Anthony Masure

Résumé

Deep learning artificial intelligence (AIs) works by comparing and aligning “raw” or “labeled” data sets. This mimetic paradigm (remix of “data sets”) makes it difficult to discern “generated” copy of original. Therefore, the question of acceptance of a world shaped by AIs arises less in terms of replacement than in terms of recovery: an environment in which one could not distinguish what is produced, or not, with AIs. Another challenge, with the demand of ecological sobriety, is the increase of algorithmic rationality with generated shapes optimized to structural constraints. What about social and aesthetic dimensions of shaping? How can AIs be open to invention and curiosity?