Not All Artificial Intelligence Is Created Equal

Artificial Intelligence (AI) is everywhere - at least, that’s what we’re told. Every other day, there’s a headline about AI revolutionising industries or making our lives easier. But are we really talking about AI in its truest form?

When most people refer to AI today, what they’re really talking about is machine learning. And while machine learning is incredibly powerful, it’s just one piece of a much larger AI puzzle. So, why does this distinction matter?

Machine Learning vs. General AI

Machine learning is all about teaching computers to recognize patterns in data and make predictions based on that information. Think of it like a supercharged version of a child learning to read by recognizing letters, words, and sentences. It gets better with practice but is still limited by what it’s been trained on.

General AI, on the other hand, is what most people imagine when they hear the term “artificial intelligence.” It’s a type of AI that can understand, learn, and apply knowledge across a broad range of tasks—just like a human. We’re not there yet. Current AI systems don’t “think” the way humans do; they don’t have common sense or creativity. They excel at specific tasks but are far from replicating human intelligence in a general sense.

DeepMind founder Shane Legg has gone so far as to predict that we may see general AI by 2028, while Demis Hassabis, also of DeepMind, believes we’re just a few years away from achieving human-level AI. These predictions sound optimistic but I would certainly never consider arguing with either of those technology leviathans. and they certainly underline just how fast the field is moving and how seriously experts are taking the possibility.

​(Futurism)

​(The Independent)

Why Does This Matter?

Understanding the difference between machine learning and AI is important because it sets the right expectations. We’re seeing amazing advancements, but it’s essential to recognize the limitations. When companies or products advertise “AI,” they’re often referring to machine learning models, not general intelligence. This distinction helps manage hype and sets realistic goals for the future of automation and technology.

“Software is hard”

As I look at the business process management systems that we are working on today, I am reminded of a conversation I had many (many) years ago, while studying at a university in Germany. I was taking an assembler programming module—not because I had a passion for low-level code, but because it required far less knowledge of the German language!

I remember a friend, during one of our projects, lamenting, “Why can’t we just talk to the computer and tell it what we want, in human language? Like, ‘Play a tune!’ How hard is that to understand?” At the time, it was an amusingly facetious comment—computers were rigid, and you needed to speak their language, not the other way around. But it’s amazing how far we’ve come.

The recent advances in machine learning and natural language processing have brought us closer to that vision. Now, we can give simple commands and technology responds with increasing accuracy.

Human-Computer Interaction: Then and Now

Bill Gates once said that one of the most important developments in computing was the Human-Computer Interface (HCI), the tools that allowed humans to interact more naturally with machines. That evolution, from command lines to graphical interfaces, changed the landscape of technology. Recently, Gates has remarked that the advances in AI are comparable in significance to those early breakthroughs in HCI. The ability for machines to understand and process human language—or even anticipate our needs—could be the next transformative step for technology.

In the world of business process management, for instance, machine learning can analyse vast amounts of data to optimize processes and predict outcomes. However, for now at least, it still requires humans to guide and interpret those results. At Alboran Software we give the user the ability to provide that necessary guidance and receive the required results in their own language (even including German).

We use the term AI because that is what is generally understood, but this is Machine Learning and knowing the difference between the two can help businesses make better decisions on how to implement technology effectively, without getting lost in buzzwords.

So while the future of AI is bright, let’s make sure we’re clear on where we are today—and where we’re headed.