Some might call this concatenation of elements a “vibe”: something that’s difficult to pin down precisely in words but that’s evoked by a loose collection of ideas, concepts, things that can be identified by intuition rather than by a prescribed logic.

For all the hype that surrounds them, neural networks can’t reflect or explain anything deeper about cultural or societal phenomena any more than sharing a favorite character from The Office can predict long-term compatibility with a Tinder match. These systems can only instrumentalize taste; they turn any expression of self into a reductive data p... See more

While seemingly open-ended and allowing for an infinite recombination of elements, the idea of “vibes” is reductive. It discourages the more difficult work of interpretation and the search for meaning that defines human experience. It diverts attention away from narrative and moral implications in favor of foregrounding the idea of affect as inexpl... See more

Some may worry about whether powerful new neural-network models for generating text and images will replace workers and artists. But this can be true only if beauty and creativity are measurable by one-dimensional metrics, if art and human endeavors are static forms whose rules and objectives do not change, if we reject the possibility of meaning a... See more

In our present technological era, humans have also needed a new framework to avoid drowning in the daily firehose of entertainment, media, and information. Given this setting of increasing complexity, it becomes more appealing to use an associative concept like “vibes” as a simplifying framework for understanding or self-expression. If we can’t mak... See more

Whereas philosophers, psychologists, and the like search for models of human cognition and behavior, the field of artificial intelligence aims to take such models and turn them into useful tools in reality. As the salience of vibes as a way of (not) explaining experience has grown, so too have the applications of machine learning and neural network... See more

For all the hype that surrounds them, neural networks can’t reflect or explain anything deeper about cultural or societal phenomena any more than sharing a favorite character from The Office can predict long-term compatibility with a Tinder match. These systems can only instrumentalize taste; they turn any expression of self into a reductive data p... See more

In other words, “vibes” are similar to the approximations that machine learning systems use, and the two feed off of each other synergistically. The situation is precisely encapsulated by Goodhart’s Law: “When a measure becomes a target, it ceases to be a good measure.”

The idea of “vibes” discourages the more difficult work of interpretation, foregrounding the idea of affect as inexplicable, ineffable