Robot Rights, an approach appealing to Animal Rights Theory
- Authors: Millin, Murray David
- Date: 2021-10-29
- Subjects: Artificial intelligence , Singer, Peter, 1946- , Dennett, D C (Daniel Clement) , Animal rights , Ethics , Asimov, Isaac, 1920-1992 Criticism and interpretation , Asimov, Isaac, 1920-1992. Bicentennial man , Asimov, Isaac, 1920-1992. Sally , Preference utilitarianism
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10962/191854 , vital:45172
- Description: This thesis proposes that Peter Singer’s theory of preference utilitarianism, which is designed to be universally applicable to humans and animals, can be applied to robots of a particular kind — such as those seen in Isaac Asimov’s work. I shall do this by using Singer’s conception of interests as a framework, and appealing to Daniel Dennett’s intentional stance to deal with methodological issues about other minds. I shall then apply those theories to Isaac Asimov’s Sally and The Bicentennial Man. These two narratives show the importance of the intentional stance as an ethical tool and provide an example of how we might talk about the interests of a robot. Sally’s behaviour and ethical status is examined according to how she is perceived, and so I shall investigate how various persons engage with her and why they do so in those manners. This narrative demonstrates the value of the intentional and design stance as methods to approach other minds problems with regards to ethical status. The Bicentennial Man’s Andrew allows us to look for interests in a more concrete way. I look to see how he situates himself in his world, as well as investigate how and why he makes the demand to be morally considerable. This will be done by examining his creativity, personal development and drive for mortality throughout the narrative. , Thesis (MA) -- Faculty of Humanities, Philosophy, 2021
- Full Text:
- Date Issued: 2021-10-29
- Authors: Millin, Murray David
- Date: 2021-10-29
- Subjects: Artificial intelligence , Singer, Peter, 1946- , Dennett, D C (Daniel Clement) , Animal rights , Ethics , Asimov, Isaac, 1920-1992 Criticism and interpretation , Asimov, Isaac, 1920-1992. Bicentennial man , Asimov, Isaac, 1920-1992. Sally , Preference utilitarianism
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10962/191854 , vital:45172
- Description: This thesis proposes that Peter Singer’s theory of preference utilitarianism, which is designed to be universally applicable to humans and animals, can be applied to robots of a particular kind — such as those seen in Isaac Asimov’s work. I shall do this by using Singer’s conception of interests as a framework, and appealing to Daniel Dennett’s intentional stance to deal with methodological issues about other minds. I shall then apply those theories to Isaac Asimov’s Sally and The Bicentennial Man. These two narratives show the importance of the intentional stance as an ethical tool and provide an example of how we might talk about the interests of a robot. Sally’s behaviour and ethical status is examined according to how she is perceived, and so I shall investigate how various persons engage with her and why they do so in those manners. This narrative demonstrates the value of the intentional and design stance as methods to approach other minds problems with regards to ethical status. The Bicentennial Man’s Andrew allows us to look for interests in a more concrete way. I look to see how he situates himself in his world, as well as investigate how and why he makes the demand to be morally considerable. This will be done by examining his creativity, personal development and drive for mortality throughout the narrative. , Thesis (MA) -- Faculty of Humanities, Philosophy, 2021
- Full Text:
- Date Issued: 2021-10-29
The use of simulators and artificial intelligence in leadership feedback
- Authors: Ntombana, Sixolile
- Date: 2022-10-14
- Subjects: Artificial intelligence , Leadership , Employees Rating of , Communication in industrial relations , Qualitative reasoning Technological innovations , Chatbots
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/357685 , vital:64767
- Description: Leadership is a key factor in team success. For leadership to succeed, leaders need to possess the requisite competencies that can facilitate their performance. Team skills is identified as a leadership competency that is prioritised and most sought after by leaders. This follows studies that confirm that team skills are vital for leadership and team success. For leadership to develop team skills, feedback must be provided. Feedback is identified as information that is provided by an observer on a particular performance. The role of feedback in leadership development serves the purposes of engagement and self-reflection and evaluation of a leader’s performance. In this light, feedback cannot be separated from leadership as it is an essential part of communication in a leadership context. The nature and source of feedback can affect how the feedback is received, as shown by studies that suggest that the effectiveness of feedback goes beyond the content or nature (good/bad feedback) of the feedback. This study looks at two feedback sources: humans and artificial intelligence (AI) using students as the population. Humans have been the traditional source in feedback provision. Thus, in a team setting peers provide feedback on their peers’ performances. Unprecedented technological advancements have seen the improvement of AI capabilities to being able to give feedback. This has made AI a feedback source. Following these developments, this research assessed the way in which humans and AI provide feedback and the way in which students react to feedback provided by humans and AI. The research used chatbot AI, a Skills Simulator Assessment, launched by Kotlyar (2018). Students registered for Management One at Rhodes University in 2021 were the population for this research. The research was comprised of two phases where in phase one they were assessed by the Skill Simulator Assessment and in phase two they were assessed by their peers. This research found that students are not averse to feedback from AI, although they prefer peer feedback. It was further found that peer feedback tends to be tainted by lenience, while AI is not affected by lenience. This finding marked a significant development of AI in feedback provision. , Thesis (MCom) -- Faculty of Commerce, Management, 2022
- Full Text:
- Date Issued: 2022-10-14
- Authors: Ntombana, Sixolile
- Date: 2022-10-14
- Subjects: Artificial intelligence , Leadership , Employees Rating of , Communication in industrial relations , Qualitative reasoning Technological innovations , Chatbots
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/357685 , vital:64767
- Description: Leadership is a key factor in team success. For leadership to succeed, leaders need to possess the requisite competencies that can facilitate their performance. Team skills is identified as a leadership competency that is prioritised and most sought after by leaders. This follows studies that confirm that team skills are vital for leadership and team success. For leadership to develop team skills, feedback must be provided. Feedback is identified as information that is provided by an observer on a particular performance. The role of feedback in leadership development serves the purposes of engagement and self-reflection and evaluation of a leader’s performance. In this light, feedback cannot be separated from leadership as it is an essential part of communication in a leadership context. The nature and source of feedback can affect how the feedback is received, as shown by studies that suggest that the effectiveness of feedback goes beyond the content or nature (good/bad feedback) of the feedback. This study looks at two feedback sources: humans and artificial intelligence (AI) using students as the population. Humans have been the traditional source in feedback provision. Thus, in a team setting peers provide feedback on their peers’ performances. Unprecedented technological advancements have seen the improvement of AI capabilities to being able to give feedback. This has made AI a feedback source. Following these developments, this research assessed the way in which humans and AI provide feedback and the way in which students react to feedback provided by humans and AI. The research used chatbot AI, a Skills Simulator Assessment, launched by Kotlyar (2018). Students registered for Management One at Rhodes University in 2021 were the population for this research. The research was comprised of two phases where in phase one they were assessed by the Skill Simulator Assessment and in phase two they were assessed by their peers. This research found that students are not averse to feedback from AI, although they prefer peer feedback. It was further found that peer feedback tends to be tainted by lenience, while AI is not affected by lenience. This finding marked a significant development of AI in feedback provision. , Thesis (MCom) -- Faculty of Commerce, Management, 2022
- Full Text:
- Date Issued: 2022-10-14
A model for recommending related research papers: A natural language processing approach
- Authors: Van Heerden, Juandre Anton
- Date: 2022-04
- Subjects: Machine learning , Artificial intelligence
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10948/55668 , vital:53405
- Description: The volume of information generated lately has led to information overload, which has impacted researchers’ decision-making capabilities. Researchers have access to a variety of digital libraries to retrieve information. Digital libraries often offer access to a number of journal articles and books. Although digital libraries have search mechanisms it still takes much time to find related research papers. The main aim of this study was to develop a model that uses machine learning techniques to recommend related research papers. The conceptual model was informed by literature on recommender systems in other domains. Furthermore, a literature survey on machine learning techniques helped to identify candidate techniques that could be used. The model comprises four phases. These phases are completed twice, the first time for learning from the data and the second time when a recommendation is sought. The four phases are: (1) identify and remove stopwords, (2) stemming the data, (3) identify the topics for the model, and (4) measuring similarity between documents. The model is implemented and demonstrated using a prototype to recommend research papers using a natural language processing approach. The prototype underwent three iterations. The first iteration focused on understanding the problem domain by exploring how recommender systems and related techniques work. The second iteration focused on pre-processing techniques, topic modeling and similarity measures of two probability distributions. The third iteration focused on refining the prototype, and documenting the lessons learned throughout the process. Practical lessons were learned while finalising the model and constructing the prototype. These practical lessons should help to identify opportunities for future research. , Thesis (MIT) -- Faculty of Engineering the Built Environment and Technology, Information Technology, 2022
- Full Text:
- Date Issued: 2022-04
- Authors: Van Heerden, Juandre Anton
- Date: 2022-04
- Subjects: Machine learning , Artificial intelligence
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10948/55668 , vital:53405
- Description: The volume of information generated lately has led to information overload, which has impacted researchers’ decision-making capabilities. Researchers have access to a variety of digital libraries to retrieve information. Digital libraries often offer access to a number of journal articles and books. Although digital libraries have search mechanisms it still takes much time to find related research papers. The main aim of this study was to develop a model that uses machine learning techniques to recommend related research papers. The conceptual model was informed by literature on recommender systems in other domains. Furthermore, a literature survey on machine learning techniques helped to identify candidate techniques that could be used. The model comprises four phases. These phases are completed twice, the first time for learning from the data and the second time when a recommendation is sought. The four phases are: (1) identify and remove stopwords, (2) stemming the data, (3) identify the topics for the model, and (4) measuring similarity between documents. The model is implemented and demonstrated using a prototype to recommend research papers using a natural language processing approach. The prototype underwent three iterations. The first iteration focused on understanding the problem domain by exploring how recommender systems and related techniques work. The second iteration focused on pre-processing techniques, topic modeling and similarity measures of two probability distributions. The third iteration focused on refining the prototype, and documenting the lessons learned throughout the process. Practical lessons were learned while finalising the model and constructing the prototype. These practical lessons should help to identify opportunities for future research. , Thesis (MIT) -- Faculty of Engineering the Built Environment and Technology, Information Technology, 2022
- Full Text:
- Date Issued: 2022-04
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