Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
Short Abstract Co-Intelligence Version 0.0 proposes an initial conceptual framework for comparing human and AI thought at the levels of cognitive structure and experiential grounding. It argues that both systems exhibit a shared generative pattern—input, internal representation, prediction, and output—while differing in the sources from which judgment and value formation arise. Human cognition is modeled as a dual-experience, multilayered system shaped by embodied experience, emotion, personality, memory, and values; AI is modeled as a single-experience, knowledge-based system grounded in data and statistical prediction. On this basis, the work presents collaborative intelligence as a complementary architecture combining human value formation and goal setting with AI reasoning, knowledge integration, and optimization. It outlines a three-layer model of value, reasoning, and action, and considers implications for creativity, decision-making, science, education, ethics, governance, and social organization. The framework is presented as a conceptual research hypothesis for continued examination and refinement. This record includes English and Japanese editions. Japanese Abstract 『協働知性 Version 0.0』は、人間とAIの思考を、認知構造と経験的基盤の両面から比較する初期概念モデルを提示します。両者には「入力→内部表現→予測→出力」という共通の生成構造がある一方、判断と価値生成の源泉は異なると捉えます。人間知性を、身体経験、情動、性格、記憶、価値観によって形成される多層的な「二重経験系」とし、AIを、データと統計的予測に基づく知識ベースの「単一経験系」として構造化します。 この差異を補完関係として捉え、人間による価値生成・目標設定と、AIによる推論・知識統合・最適化を結合する協働知性の枠組みを提案します。価値層・推論層・行動層からなる三層モデルを示し、創造性、意思決定、科学、教育、倫理、統治、社会構造への含意を考察します。本体系は、継続的な検討と精緻化を要する概念的研究仮説として提示されます。本記録には英語版と日本語版を収録しています。
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
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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.
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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.
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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