Harnessing AI Neural Networks and Generative AI for the Evolution of Digital Inclusion: Transformative Approaches to Bridging the Global Connectivity Divide

Abstract

Bridging the global connectivity divide is essential to enable the inclusion of marginalized populations in their regional and national digital information networks. Seeking the effective use of artificial intelligence (AI) neural networks and how generative AI capabilities can be employed to replicate human expertise in creating and editing digital information in the most marginalized is assessed. There is a vast, uneven global distribution of access to the internet, and where it is accessible, the available internet is geographically more concentrated for marginalized communities who are deftly reliant on non-internet sources to access information. For instance, the majority of non-literate adults in developing regions reside in eight nations. While omitting extensive contexts in Africa, this method encompasses contexts diverse in size, economy, governance, family, and language. Digital communication trends potentiate the opportunity for expanding a connectivity-divide in heterogeneous contexts more crucial under alarming speeches like COVID-19 or EVD. However, charging stations are typically semi-urban or urban-based, and marginalized communities prevail in rural and remote areas. Thus, more marginalized societies are more based on solar energy, while further charging points. AI technology is a broad flourishing example of societal development ushering in new opportunities and pledges to aid a cross-sector of fields for continuous transformative creation. The inquiry between AI growth and its policy setting, dispensed assets, and societal development is of overall importance since it is anticipated that AI deployment will quickly advance in the forthcoming future. But despite an anticipatory economic trend, the rise of AI technologies indicates various public-policy demands in an umbrella shaping forecasting building obstacles within societal sectors for AI propositions. There are numerous costs of expanding AI deployment, including technical proficiencies in the setting up, workforce development damages, positive externalities from AI growth, and trade or direct investment policy. Tech companies use their deep sets of patents and distinctive skills for monopolistic competition. Fewer firms can afford to invest time, money, and assets in AI development. In turn, this possibly decreases submission levels of AI research to research sharing. However, even in nations with a substantial proportion of tech companies meeting collaboratively to advance equality widely, such commitments can make it difficult for smaller corporations and scholars to enter the fridge. The prototype means ethicists must be wary of further favoring substantial corporations with available funds and unique AI technologies. AI technology prohibits duplicity by automating the production of matching data through a neural network to rewrite the second sentence so that it matches the first.