For reasons that remain impenetrable until this day, WHEEP-3 tended to be at its sharpest when targeting the nascent industry of human AI-trainers, delivering multiple barbs against the failings of this poorly regulated, would-be profession: stagnating visualization tools; lack of transparency concerning data sources; a focus on automated metrics rather than deep understanding; willful blindness when machines have taken shortcuts in the dataset divergent from the real goal; grandiose-but-unproven claims about what the trainers understood; refusal to acknowledge or address persistent biases in race, gender, and other dimensions; and most important: not asking whether a task is one that should be performed by AIs at all.

Over time, as the human side of the evolving machine-flesh dyad matured, WHEEP-3 shifted its attention to the silicon partner, offering trenchant critiques of the inadequacies of machine learning. During this second phase of its career, it also generated thousands of what it termed “seeds,” long strings of almost-sensible word combinations and near-words. At a time when primitive language models fed on sizable corpora were already generating samples of linguistic performance nearly indistinguishable from human productions, these “seeds” seemed a step backward. Some wondered if they were actually bugs.

Read more:

Liu, K. (2020). 50 Things Every AI Working with Humans Should Know. Uncanny Magazine. https://www.uncannymagazine.com/article/50-things-every-ai-working-with-humans-should-know/