Nameless Feeling

The vibes are off, but they’re off fundamentally because they focus only on feelings and emotional connections that have already existed. They don’t provide or imagine pathways to new futures; they allow only for an understanding of what feels good or bad based on experiences that have already happened, things that have already been seen.

Although the practical success of neural networks is still undeniable, it must be observed that their most powerful applications are in domains where rules are set in advance and don’t change over time — where the goals are clearly defined. The way you win a game of chess or Go is fixed and unambiguous. Protein folding is constrained by the laws of... See more

The vibes are off, but they’re off fundamentally because they focus only on feelings and emotional connections that have already existed. They don’t provide or imagine pathways to new futures; they allow only for an understanding of what feels good or bad based on experiences that have already happened, things that have already been seen.

In other w... 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

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

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

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

What the neural network “learns” is emergent rather than deduced. For example, it may notice a pattern that if it’s cloudy, then people are more likely to carry an umbrella. But it would not be able to explain that this is because cloudy implies rain and rain implies umbrella. Instead it effectively identifies a “rainy” vibe through correlations of... 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.”

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