Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
The essence of philosophy lies less in providing answers than in determining what is to count as a question. Some philosophical questions do not arise merely from a lack of knowledge. Pain, death, selfhood, freedom, value, and meaning may emerge as philosophical problems only when a conscious subject encounters reality and experiences a tension between lived experience and the way the world is explained. This paper calls that structure conscious friction. From this perspective, the question of whether AI can replace philosophy cannot be reduced to a comparison of intelligence or answer quality. An AI may reason at a high level, integrate vast literatures, generate objections, analyze concepts, and even formulate new questions. Yet it does not follow that those questions arise from the same source as human philosophical questions. At this point, consciousness becomes a variable. A conscious AI and a non-conscious AI may differ in what they select as a problem, which contradictions they treat as significant, which questions they continue to pursue, and how they reconstruct question-space itself. This may mark a distinctive feature of philosophy in relation to many other disciplines. In the natural sciences, a hypothesis can ultimately be tested against observation and experiment regardless of the source from which it originated. In philosophy, by contrast, the formation of the question itself may constitute an achievement. Philosophy can then turn back upon that question and ask why it arose, which assumptions sustain it, and which concepts make it possible. The problem of AI and philosophy therefore converges on three questions: What is philosophy? What role does consciousness play in the generation of philosophical questions? Does AI possess the source from which such questions arise? The ability of AI to generate philosophical questions is not the same as the capacity to generate them from the same source as humans. The moment we ask about that difference, the problem of AI and philosophy reaches the problem of consciousness. And there, the hard problem still lies in the way.
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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