The Machine as an Autonomous Explanatory Agent
The holy grail of Artificial Intelligence (AI) is to transform the machine into an agent that can decide, make inferences, cluster the contents, predict, recommend, and exhibit similar higher cognitive faculties. The prowess of Large Language Models (LLMs) serves as evidence: they enable seamless na...
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| Format: | Article |
| Language: | English |
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Türk Felsefe Derneği
2024-07-01
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| Series: | Felsefe Dünyası |
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| Online Access: | https://dergipark.org.tr/tr/download/article-file/3944541 |
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| _version_ | 1832571128303845376 |
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| author | Dilek Yargan |
| author_facet | Dilek Yargan |
| author_sort | Dilek Yargan |
| collection | DOAJ |
| description | The holy grail of Artificial Intelligence (AI) is to transform the machine into an agent that can decide, make inferences, cluster the contents, predict, recommend, and exhibit similar higher cognitive faculties. The prowess of Large Language Models (LLMs) serves as evidence: they enable seamless natural language communication and widespread use across various fields by swiftly processing unstructured data and handling diverse datasets with agility. However, in order to be competent in the fields of science and industry, an agent with such capabilities must be reliable, i.e., accountable for its decisions and actions, which is a per se attribute of an autonomous agent. In this respect, this paper aims to determine whether state-of-the-art technologies have already created an autonomous explanatory agent or are paving the way for the machine to become an autonomous explanatory agent. To achieve this, the paper is structured as follows: The first part investigates the types and levels of explanations in explanation models, providing a foundation for understanding the nature of explanations in everyday life. The second part explores explanations in the context of artificial intelligence, focusing on types of explanatory systems in the research field of eXplainable AI (XAI). The third part delves into whether and to what extent the state-of-the-art machine learning models function as autonomous explanatory agents, based on the exploration in the second part and considering the field of Human-Computer Interaction. |
| format | Article |
| id | doaj-art-c2eef165300146e4aebd7b082bd62fa8 |
| institution | Kabale University |
| issn | 1301-0875 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | Türk Felsefe Derneği |
| record_format | Article |
| series | Felsefe Dünyası |
| spelling | doaj-art-c2eef165300146e4aebd7b082bd62fa82025-02-02T13:20:47ZengTürk Felsefe DerneğiFelsefe Dünyası1301-08752024-07-017926527910.58634/felsefedunyasi.14873762040The Machine as an Autonomous Explanatory AgentDilek Yargan0https://orcid.org/0000-0001-9618-6740Universität RostockThe holy grail of Artificial Intelligence (AI) is to transform the machine into an agent that can decide, make inferences, cluster the contents, predict, recommend, and exhibit similar higher cognitive faculties. The prowess of Large Language Models (LLMs) serves as evidence: they enable seamless natural language communication and widespread use across various fields by swiftly processing unstructured data and handling diverse datasets with agility. However, in order to be competent in the fields of science and industry, an agent with such capabilities must be reliable, i.e., accountable for its decisions and actions, which is a per se attribute of an autonomous agent. In this respect, this paper aims to determine whether state-of-the-art technologies have already created an autonomous explanatory agent or are paving the way for the machine to become an autonomous explanatory agent. To achieve this, the paper is structured as follows: The first part investigates the types and levels of explanations in explanation models, providing a foundation for understanding the nature of explanations in everyday life. The second part explores explanations in the context of artificial intelligence, focusing on types of explanatory systems in the research field of eXplainable AI (XAI). The third part delves into whether and to what extent the state-of-the-art machine learning models function as autonomous explanatory agents, based on the exploration in the second part and considering the field of Human-Computer Interaction.https://dergipark.org.tr/tr/download/article-file/3944541makine açıklamalarıotonom failxaiontolojiibeifemachine explanationautonomous agentxaiontologyhai |
| spellingShingle | Dilek Yargan The Machine as an Autonomous Explanatory Agent Felsefe Dünyası makine açıklamaları otonom fail xai ontoloji ibe ife machine explanation autonomous agent xai ontology hai |
| title | The Machine as an Autonomous Explanatory Agent |
| title_full | The Machine as an Autonomous Explanatory Agent |
| title_fullStr | The Machine as an Autonomous Explanatory Agent |
| title_full_unstemmed | The Machine as an Autonomous Explanatory Agent |
| title_short | The Machine as an Autonomous Explanatory Agent |
| title_sort | machine as an autonomous explanatory agent |
| topic | makine açıklamaları otonom fail xai ontoloji ibe ife machine explanation autonomous agent xai ontology hai |
| url | https://dergipark.org.tr/tr/download/article-file/3944541 |
| work_keys_str_mv | AT dilekyargan themachineasanautonomousexplanatoryagent AT dilekyargan machineasanautonomousexplanatoryagent |