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🇺🇦 #NowPlaying on KEXP's #MorningShow
Placebo:
🎵 Pure Morning
#Placebo
https://djvictor2.bandcamp.com/track/placebo-pure-morning
https://open.spotify.com/track/32F0zqsDgqpIEYApk2b8sr

Causes in neuron diagrams, and testing causal reasoning in Large Language Models. A glimpse of the future of philosophy?
Louis Vervoort, Vitaly Nikolaev
https://arxiv.org/abs/2506.14239

Causes in neuron diagrams, and testing causal reasoning in Large Language Models. A glimpse of the future of philosophy?
We propose a test for abstract causal reasoning in AI, based on scholarship in the philosophy of causation, in particular on the neuron diagrams popularized by D. Lewis. We illustrate the test on advanced Large Language Models (ChatGPT, DeepSeek and Gemini). Remarkably, these chatbots are already capable of correctly identifying causes in cases that are hotly debated in the literature. In order to assess the results of these LLMs and future dedicated AI, we propose a definition of cause in neur…

Was hat die Bundesregierung an "Nie wieder ist jetzt" nicht verstanden? (Omri Boehm)
https://www.sueddeutsche.de/muenchen/muenchen-omri-boehm-menschenrechte-holocaust-israel-li.3258161

Der Philosoph Omri Boehm spricht in München über Menschenrechte und den Nahostkonflikt
Die deutsche Lehre aus dem Holocaust dürfe nicht die bedingungslose Unterstützung israelischer Kampfhandlungen sein, sagt der Philosoph Omri Boehm bei einer SZ-Veranstaltung im Residenztheater.

Large Language Models for History, Philosophy, and Sociology of Science: Interpretive Uses, Methodological Challenges, and Critical Perspectives
Arno Simons, Michael Zichert, Adrian W\"uthrich
https://arxiv.org/abs/2506.12242

Large Language Models for History, Philosophy, and Sociology of Science: Interpretive Uses, Methodological Challenges, and Critical Perspectives
This paper explores the use of large language models (LLMs) as research tools in the history, philosophy, and sociology of science (HPSS). LLMs are remarkably effective at processing unstructured text and inferring meaning from context, offering new affordances that challenge long-standing divides between computational and interpretive methods. This raises both opportunities and challenges for HPSS, which emphasizes interpretive methodologies and understands meaning as context-dependent, ambigu…

Connecting dilaton thermal fluctuation with the Polyakov loop at finite temperature
Bing-Kai Sheng, Yong-Liang Ma
https://arxiv.org/abs/2506.13549 https://…

Connecting dilaton thermal fluctuation with the Polyakov loop at finite temperature
Understanding the character of the deconfinement phase transition is one of the fundamental challenges in particle physics. In this work, we derive a formula for the expectation value of the Polyakov loop -- the order parameter of the deconfinement phase transition -- in pure $\mathrm{SU(N_{\mathrm{c}})}$ gauge systems at finite temperatures starting from the Coleman\textendash Weinberg-type effective potential encoding the trace anomaly of QCD. Our results are in good agreement with the Lattic…

Ernst #Bloch - Konkrete Utopie in der Jetztwelt
".... the very desire to transcend the status quo supports the idea that the world is pregnant with unrealized possibilities (Bloch 1988: 16)"
1988, The Utopian Function of Art and Literature: Selected Essays, Jack Zipes and Frank Mecklenburg (trans.), Cambridge, MA: MIT Press

How Pete Carroll and his philosophy impacted a trio of NFL head coaches https://www.raiders.com/news/pete-carroll-coaching-tree-dan-quinn-dave-canales-brian-schottenheimer

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[1/7]:
A philosophical and ontological perspective on Artificial General Intelligence and the Metaverse

How Pete Carroll and his philosophy impacted a trio of NFL head coaches https://www.raiders.com/news/pete-carroll-coaching-tree-dan-quinn-dave-canales-brian-schottenheimer

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features
Miguel A. Lago, Ghada Zamzmi, Brandon Eich, Jana G. Delfino
https://arxiv.org/abs/2506.13917

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features
Explainability features are intended to provide insight into the internal mechanisms of an AI device, but there is a lack of evaluation techniques for assessing the quality of provided explanations. We propose a framework to assess and report explainable AI features. Our evaluation framework for AI explainability is based on four criteria: 1) Consistency quantifies the variability of explanations to similar inputs, 2) Plausibility estimates how close the explanation is to the ground truth, 3) F…