Philippe Boisnard
Online Projects
- Latent Flora — Structural Unconscious 1,100 AI-generated flowers, SAM segmentation, UMAP latent space walk
- Latent Art History — Computational Hermeneutics 1,000 artworks analyzed through CLIP semantic axes and SAM segmentation
- Art Explorer — Visual Search Interactive SAM + CLIP tool for visual similarity exploration
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Writings on AI & Art
- On the Structural Unconscious of Generative Models 2026 — Examining latent biases in image generation
- From Metrology to Hermeneutics: Beyond Manovich 2026 — CLIP as a tool for computational art criticism
- Segmentation as Interpretation 2025 — SAM and the cultural bias of computational perception
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Practice & Research
Philippe Boisnard is an artist, writer, and researcher working at the intersection of artificial intelligence, digital poetics, and computational aesthetics. His practice explores the latent structures embedded within generative models — the unconscious patterns, cultural biases, and perceptual habits that machines inherit from their training data.
His current work focuses on using segmentation models (SAM) and vision-language models (CLIP) as instruments of critical analysis, treating AI not as a creative tool but as a mirror that reveals the hidden architectures of visual culture. By walking through the latent spaces of generative and analytical models, his projects make visible what these systems have learned to see — and what they systematically fail to perceive.
His approach draws on a lineage that includes Lev Manovich's Cultural Analytics, extending computational art analysis from physical measurement to semantic interpretation. Where earlier methods measured brightness and saturation, Boisnard's work asks whether a painting is sacred or profane, ordered or chaotic — questions that only became computationally tractable with the emergence of multimodal AI models.