natural language processing

The words we publish and hold up for peer review remain the best representation of our brains at work in the digital world. A published paper is the best way to look closely at the foundational assumptions of LLMs. And those begin with pop culture.

Transformers take static vector embeddings, which assign single values to every token, and expand their context, nearly simultaneously as they process the context of every other word in the sentence. But who cares, let's listen to a pop song!

The first step in natural language processing is creating word-numbers, represented as points in space. If this confuses you, you're not alone. Keep reading.

Even in the face of "black box" algorithms, the history of artificial intelligence—natural language processing, more specifically—has left plenty of clues.

To put it another way: optimizing with GEO reverse engineering tactics is like entering a house through a small attic window. GEO ignores that the research frameworks literally embedded in the outputs of the model are the keys to the front door.

Perpetuated primarily by startups hungry for users and the entrepreneurial agencies and thought leaders who serve them, bad data begets worse expectations. Rapid rocketship visibility graphs imply that business results will follow—almost never the case long-term.