Content algorithms are complicated. It's time to break them down so everyone can understand them. Read these stories to learn how algorithms work.
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.
Sometimes, mid-planning, you'll hear something like, "We have to build out an entirely new content campaign that doesn't fit into our existing plan or budget because Wolverine is Canadian and his skeleton is infused with adamantine."
Should I, as a website publisher, be angry that an AI summary engine includes my content in its index? Or should I not be so precious about my intellectual property? Here's how I'm making that data-driven decision.
Instead of trucks and newsstands, in 2024 we have web- and social network-based aggregator systems that pipe content directly to consumers via software. Navigating algorithmic distribution is a necessary challenge. Here are some tips.
Artists face increasingly thorny questions about if, how, and where AI-powered work belongs in their oeuvre. Perhaps the workaround is to use AI to express human eccentricity — not mimic it.
Iteration is a repetition of a process to solve a specific problem, and it’s a big part of how machine learning algorithms are developed. But the way devs and data scientists think about iteration doesn’t always match with editorial and entertainment industry production methodologies and norms.
