Beyond the 'AI' Hype: A Call for More Precise Terminology

The debate over the term "Artificial Intelligence" is ongoing, as many feel it's a loaded and often misleading label. It has become a catch-all that obscures more than it clarifies, lumping vastly different technologies under one hyped-up umbrella. This ambiguity can set unrealistic expectations and hinder productive conversations.

The Argument for Specificity

For technical professionals and serious discussions, the most effective solution is to abandon the generic "AI" label in favor of precise terminology. Instead of saying you're "doing AI," it's far more informative to state what you're actually building or using. This could include:

• Large Language Models (LLMs)
• Support Vector Machines (SVMs)
• XGBoost
• Linear Regression
• Expert Systems
• Prolog Interpreters

Using specific terms grounds the conversation in reality. It immediately communicates the scope, capabilities, and limitations of the technology in question, moving the focus from science-fiction fantasy to practical engineering.

A New Name for Public Perception?

While specificity works well for experts, a single, evocative term will likely always dominate public perception. This has led to suggestions for alternative names that better capture the essence of the technology. One humorous yet insightful proposal is "Directed Stupidity."

This name cleverly contrasts the technology with general human intelligence. While our own intelligence can be broad and undirected, current AI is highly directed—it excels at executing specific tasks within a narrow domain. The "stupidity" part of the name points to its lack of common sense, general awareness, and its tendency to fail spectacularly when pushed outside its training data. It is a powerful tool, but one that operates without genuine understanding, making it a form of focused, yet ultimately limited, computation.