Sep 2026· Technophany, A Journal for Philosophy and Technology· 0 citations
Abstract
This paper examines how generative artificial intelligence reshapes creativity and meaning-making, proposing a shift from individual authorship toward collective, hybrid, and ritual forms of creation. Drawing on performative, process, communicative, and feminist-posthumanist perspectives, it argues that AI-driven creativity constitutes an “AI ritual”: a socio-technical process and performance where meaning emerges through interaction among humans, algorithms, and data. Rather than viewing creativity as a singular act rooted in human origin or agency, the paper conceptualizes it as a communicative performance involving a range of heterogeneous actors—code, datasets, users, and audiences. Echoing Barthes’ proclamation of the “death of the author,” it proclaims the “death of the creator,” reframing creativity as a dynamic, ritualized, and dialogical-collaborative process. Meaning is understood not predominenatly by its origin but by its circulation and transformation within a communicative collective. After reimagining creativity in the AI age as a shared, posthumanistic, and performative process of co-production, (re-)performance, and co-becoming that is more like love than control, the paper then explores some implications for aesthetics, AI research, and politics of art.
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
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
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
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 6, 2026
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.