Recently I have had reason to try to learn a bit about a type of AI (artificial intelligence) called machine learning. It turns out that sometimes a researcher in this area will encounter the Helvetica scenario, which is a phenomenon where a machine learning model gradually degrades due to errors coming from uncurated training on the outputs of another model, including prior versions of itself. The Helvetica scenario, also called “Model Collapse” (Wikipedia article) is a common problem when training generative adversarial networks (GANs) (Machine Learning Glossary). The natural question for the reader is where this terminology came from?
The first use of this term in the context of generative AI is the seminal paper Generative Adversarial Nets (Goodfellow et al., Advances in Neural Information Processing Systems 27 (2014) (arxiv.org). As it turns out, the terminology was coined in the pilot episode (Youtube) of the 2002 BBC television series Look Around You (Wikipedia article). The Simpsons’ creator Matt Groening called it “one of the funniest shows I’ve ever seen”. I recommend this show to everyone.
Thank you – I didn’t know that this scenario (which is now receiving well-deserved, if belated, attention) had a catchy name. You are a, er, font of wisdom. Here’s the clip: https://www.youtube.com/watch?v=t4CRCJUmWsM&t=353s