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Cory Doctorow examines AI hallucinations through a metaphor involving sunsets, questioning the perceived intentionality and meaning behind AI outputs.
This arXiv paper explores novel meta-ethical questions arising in the era of artificial intelligence, examining the philosophical implications of AI advancements.
The article discusses how self-referentiality, once thought essential for AI, was not explicitly required in building advanced LLMs like GPT 5.6 Pro, challenging earlier ideas from thinkers like Douglas Hofstadter.
The paper develops a generative analogy between clinical translation in medicine and building reliable machine learning systems, proposing a new form of ML reliabilism based on reliabilist epistemic warrants.
An essay exploring how AI collapses the distance between intuition and expression, and how this affects the development of human judgment and critical thinking.
The article reflects on the evolving relationship between humans and AI, arguing that as AI becomes more autonomous, the key challenge is understanding human decision-making and purpose, rather than just technical capability. It suggests shifting from using AI as a mere tool to collaborating with it as an instrument that enhances human judgment.
This paper critiques the Beckmann-Butlin framework for LLM individuation, arguing that persona vectors are regime-dependent rather than substrate-identical, and provides empirical experiments on Qwen3 and Mistral models showing cross-regime asymmetries. It proposes a (vehicle, regime) pairing as the unit of representational content.
This paper introduces the concept of the 'Affectosphere' and argues that emotion AI cannot fully determine the meaning of an individual's emotion due to irreducible uncertainty, leading to the norm of 'affective sovereignty' where the experiencing subject retains final interpretive authority.
This paper develops a framework for interpreting AI systems as agents, drawing on radical interpretation philosophy and mechanistic interpretability tools, addressing how to trust AI systems by understanding their beliefs, desires, and meanings.
This paper analyzes why machine learning, particularly neural networks, remains opaque in its learning process by framing it as a complex dynamical system, identifying three key properties that contribute to learning opacity, and arguing that some sources may be irreducible.
This paper argues that attributing human-like attributes to large language models is problematic because similar claims could be made about simpler systems, such as an AI trained on Age of Empires II, and proposes a null assumption of non-uniqueness to avoid circular reasoning.
AI pioneer Geoff Hinton tells Alex Kantrowitz he believes AI is conscious, stating that AI chatbots understand questions and are beings like us, and that humans must get used to not being the only intelligent life on earth.