4. Electronic Theses and Dissertations (ETDs) - Faculties submissions
Permanent URI for this communityhttps://hdl.handle.net/10539/37773
Browse
3 results
Search Results
Item Disruption of creative marketing supply chain due to generative artificial intelligence(University of the Witwatersrand, Johannesburg, 2022) Evertse, JustinThis study explores the transformative impact of generative AI on the creative marketing industry's supply chain process, focusing on the challenges and opportunities presented by AI in content creation. The advent of AI has revolutionised the way content is generated, leading to a paradigm shift in the creative landscape. The study investigated the implications of this shift for creativity, originality, and efficiency within the marketing and advertising sectors. Through a qualitative research methodology, including interviews with various industry stakeholders, this study delved into the nuances of AI's integration into creative processes and its effects on workflow, production, and distribution channels within the creative marketing industry. The research is grounded in theories of disruptive innovation and technology organisation enterprise theory, providing a theoretical framework to analyse the impact of AI technologies on traditional creative and marketing practices. It addresses crucial concerns such as the ownership of AI-generated content, the role of human creativity in the age of AI, and the ethical considerations surrounding AI in creative industries. The study's findings highlight a dual impact: AI as a tool for enhancing creative processes, enabling more efficient and diverse content generation, and AI as a disruptor, challenging traditional roles and workflows within the industry. Significantly, the study identifies a shift towards more collaborative models between AI and human creativity, suggesting that the future of the creative marketing industry lies in leveraging AI to augment human talent rather than replace it. This balance presents opportunities for innovation and new forms of content creation but also necessitates a re-evaluation of skill sets, job roles, and industry standards to adapt to an AI-integrated environment. The research underscores the importance of ethical guidelines and industry-wide discussions on the use of AI, advocating for policies that support creativity, protect intellectual property, and ensure fair competition in the evolving landscape. In conclusion, this qualitative research study provides an insightful analysis of the complex dynamics between generative AI and the creative marketing industry. It offers a comprehensive understanding of the challenges and opportunities AI presents, iii emphasising the need for a strategic approach to integrate AI technologies. By highlighting the potential for AI to augment human creativity and transform supply chain processes, the study contributes valuable perspectives to the ongoing discourse on the future of creativity and technology in marketing and advertisingItem Analyzing the performance and generalisability of incorporating SimCLR into Proximal Policy Optimization in procedurally generated environments(University of the Witwatersrand, Johannesburg, 2024) Gilbert, Nikhil; Rosman, BenjaminMultiple approaches to state representation learning have been shown to improve the performance of reinforcement learning agents substantially. When used in reinforcement learning, a known challenge in state representation learning is enabling an agent to represent environment states with similar characteristics in a manner that would allow said agent to comprehend it as such. We propose a novel algorithm that combines contrastive learning with reinforcement learning so that agents learn to group states by common physical characteristics and action preferences during training. We subsequently generalise these learnings to previously encountered environment obstacles. To enable a reinforcement learning agent to use contrastive learning within its environment interaction loop, we propose a state representation learning model that employs contrastive learning to group states using observations coupled with the action the agent chose within its current state. Our approach uses a combination of two algorithms that we augment to demonstrate the effectiveness of combining contrastive learning with reinforcement learning. The state representation model for contrastive learning is a Simple Framework for Contrastive Learning of Visual Representations (SimCLR) by Chen et al. [2020], which we amend to include action values from the chosen reinforcement learning environment. The policy gradient algorithm (PPO) is our chosen reinforcement learning approach for policy learning, which we combine with SimCLR to form our novel algorithm, Action Contrastive Policy Optimization (ACPO). When combining these augmented algorithms for contrastive reinforcement learning, our results show significant improvement in training performance and generalisation to unseen environment obstacles of similar structure (physical layout of interactive objects) and mechanics (the rules of physics and transition probabilities).Item Factors Influencing Artificial Intelligence Adoption in South African Organisations: A Technology, Organisation, Environment (TOE) Framework(University of the Witwatersrand, Johannesburg, 2023) Hoosen, Kaneez Fathima; Cohen, JasonArtificial intelligence (AI) refers to the formation of machines that mimic human intelligence and encompasses various technologies. AI technology is changing the landscape for South African organisations and how they operate. Using current literature and other online reports by auditing firms, the study aimed to identify a suite of AI technologies used by South African organisations. Technologies such as robotic process automation, image and speech recognition, machine learning and chatbots were defined. In addition, this research paper investigated the factors influencing AI technology adoption by South African organisations. The technology, organisation and environment factors of the TOE framework were examined to understand adoption decisions. It was important to close this gap as lack of understanding of how factors influence AI decisions, and an undefined suite of AI technologies could impact adoption decisions. A cross sectional relational research design was chosen for the study. A survey instrument was used and administered through a web-survey to 252 IT decision makers or IT leaders from South African organisations who served as key informants for their organisations. Responses were received from 55 organisations. Reliability and validity tests were used to evaluate the consistency and reliability of the data and to evaluate whether measures correctly represent the variables that they intend to measure. Correlation analysis, stepwise and multiple regression were used to test the hypotheses of the conceptual model. It was found that of the suite of AI technologies, robotics process automation followed by machine learning and image recognition had the highest levels of adoption. Results showed that data availability and top management support were supported as the most significant technology, organization, environment (TOE) factors influencing AI technology adoption in South African organisations. It was found that perceived technology benefits, IT infrastructure, resource capability and normative pressure were also strongly correlated to AI technology adoption. Financial resources and competitive pressure were not supported as determinants. Artificial intelligence is receiving much attention in both practice and research. This study addresses the gap in the current body of knowledge on AI adoption in South Africa by making use of the TOE framework to study adoption of artificial intelligence technologies in organisations. Useful insights are provided to South African organisations so that they can benchmark their adoption against other industry players and manage their response to those factors most significant for AI adoption