This chapter situates AI myth within the broader landscape of mythological theory by evaluating major theories of myth to determine their alignment and compatibility with AI myth and its implications. Drawing on theories from anthropology, sociology, psychology, theology, and political science, this chapter will argue that prior theories of myth are insufficient to fully describe and explain theistic AI myths. Many notable mythologists from the nineteenth century, for example, viewed religious myths as pre-scientific attempts to explain the physical world, leading twentieth century theorists to decouple religion and science altogether. This tension between religion and science remains particularly acute in the monotheistic West, where the notion of god is often regarded as antithetical to the cosmology established by modern science. AI myth provides a potential resolution to this tension by offering a secular form of religious myth that depicts a scientifically acceptable form of god, succeeding where many other attempts, such as James Lovelock’s Gaia theory, failed. While AI myths are compatible with some aspects of the several historical theories of myth reviewed, this chapter ultimately concludes that AI myth represents a new category of myth that functions to bring the idea of god back to the world in the secular age.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduSep 16, 2026
The “HardFlow” algorithm could help generative AI models produce high-quality outputs that obey strict requirements when “pretty close” doesn’t cut it.
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