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Artificial Intelligence

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https://africarxiv.ubuntunet.net/handle/1/1624
When studying Artificial Intelligence (AI), the subject can be divided into several key categories, each addressing different aspects of the field. These categories provide a comprehensive framework for studying AI, allowing researchers, practitioners, and students to explore the field from multiple perspectives, considering both the technical aspects and the broader societal impacts. Here's an overview:

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Browsing Artificial Intelligence by Author "Njunguna, Samuel"

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    Strengthening the Emerging Digital Technologies Ecosystem in Kenya
    (2023-09) Ogot, Madara; Muthee, Margaret; Muriuki, Rita; Njunguna, Samuel
    The rapid increase in devices (mobile phones, computers, sensors, etc.) connected to the Internet (and thus to databases) has resulted in exponential growth in data generation and associated EDTs that can “identify patterns in observed data, build explanatory models, and make predictions quicker and with more accuracy than humans” (Pawelke et al., 2017) EDT/x-data-based applications and algorithms are mainly created in the developed countries and often lack transparency arising from intellectual property rights, thus hindering realization of the enormous potential EDT/x-data- based applications have in addressing socio-economic challenges faced by developing countries, including Kenya. Where applications exist, they are often not broadly accessible, especially for persons with disabilities, areas with slow internet connections or members of underrepresented groups. In this policy brief, the generic term “big data” is unpacked into four overlapping categories of data: big data, open data, user-generated data and real-time data, and are collectively referred to as “x-data”. EDTs are taken to include artificial intelligence (AI), blockchain, geographic information systems (GIS), the Internet of Things (IoT), and big data analytics. These methodologies are often used collectively. Gaps in support systems to develop EDT/x-data-based applications have created new digital divides between developing and developed countries. Further, barriers persist in the use and take-up of x-data by decision-makers, competing data sources, quality of data, limited aware- ness of data existence, and inadequate transformation of data into useful information or tailoring to match the decision-makers needs