Nameless Feeling

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

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

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.”

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

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

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

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

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