Whether it’s born in biology or forged in silicon, intelligence captivates us. So, why do we call one kind ‘Artificial’?

We hear “AI” just about everywhere these days, don’t we. It’s in the news, in our work, sometimes it feels like it’s in our coffee. But I’ve been thinking a lot about that first word, “Artificial”. Why that one, and what does it really mean for how we see this technology.

Looking Back, How The Name Came To Be

To really get it, we have to go back a bit, to the 1950s. The story usually starts around 1956, at a workshop at Dartmouth College. A group of researchers, pioneers really, got together. John McCarthy, who was a young mathematician then, is often credited with coining the term “Artificial Intelligence” for their project.

Their idea, a pretty bold one for the time, was to explore how machines could be made to simulate, or mimic, human intelligence. They were thinking about things like problem solving, language, learning. So, “Artificial” was used to make it clear, this was intelligence created by humans, man-made, not the biological kind we all have. It was about building something that acted intelligent.

What “Artificial” Meant Back Then

So, “Artificial” wasn’t really meant to be a judgment, like saying it was fake. It was more of a description. It meant crafted, or engineered, a human attempt to replicate aspects of our own thinking processes in machines. It helped define this new field of study, giving it a name and a focus. The goal was to see if intelligence, or at least intelligent behavior, could exist in something non-biological.

It was an aspirational term, really. It pointed towards a future where machines could do things that, up until then, only humans could.

But Times Have Changed, And So Has AI

Now, fast forward to today. The AI we’re seeing, it does more than just mimic basic routines. These systems can learn from huge amounts of data, they can generate new text, images, even code. They can adapt, and sometimes, as we’ve seen at NeuraXplore when developing things, they come up with solutions that surprise us.

This is where that word “Artificial” starts to feel a bit, well, less of a perfect fit for some of what we’re seeing. When a system genuinely learns and improves on its own, or creates something entirely new, calling it just a simulation, or artificial in the sense of being a lesser copy, it doesn’t quite capture the whole picture anymore, does it.

So, Does “Artificial” Still Tell The Full Story

This is where the conversation gets interesting for me. The original meaning, man-made, still applies of course. But the other shades of “artificial”, like imitation or second-rate, they can cloud how we see these advanced systems.

It’s not that the term is wrong, exactly. But maybe it’s not always the most helpful or complete description for every kind of system or every interaction we have with them.

Thinking About Other Ways to Describe It

That’s why you hear people, myself included sometimes, exploring other terms for specific contexts. Words like “Generative Intelligence” when we talk about creating new things, or “Evolving Intelligence” because these systems change.

At NeuraXplore, for example, with our PALS system that helps students learn, we often talk about “Symbiotic Intelligence”. We use this because it emphasizes the partnership, the way the technology works with the student, each helping the other grow. It feels more descriptive of that particular relationship. It’s not about replacing the original term AI, but adding more specific language where it helps understanding.

Why Our Words Matter So Much

How we talk about AI really does shape how people perceive it, and whether they’re open to using it. If the main word we use has any hint of being “fake” or “not real”, it can create distrust, or misunderstanding, especially when we’re talking about using AI in important areas like education or healthcare.

Clearer, more specific language might not solve all the challenges in AI, like bias or safety, but it can help us have more productive conversations about what we’re building and how it should be used.

Looking Ahead, As AI Keeps Growing

I think, as these technologies become even more integrated into our lives, our language will naturally evolve along with them. It’s happened before with other big technologies. We find new words, or old words take on new meanings, because our understanding deepens.

So, the discussion about “Artificial” in Artificial Intelligence is a healthy one to have. It shows the field is maturing.

And it brings us back to a really fundamental question, one we should keep asking,

“What kind of intelligence are we truly shaping, and what kind of relationship do we really want to have with it.”

(Just a quick note on process, I did use an AI assistant to help with some of the drafting for this piece, seemed fitting given the topic. The core thoughts, and the voice, are fully my own though.)