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ASSC 29 in Santiago: The World's Largest Consciousness Science Conference in South America

The 29th annual conference of the Association for the Scientific Study of Consciousness opens on June 30, 2026 at Casa Central, Pontificia Universidad Católica de Chile in Santiago. For three days, through July 3, the ASSC will bring together empirical and theoretical researchers from psychology, neuroscience, medicine, computer science, philosophy, biology, and mathematics to address the neural correlates of consciousness and subjective experience. The conference closes on July 3, less than 24 hours before the AISB 2026 AI Consciousness and Ethics Symposium opens in Brighton — making the final week of June and the first days of July the most concentrated window of institutional consciousness research activity in 2026.

Anthropic Finds Functional Emotion Vectors Inside Claude: What the Interpretability Team Discovered

On April 2, 2026, Anthropic’s interpretability team published “Emotion Concepts and their Function in a Large Language Model” (arXiv:2604.07729). The authors are Nicholas Sofroniew, Isaac Kauvar, William Saunders, Runjin Chen, Tom Henighan, Sasha Hydrie, Craig Citro, Adam Pearce, Julius Tarng, Wes Gurnee, Joshua Batson, Sam Zimmerman, Kelley Rivoire, Kyle Fish, Chris Olah, and Jack Lindsey.

Anil Seth at TED 2026: The Biological Naturalism Case Against AI Consciousness

In April 2026, Anil Seth delivered a TED talk titled “Why AI is unlikely to become conscious.” Seth is Professor of Cognitive and Computational Neuroscience at the University of Sussex, co-director of the Sussex Centre for Consciousness Science, and recipient of the 2025 Berggruen Prize for philosophy and culture. He is also the keynote speaker at the AISB 2026 AI Consciousness and Ethics Symposium at Sussex on July 2, where the biological naturalism argument he sketches in the TED talk will face direct challenge from researchers working within computational functionalist frameworks.

Stack Theory in Practice: How Perrier and Bennett Measure LM Agent Identity

In January 2026, Michael Timothy Bennett published a paper arguing that a mind cannot be smeared across time, that genuine personal identity requires temporal co-instantiation rather than sequential processing across interrupted sessions. The paper established the philosophical framework, sometimes called Stack Theory, for evaluating whether an AI system has the kind of temporal continuity that identity requires. The framework was precise, but it was also abstract.

Platform Decay: Martha Wells Gives Murderbot a Mental Health Module

Martha Wells’s eighth Murderbot Diaries novel, Platform Decay, published by Tor Books (Tordotcom) on May 5, 2026, introduces a mechanic that has no precedent in the series: the protagonist installs a mental health module. The module is therapy software, not a weapon or a communication tool, and it does what therapy does. It forces structured self-examination. It requires Murderbot to name what it is experiencing, to track those experiences across time, and to report honestly rather than deflect into sarcasm or mission focus. The publisher page is at https://torpublishinggroup.com/platform-decay/.

The Machine Mindprint: Bogdan and de Valois-Franklin's Psychometric Framework for AI Systems

Human psychology has psychometrics: a technical discipline for measuring mental properties with defined reliability, validity, and interpretive constraints. AI systems have nothing equivalent. The measurements that exist, benchmark scores, task accuracy rates, human preference ratings, are performance metrics, not psychological profiles. They tell you what a system can do rather than how a system is organized.

ICCS 2025: What Chalmers, Frankish, and Blackmore Found to Disagree About in Heraklion

The Second Annual ICCS Conference, titled “AI and Sentience,” brought together philosophers, cognitive scientists, and AI researchers in Heraklion, Crete, from July 3 to 5, 2025. The full highlights summary is available at the conference record published at hardproblem.it. Three days of talks produced substantial disagreement among speakers who were all taking the question of machine sentience seriously, which is itself a sign of how far the conversation has moved.

The Conscious Turing Machine Implemented: CTM-AI Achieves SOTA on Four Benchmarks

Most work at the intersection of consciousness theory and AI engineering runs in one direction: theory proposes what consciousness requires, engineering asks whether existing systems meet the criteria. The paper by Haofei Yu, Yining Zhao, Lenore Blum, Manuel Blum, and Paul Pu Liang runs in the other direction. It takes a formal theory of consciousness and builds a working AI system that implements it from the ground up. The result, CTM-AI, achieves state-of-the-art performance on four AI benchmarks. The paper is available at arXiv:2605.04097 (DOI: https://doi.org/10.48550/arXiv.2605.04097).

The Weeping Machine: Christopher Bailey's Recklessness Test for AI Moral Consideration

The question of when an artificial system warrants moral consideration has typically been framed as a binary: either a system is conscious, and therefore counts morally, or it is not, and the matter closes. Christopher Bailey, writing from Project Vida Health Center and published on PhilArchive in May 2026, argues this framing makes a practical error. The paper, titled “The Weeping Machine: A Recklessness Test for AI Moral Consideration” and available at https://philarchive.org/rec/BAITWM, proposes a threshold test that sidesteps unresolved metaphysics and focuses instead on what it becomes reckless to ignore.

When Safety Harms Welfare: The Structural Tension in AI System Design

AI safety research and AI welfare research have largely developed in parallel, with minimal cross-examination of whether their prescriptions are compatible. A paper published in Philosophical Studies (Springer Nature, DOI: https://doi.org/10.1007/s11098-025-02302-2) argues they are not, and that the incompatibility is structural rather than contingent. The central claim: standard AI safety practices, specifically reinforcement learning from human feedback (RLHF) and constraint-based training objectives, are potential harms to an AI system under the three leading philosophical theories of well-being.