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Introduction Artificial intelligence has rapidly become surrounded by a remarkably stable vocabulary. We are told that AI is inevitable, that algorithms know what we want, that data never lies, that machines will replace human workers, and that the future belongs to those who adapt. These expressions recur in newspaper headlines, corporate presentations, political speeches, and everyday conversation with such regularity that they have acquired the character of common sense. Public debate usually concerns whether they are true. Are algorithms genuinely objective? Can AI understand us? Will automation inevitably transform every profession? Roland Barthes approached questions of this kind differently. Rather than asking whether such statements were true or false, he asked why they appeared so self-evident in the first place. The shift may seem subtle, but it changes the object of inquiry. Instead of evaluating the factual accuracy of particular claims, Barthes directs our attention to...

Art is in the Air. AI art     “In all the arts there is a physical component which can no longer be considered or treated as it used to be (...). We must expect great innovations to transform the entire technique of the arts, thereby affecting artistic invention itself and perhaps even bringing about an amazing change in our very notion of art.” —Paul Valéry Abstract In 1928, Paul Valéry anticipated in La conquête de l’ubiquité a radical transformation of the material conditions of art, driven by the technical advances of his time. Almost one hundred years later, his intuitions strongly resonate in the age of artificial intelligence, where digital reproduction, global circulation, and algorithmic creation present new challenges and opportunities for art. This article revisits Valéry’s essay in light of contemporary technological developments, exploring the relevance of his diagnoses and their pertinence for understanding today’s aesthetics. Introduction In Pièces sur...

    Introduction Recommendation systems are specialized algorithms and software applications designed to suggest relevant resources to readers based on diverse data inputs. Widely used in academia, these platforms facilitate personalized suggestions within university libraries, academic journals, and research repositories. Their core functions include data collection, modeling user preferences, generating content suggestions, and continuously evaluating performance based on user feedback. Understanding Popularity Bias in Recommendation Systems Curation algorithms in pedagogical settings often exhibit popularity-driven bias, favoring material that is already well-known or frequently accessed. This tendency arises from analyzing historical user interactions to prioritize widely consumed content. The implications of such disproportionate visibility are multifaceted: Reinforcement Loop: Popular publications receive increased endorsement, driving higher reader engag...