as populist AI. [ABSTRACT FROM AUTHOR]

In the 21st century, global waste challenges worsen in developing nations relying on manual sorting. This improper waste disposal poses significant threats to human health and the environment, necessitating the adoption of Artificial Intelligence-Based Solid Waste Segregation Technology (AIBSWST). In this context, the advanced Frank t-norm captures nuanced relationships in fuzzy logic, crucial in scenarios where fuzzy set order matters. Building on these principles, the complex q-rung picture fuzzy set (Cq-RPFS) becomes instrumental in representing decision-makers preferences in a two-dimensional manner, enhancing the handling of vague information in real-world scenarios. Expanding on these foundational principles, the paper introduces innovative Frank operations grounded in Frank t-norms within the context of Cq-RPFS. Leveraging these operations, the paper proposes four robust aggregation operators (AOs) under Cq-RPFS: complex q-rung picture fuzzy Frank weighted average (Cq-PFFWA), complex q-rung picture fuzzy Frank weighted geometric (Cq-PFFWG), complex q-rung picture fuzzy Frank ordered weighted average (Cq-PFFWOA), and complex q-rung picture fuzzy Frank ordered weighted geometric (Cq-PFFWOG). These AOs exhibit essential properties such as idempotency, monotonicity, and boundedness. A Multi-Criteria Decision-Making (MCDM) method based on the proposed AOs is suggested to validate these strategies. A real-life case study on India's adoption of AIBSWST serves as a practical application, with thorough analyses, including sensitivity, comparative, and superiority assessments, evaluating the performance of the approaches. A thoughtful discussion of the pros and cons of the proposed AOs accompanies the analysis, emphasizing the significance of the approach in ensuring the cleanliness and health of developing nations. [ABSTRACT FROM AUTHOR]

In the paper I'm going to highlight the maturing insurance industry in our country & how it deals with the ongoing frauds by various means. With the emerging technology, fraudsters are becoming smart & new sophisticated fraud ideas are emerging. To address this, the Insurance Industry needs to be upgraded to become smart in tackling the market wide problem that happens in so many different ways & at so many different levels. Insurance Industry needs to smartly prevent, detect & thereby manage frauds from occurring. With the emerging technology the companies are adapting to newer upgraded equipment & tools to prevent & detect fraud from happening. The role of data analytics is important in the tackling of the menace through continuous & repeated reviewing & scoring of the claims. Companies are now adopting newer & layered tools to detect fraud. While there are Anti-Fraud policies being issued by companies to have internal check on the fraud, there are various other frameworks to keep a check on both external & internal fraud. Now is the time for Artificial Intelligence to be adapted by the industry. A lot of companies are now accepting that the new trend of IoT (internet of things) & chatbots needs to be applied for larger data analysis, quick policy issuance & faster claim processing without hassle & with early detection of possibility of fraud. Things are moving faster in the industry & India needs to keep pace so also adopt technology which can help prevent, detect & manage fraud in a smarter way. Quite a few developed countries with a matured insurance industry are able to deal with insurance fraud legally not only because of the adaptation of developed technology but also because their insurance fraud laws are in place. India as a developing country with a maturing Insurance market, also needs to have a proper law in place for Insurance Fraud. [ABSTRACT FROM AUTHOR]

Applying artificial intelligence in education is relevant to addressing the current educational crises. Many available solutions apply Convolutional Neural Networks (CNNs) to help improve educational outcomes. Therefore, a series of works have been developed integrating techniques in different educational contexts, for instance, in online teaching practices. Given the various studies and the relevance of CNNs for educational applications, this paper presents a systematic literature review to discuss the state-of-the-art. We reviewed 133 papers from the IEEE Xplore, ACM Digital Library, and Scopus databases. Based on our revision, we discuss characteristics of studies such as publication venues, educational context, datasets, types of CNNs models, and performance of models. We evidence that the literature regarding CNNs still misses more studies discussing educational problems faced by Global South students, considering both teaching and learning perspectives. Such a population cannot be neglected during experiments due to specific educational weaknesses (for example, basic skills) demanding personalized solutions. • A systematic literature review of 133 published papers. • China covered most publications, followed by India. • There is a research gap regarding using CNNs in some regions of the Global South. • The main educational context with CNN applications is students' performance. [ABSTRACT FROM AUTHOR]

This article examines the history of a similarity measure—the Mahalanobis Distance Function—and its movement from colonial India into contemporary artificial intelligence technologies, including facial recognition, and its reapplication into postcolonial India. The article identifies how the creation of the Distance Function was connected to the colonial "problem" of caste and ethnic classification for British bureaucracy in 1920-1930s India. This article demonstrates that the Distance Function is a statistical method, originating to make anthropometric caste distinctions in India, that became both a technical standard and a mobile racialized technique, utilized in machine learning applications. The creation of the Distance Function as a measure of "similitude" at a particular period of colonial state-making helped to model wider categories of classification which have proliferated in facial recognition technology. Overall, we highlight how a measurement function that operates in recognition technologies today can be traced across time and space to other racialized contexts. [ABSTRACT FROM AUTHOR]