Browsing by Subject "artificial intelligence"

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    Open Access
    COVID-19 Diagnosis: A Review of Rapid Antigen, RT-PCR and Artificial Intelligence Methods
    (Multidisciplinary Digital Publishing Institute, 2022-04-03) Aruleba, Raphael Taiwo; Adekiya, Tayo Alex; Ayawei, Nimibofa; Obaido, George; Aruleba, Kehinde; Mienye, Ibomoiye Domor; Aruleba, Idowu; Ogbuokiri, Blessing
    As of 27 December 2021, SARS-CoV-2 has infected over 278 million persons and caused 5.3 million deaths. Since the outbreak of COVID-19, different methods, from medical to artificial intelligence, have been used for its detection, diagnosis, and surveillance. Meanwhile, fast and efficient point-of-care (POC) testing and self-testing kits have become necessary in the fight against COVID-19 and to assist healthcare personnel and governments curb the spread of the virus. This paper presents a review of the various types of COVID-19 detection methods, diagnostic technologies, and surveillance approaches that have been used or proposed. The review provided in this article should be beneficial to researchers in this field and health policymakers at large.
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    Open Access
    Epistemic opacity: a feature not a bug: an exploration into the relationship between brains and ANNs
    (2025) Schoeman, Keldt; Nefdt, Ryan
    AI in the 21st century has come to be dominated by one school in particular, connectionism. And its successes are all around us – in the media we consume, in the music we listen to, in the cold calls we receive, etc. While this school was founded by psychologists, logicians, and philosophers with the goal of replicating human-level intelligence, the field has undergone a drastic transformation in recent years, entering a paradigm which is now dominated by engineering goals. Within this new paradigm, connectionism is no longer characterized as a field modelling the brain, but rather a mere engineering tool with incredible powers of pattern recognition. However, while the move to employ connectionist AI as a tool has led to remarkable successes in a variety of fields, it has also come with issues such as the black box problem, or epistemic opacity. Within a strictly engineering paradigm, attempts to explain the internal reasoning of these networks remain unsatisfying. Therefore, I propose recoupling connectionist networks with their roots in brain modelling, which would in turn open rich, new explanations for problems like epistemic opacity. Simply put, when we place the problem of opacity within the context of brain modelling, it appears that it may not be a problem at all, but an emergent feature of a complex system. In other words, we are beginning to have difficulty understanding modern connectionist networks in much the same manner we struggle to understand brains. Hence, it might well be feature, not a bug, that these systems should disappear into the mists of complexity.
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    Examining copyright infringement and liability in Generative Artificial Intelligence training and use: a legal perspective in South Africa and beyond
    (2025) Mikioni, Tendai; Ncube, Caroline
    Once again, humanity has welcomed technological advancement, this time around artificial intelligence, with mixed reactions. The creative industry is no exception to this rapidly evolving technology, with generative AI (genAI) deepening its claws in the creative industry. What lies within the fabric of genAI is a primary concern. In order to be trained (taught), genAI ingests enormous amounts of data, which is harvested indiscriminately. This is a cause for concern for those whose work is harvested and utilised without compensation, credit and consent. In addition, when genAI is deployed, the user's input prompts it to create works of their desires, ranging from images to musical lyrics. To that end, it remains to be answered whether the use of works for the purpose of training genAI and the generation of works by genAI trained using copyrighted works amount to copyright infringement. It is the duty of this dissertation to examine whether the South African copyright regime would deem it an infringement to make use of copyrighted works for training genAI. In addition, this dissertation goes further to examine whether there is a possibility of copyright infringement materialising when a user generates works through genAI. In this dissertation, the training of genAI will be referred to as the “input phase”, while the use of genAI by a user will be referred to as the “output phase.” In addition, the issue of who is liable when copyright infringement materialises will be analysed. In the end, the author submits recommendations for South Africa to address copyright infringement liability. Leading jurisdiction in copyright and AI regulations will be infused to enrich the discussion
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    Exploring the potential of mind joy as a Generative AI socratic tutor: fostering 21st-century skills in the general education certificate mathematics curriculum
    (2025) Van Der Merwe, Joricke; Gachago, Daniela; Gachago, Daniela
    This qualitative interpretive study explores how Artificial Intelligence (AI) has transformed many aspects of modern life, yet its potential within South Africa's General Education Certificate (GEC) Mathematics curriculum remains largely unexplored. This study aimed to explore the feasibility and potential of Mindjoy, a Socratic generative AI-enabled tutor, within the GEC Mathematics curriculum for Grade 9 learners. The focus was on understanding its impact in fostering key 21st-century skills: collaboration, communication, and critical thinking. This qualitative study is grounded in social constructivism with Laurillard's (2013a) conversational framework as an epistemological approach. Twenty-six learners were asked to complete online questionnaires on online platforms. Furthermore, the study made use of an AI-enabled Socratic learning environment, which exposed learners to two types of mathematics activities. On Day 2 of the study, 26 learners interacted with Mindjoy based on structured mathematical activities. On Day 3 of the study, 10 learners aim to solve problem-based learning activities in collaboration with Mindjoy. Data were coded through thematic inductive analysis using Discourse Analysis and Computational Grounded Theory approaches. Findings revealed that Mindjoy's ability to act as a Socratic tutor is impacted by the ability of the learners to prompt, as well as the type of mathematical activity that learners engage in. The findings highlighted that teachers need to be intentional about their choice of teaching approaches when implementing Socratic AI tutors in mathematics learning. The study illustrated that Socratic questioning delivered by Mindjoy showed potential for guided learning and elicitation; however, its full potential as an AI-powered pedagogical tool revealed limitations, especially during structured mathematical learning concepts. Recommendations include a pre- trained AI mathematics-specific tutor that will probe learners to think critically and help to maintain focus on the learning of mathematics. Additionally, it is recommended that a critical AI literacy framework be implemented to guide both teachers and learners in using AI in a useful, ethical, responsible, and respectful way. Reviewing the current GEC curriculum and assessment framework to include AI literacy as a 21st-century skill was also recommended.
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    How can emerging technology remedy the deficiency in robust enforcement mechanisms for digital copyright infringement within the South African music industry?
    (2025) Mushati, Julita; Ncube, Caroline
    This dissertation examines the deficiency in robust enforcement mechanisms for digital copyright infringement within the South African music industry, which constant technology developments have worsened. The emergence of technology has been a dual-edged sword for the music industry. While it empowers musicians to reach vast consumers, it simultaneously simplifies and accelerates unwarranted copying, access, and reproduction of copyrighted material. Consequently, protecting intellectual property rights has become strenuous due to the rapid increase of file-sharing systems, therefore, prompting a dire need for modernised solutions. The principal purpose of this research is to explore how emerging technologies can assist in reinforcing enforcement mechanisms and analyse the deficiencies in the South African music industry context. This study utilises a desk research method examining emerging technologies and their association with the enforcement of copyrights in the music industry. Technologies such as Digital audio watermarking, Blockchain technology, artificial intelligence, quantum computing, digital audio fingerprinting, and machine learning will be examined in this paper, and how these emerging technologies can potentially establish robust enforcement mechanisms. In particular, the findings of this research reveal the dual role of technology that enables digital copyright infringement, which presents a substantial threat to the protection of copyright, however, emerging technology can be tactically employed to address the enforcement challenges, and the need for Copyright laws to be at par with technological advancements. This dissertation provides informative and valuable awareness of the relationship between merging technology and enforcement mechanisms within the context of the South African music industry and how this relationship can foster an environment that upholds copyright works and affords musicians the recognition and financial incentive they deserve.
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    Leveraging blockchain and artificial intelligence for enhanced copyright enforcement in South Africa
    (2025) Mugauri, Joseph; Ncube, Caroline
    Leveraging Blockchain and Artificial Intelligence for Enhanced Copyright Enforcement in South Africa The Fourth Industrial Revolution has significantly impacted copyright enforcement, particularly in South Africa, where technological advancements have bolstered and challenged existing frameworks. This dissertation explores the integration of blockchain and artificial intelligence (AI) to enhance copyright enforcement mechanisms in the digital realm. Current enforcement methods face several challenges, including lengthy legal proceedings, slow legislative updates, circumvention techniques, accessibility concerns, non-compliance, and algorithmic bias. These issues necessitate innovative solutions to protect intellectual property rights effectively. With its decentralised and immutable nature, blockchain technology offers promising solutions for transparent and efficient copyright enforcement. Concurrently, AI technologies such as machine learning, deep learning, and adaptive algorithms can enhance the detection and prevention of copyright infringements, including brute force attacks, malware, phishing, and illegal streaming. This research delves into the potential of AI and blockchain for detecting infringing content, blockchain-based arbitration platforms for resolving disputes, and adaptive watermarking algorithms to trace the unauthorised use of digital content. The benefits of these technologies include increased efficiency, improved accuracy, transparency, and traceability. However, implementing these technologies poses challenges such as regulatory compliance, privacy concerns, ethical considerations, and associated costs. It recommends policy updates, ethical guidelines, and collaborative efforts to ensure blockchain and AI's responsible and effective use in copyright enforcement. By leveraging these technologies, South Africa can strengthen its copyright framework, fostering creativity and innovation while safeguarding intellectual property rights in the digital age.