Last updated on December 20th, 2025 at 04:22 am
But the truth is, I would toss around AI and Machine Learning and have them used interchangeably the same thing. Everyone does it. You see a Netflix suggestion and you say it is AI. You have heard about ChatGPT and think that it is pure Machine Learning. I had the same problem, as well, and that irritated me to the extent of trying to find out what it was.
This is what I learned, in the simplest way possible.
Table of Contents
The Vision: AI is the Aim, ML is the technique.
So here’s the deal. The big concept is Artificial Intelligence or creating machines that can think, reason and solve problems just like humans. It’s the dream. Robotics to drive your home, self-driving cars and virtual assistants such as Siri. All of that falls under AI.
Machine Learning? That is one way of getting there. It is a particular methodology in which you input a large amount of data into a computer and it learns trends independently without you having to write every rule into a computer program. Move that way: AI will be training a robot to play chess and ML will be training it to watch 1 million games of chess until it learns how to win by itself.
I accidentally stumbled upon a straightforward analogy that made sense to me: rule-based systems (such as with AI: in case this occurs, do that) can be used, but with ML it is explicitly learned through experience.
Why It Gets Confusing (And Why I Got It Wrong)
Here’s where I tripped up. All the hype is given to Machine Learning since it happens to be the one that is working at the moment. Machine learning under the hood Construction of an email filter that is powered by AI, or a provider that detects fraud, is frequently being driven by Machine Learning.
However not every AI is Machine Learning. Other AI systems operate based on pre-established rules, and they learn nothing new. Imagine a program like an old-school chess playing one which adheres to coded instructions – it is AI, but not learning or getting better by itself.
I initially believed that it was only word games. Then I just knew that the difference does count when you are trying to know what such systems can or can not do.
How They Actually Work Differently
AI’s Approach:
- Is able to reason and solve problems.
- Rules, logic or learning (or a combination of the 3)
- Not only covers basic chatbots but includes, as well, advanced robotics.
Machine Learning’s Approach:
- Needs data – lots of it
- Discovers patterns that have not been programmed.
- Improves as things are repeated.
I tried this concept with one of the things that I use daily: Spotify recommendations. Spotify also has an AI named Machine Learning which monitors what I jump, replay and save. It learns my taste over time. However, it is the whole system, including playlist management, UI choice, or voice recognition, which is gained by the wider AI in conjunction with the ML.
Practical Cases and situations that worked out in my favor.
I borrowed some equipment so that I could compare the difference in action:
Pure Machine Learning:
- Spam filters teaching the algorithm that you consider this to be junk mail.
- Amazon product discovery based on your shopping behavior.
- Automatic typing on your cell phone keyboard.
AI (That Might Not Use ML):
- Decision tree spending customer service bots.
- GPS navigation computing the fastest path through algorithms.
- Scripted behavior game AI in older video games.
AI + Machine Learning Combined:
Self-driving vehicles (AI makes decisions concerning navigation, ML identifies pedestrians and road signs).
AI (osa vocation handles chores, speech recognition upgraded by ML)
This was the surprising fact: using the most basic rule-based AI, according to Google Cloud, it is possible to achieve AI functionality without large datasets, whereas in the case of Machine Learning, it is definitely required to train it with large volumes of data.
The Typologies Which Do Matter.
After undergoing the foundations, I needed to understand what they are. As it happens, AI has classifications such as Narrow AI (tasks, such as facial recognition), and the sci-fi one such as General AI (intelligence on a human level and everywhere – we have not yet even remotely reached it).
Machine Learning is divided into three major categories:
- Supervised Learning You give it examples, with the answers (as finished pictures: this is a cat, this is a dog).
- Unsupervised Learning – It self discovers patterns within unlabeled information (and classifies customers according to behaviour)
- Reinforcement Learning through it is a learning approach that works through trial and error, with rewards given on good actions (how robots learn to walk).
Appreciatively, the fact that Coursera mentions that in the real world today, the majority of applications are based on supervised learning is interesting as that is the most trusted approach when you have quality data.
What This Means to You (Why I Care Now).
It is more than academic to know the difference. When a person declares to be delivering something powered by AI, now you may ask: Does it learn and improve, or it tries to apply clever rules?
In case you would like to learn this stuff (I am considering it), begin with basics of machine learning. Programs such as Elements of AI have free classes that do not require you to be a programmer. I borrowed it out – it is freakishly available.
And, it is wise to know this when reading AI regulation or ethics news. Machine Learning systems are capable of assimilating bias based on the training data (never assumed this before). It is a ML specific problem, as opposed to one that similarly impacts rule based AI systems.
My Main Takeaway
Having digested all this, I have a basic concept model of AI that I am attempting to construct: AI. The way you are trying to build it is called Machine Learning, at least one of the powerful methods to do so.
This does not require a scholar. I wasn’t. However, now as I encounter a so-called smart functionality in an app, it can be assumed whether it trains with my actions or simply performs a set of logic that has been well-written.
And honestly? That’s pretty cool to know.
You are not alone in the event that you are still trying to get your mind around it. Trust me, as soon as it clicks, you will begin to notice the difference everywhere, you will wish the technology that you use on a daily basis were different and you will start thinking about it.
Read:
What is the Difference Between Deep Learning and Machine Learning
I’m a technology writer with a passion for AI and digital marketing. I create engaging and useful content that bridges the gap between complex technology concepts and digital technologies. My writing makes the process easy and curious. and encourage participation I continue to research innovation and technology. Let’s connect and talk technology!
