Artificial-Intelligence

TL;DR for operators Generative AI can produce a pile of plausible options before a human team has finished developing one. The harder question begins afterward: which option is actually interesting, which is merely competent, and when does an unexpected result deserve to change the direction of the work? Ivan Magrin-Chagnolleau’s Can an AI System Be Creative? A Critical Perspective from Art and Engineering1 makes that gap visible in a haiku exercise. Across six batches, the AI produced 60 poems that followed the requested form, yet repeatedly converged on probable structures and was weak at distinguishing its strongest outputs. The paper’s argument is therefore not simply that AI lacks novelty. It separates rapid generation from the harder capabilities of judging creative value and recognizing when an accident is significant enough to revise the original objective. ...

TL;DR for operators The fashionable question is whether an AI can infer what another agent believes, intends, or misunderstands. The more operational question is whether it should bother. Nikolos Gurney’s paper proposes a causal model for deciding when an artificial agent should engage theory-of-mind reasoning in conflict.1 Rather than treating mentalizing as an always-on capability, the model activates it when three conditions create enough pressure: information is unevenly distributed, an analytical solution is inaccessible, or the agent believes there is a meaningful mismatch between its own sophistication and its opponent’s. ...