Please do not confuse AI with intelligence.

We tend to get confused by the word ‘intelligence’ in AI. That is because the word actually has two official meanings in almost all languages. Originally, ‘intelligence’ meant cognitive capacity, in other words cleverness. Later, in fields like security, business, and military strategy the term ‘intelligence’ was used to describe information. The word 'intelligence' in the phrase ‘artificial intelligence’ is rather confusing. This term with its two meanings are often used interchangeably in everyday language, but it often means two different things, causing misinterpretation. The CIA (Central Intelligence Agency), can be used as an example—it is an Intelligent Agency, yet in this context, it has nothing to do with being clever, it actually means an agency that collects and distributes data. In French the word intelligence is standard to indicate cleverness, but in the French army the word renseignement (information) is used when it comes to espionage or strategy. It is primarily used to describe specific facts, data, or details that someone has requested or provided, rather than abstract information. "Collecter de l'intelligence" sounds like collecting brain cells! 

Intelligence is used so widely for cleverness as well as for information that most people get confused, but the two words have two very distinct definitions.

The adage “Military intelligence is a contradiction in terms" is true in more ways than one. 

While the term "Artificial Intelligence" is widely used across the industry today, there are strong debates surrounding it.

It is a historic, universally recognised term established by early computer scientists. It captures just the goal of the technology: to simulate cognitive functions like learning, reasoning, and problem-solving. Information consists of raw data, facts, or figures that have been processed and organised to give them meaning. 

The term "Artificial Information Processing" describes, more accurately, the mechanics of the technology. At its core, modern machine learning is about ingesting massive datasets, recognising patterns, and calculating probabilities—information processing therefore—no real biological intelligence. Machine intelligence is modelled on the human left brain’s serial procedures and not on the subtleties of the right brain. Replicating mainly the human left brain functions, leaving out the right brain function, is not intelligence, it results in artificial information processing. 

While AI can perform complex tasks and improve efficiency, it lacks: genuine understanding and consciousness, self-awareness, and human intuition; concepts one would actually associate with intelligence. Which means mere information is not the same as the human brain’s intelligence, i.e. output of 2 hemispheres and not just one. If we humans were to use just our left brains, we would have been machines with no sense of the whole picture of reality. Which is exactly what AI is: mechanistic, reductionist, scientific, manipulated information, with NO holistic perspective, no whole truth, no complexity, no nuance, no courage, no magnanimity, no emotional intelligence, no social intelligence, no real ordinary intelligence, no original abstract thinking, no qualities that make life worth living like ethics and morals, no understanding of the information of life; just programmed to process data at high speed and then regurgitate so we can make sense of it. Information simply informs; it tells you what happened.

In summary:

AI’s strength lies in processing vast amounts of information quickly, but it often lacks the depth of human emotional and creative intelligence. Unfortunately, the term Artificial Intelligence anthropomorphizes the technology, making it sound like human intelligence, which leads to all sorts of complications. 

The term “Artificial Information Processing“ describes completely and literally what AI does; it computes, stores, retrieves, and synthesises data streams. 

Because of these nuances, industry experts often use sub-categories to be more specific and to avoid ambivalence and misunderstanding, such as Machine Learning, Natural Language Processing, and Data Pipelines.