Morgane Billuart

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Morgane Billuart (b. 1997) is a writer and visual artist. She is an affiliated researcher at the Institute of Network Cultures and the New Center for Research and Practice, and co-hosts the GirlEmployee podcast with Carmen Lael Hines. Her work explores technological development, digital practices, and internet culture. In 2024, she published Cycles, the Sacred and the Doomed, and her second book, Becoming the Product, was released in May 2025. Morgane’s work has been presented at the Stedelijk Museum, Eye Museum, Cooper Union, and in Vienna, Amsterdam, Paris, and New York. Her writings have appeared in the Institute of Network Cultures, Do.Not.Research, Blank, and Lilith Magazine.



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(...)While many would argue that the fundamental “otherness” and “distance” offered by such apps and interfaces might be a limitation, it is relevant to reclaim how projected fascinations and desires for distant and absent objects of desire are key components of humans’ emotional build-up. As suggested in the book Mating in Captivity, in which Esther Perel studies the psychological tricks and downfalls of marriage and monogamy, she writes: “When people become fused—when two become one—connection can no longer happen. There is no one to connect with. Thus separateness is a precondition for connection: this is the essential paradox of intimacy and sex.”20 -“Otherness is a fact. You don’t need to cultivate separateness in the early stages of falling in love; you still are separate.You aim to overcome that separateness.”21...(more)
AI Chatbots as Psychoanalysts, or, ‘The Talking Cure’

(...) Addressing the ideological barriers of AI and therapy would be incomplete without acknowledging the contrasting paradigms in artificial intelligence, specifically in the realm of chatbot development, where Symbolic AI and Connectionist AI present divergent approaches. Symbolic AI relies on rule-based systems and explicit representation of knowledge through symbols and logic, excelling in tasks requiring logical deduction and rule-based decision-making. Symbolic AI chatbots are characterized by predefined rules and responses, offering transparency but potentially lacking adaptability to diverse and evolving contexts. In contrast, connectionist AI, grounded in neural networks and distributed representation, emphasizes learning from data and adapting to patterns. Connectionist AI chatbots leverage neural networks to process extensive datasets, enabling them to learn and generate responses based on intricate relationships within the data. While connectionist AI provides flexibility and proficiency in handling complex patterns, it may sacrifice the transparency associated with explicit rule-based systems. In an ideal practice, the approach would involve a hybrid model that combines both symbolic and connectionist methods, allowing chatbots to benefit from rule-based reasoning and adaptive learning, resulting in a more robust and versatile AI system. In Clemens Apprich's text, Secret Agents: A Psychoanalytic Critique of Artificial Intelligence and Machine Learning, the media theorist contends that the paradigm of "Good Old-Fashioned Artificial Intelligence" (GOFAI), grounded in a symbolic information-processing model of the mind, has recently been supplanted by neural-network models in the description and creation of intelligence.11 The shift is from a symbolic representation of the world to an emulation of the brain's structure in electronic form, where artificial neurons establish connections autonomously through a self-learning process. The contemporary AI paradigm is described as connectionist,12 as neural networks emulate “the somatic nerve system of animals.”...(more)