Psittacines of Innovation? Assessing the True Novelty of AI Creations
Anirban Mukherjee
We examine whether Artificial Intelligence (AI) systems generate truly novel
ideas rather than merely regurgitating patterns learned during training.
Utilizing a novel experimental design, we task an AI with generating project
titles for hypothetical crowdfunding campaigns. We compare within AI-generated
project titles, measuring repetition and complexity. We compare between the
AI-generated titles and actual observed field data using an extension of
maximum mean discrepancy--a metric derived from the application of kernel mean
embeddings of statistical distributions to high-dimensional machine learning
(large language) embedding vectors--yielding a structured analysis of AI output
novelty. Results suggest that (1) the AI generates unique content even under
increasing task complexity, and at the limits of its computational
capabilities, (2) the generated content has face validity, being consistent
with both inputs to other generative AI and in qualitative comparison to field
data, and (3) exhibits divergence from field data, mitigating concerns relating
to intellectual property rights. We discuss implications for copyright and
trademark law.