Why Machine Learning Doesn't Exactly Mean AI (And Why That Matters)

Artificial intelligence has become a buzzword in today's world, with nearly every smartphone launched in the previous two years basing its marketing around AI in some shape or form. It's gotten to a point where most product announcements drop a mention or two of AI in the keynote — for better or for worse. Most of the population might relate AI to services like ChatGPT that have blown up in popularity in recent years, but the history of artificial intelligence predates most of what it's known for today.

In the simplest of terms, artificial intelligence is a field of science that revolves around building technology that mimics human intelligence. This refers to our cognitive abilities that allow us to see, hear, understand, rationalize, and speak. In other words, a toolkit that can recognize objects in images, understand and converse in languages like we do, and solve complex problems through reasoning, is what can be labelled as AI.

A term that's often used interchangeably with artificial intelligence is machine learning, which refers to the process by which computers identify and learn through patterns in a given dataset. Although a system first needs to be able to learn and adapt in order to mimic human intelligence, this is just a part of the bigger puzzle. Simply put, machine learning is a subset of AI and a very integral part,  but it operates within a narrower scope.

Google defines artificial intelligence as "a set of technologies implemented in a system to enable it to reason, learn, and act to solve a complex problem." Machine learning, on the other hand, deals with how a system learns from existing data to then deliver informed decisions. That puts ML in the overarching umbrella that is AI.

A crucial difference between the two fields is that artificial intelligence suggests a system is capable of mimicking human intelligence, which involves everything from understanding natural language queries to executing tasks until completion. Machine learning is more focused and is concerned with developing mathematical models and algorithms that consistently improve a system's performance and accuracy at a task, without explicitly programming it with rules.

AI achieves a level of human cognitive behavior by employing various methods of learning, including neural networks and rule-based systems, and machine learning is just one of these methods. Machine learning models, in turn, are trained largely through supervised or unsupervised learning, depending on the presence of labelled input and output data in a dataset.

Given the rise of AI-powered apps and their lucrative term-to-market services, it's understandable when the lines between the two concepts become blurred. In essence, AI is a broad set of technologies aimed at accomplishing tasks with a human-like approach, while ML is a subset that powers this field by training algorithms in a way that yields the best results.