Machine Learning: What’s the Craze?
Welcome back to our little corner of the internet! Today we’ll discover and define Machine Learning. Let’s begin!
Machine Learning. You’ve probably heard of that before, and maybe you know what it is (or maybe you don’t). This particular article is for the latter, because today we’ll be looking at what Machine Learning really is. Even if you already know what Machine Learning is, I encourage you to read on. You may find something new and interesting!
What is Machine Learning?
For you to understand what Machine Learning really means, I’m going to give you an example. Let’s say you are developing your personal style in art. Chances are, if you’ve got absolutely no idea of what your personal style is, you’ll start by looking through the artwork of artists you like. And after you’ve analysed and understood techniques, tools and skills to help you replicate the style as accurately as you can, then you’ll have found your style.
Machine Learning works similarly. After analysing petabytes* and petabytes of data, it will have understood countless techniques and nuances in a personal style (let’s say, Van Gogh’s style) and it will then be able to create output which (in Generative AI applications like DALL•E) looks like Van Gogh’s drawings.
At the core of machine learning is the idea of patterns and analysing them. Computers are great at spotting patterns, even really complex ones (or ones that span over countless petabytes of data) that humans might miss.
So (as you know), to train the AI, you “feed” it a lot of data— let’s say pictures of owls and pigeons— and tell it which ones are owls and which ones are pigeons. The computer looks at these examples and starts noticing things that make an owl an owl or a pigeon a pigeon, like black eyes or a greyish colour.
But here’s where the magic comes in: the computer doesn’t just memorize the examples. It tries to come up with some general rules or equations that help it tell the difference between owls and pigeons, purely from analysing the patterns between each example. Then, when you show it a new picture, it applies those rules or equations to decide if it’s a pigeon or an owl.
And the more examples you give it, the better it gets at spotting those patterns. Soon, the can recognize owls and pigeons from all angles and even in adjusted lighting.
Sometimes, this process is called “training the model.” The “model” is like the brain of the computer — it’s what’s learning from the examples. And “training” is just the computer getting better at understanding and recognizing those patterns.
So, in a nutshell, machine learning is about teaching computers to learn from examples and figure things out on their own, using patterns and math. Just like training yourself to replicate someone else’s art style, but with a lot more calculations involved!
*1 Petabyte is around 1024 Terabytes, and 1000 Gigabytes is around 1 Terabyte. Bear in mind a lot of modern Macs have around 256 Gigabytes of storage space.
What’s the craze?
Well, this tech (put simply) allows us to unlock new possibilities. Machine learning is causing a craze because it can teach computers to learn from examples and make sense of massive amounts of data. This tech automates tasks, provides insights from data, and personalizes experiences like Netflix recommendations (its what allows the whole learns-as-you-go concept to function). It’s advancing healthcare with further improved accuracy in diagnosis and treatments, enabling autonomous systems like self-driving cars, communication with chatbots and even contributing to monitoring the environment! The excitement comes from its wide-ranging potential to solve problems and improve various aspects of our lives.
Are you interested in The Full History of AI? Then check out yesterday’s article!
I hope you enjoyed this article (I appreciate that this is more of a common length for my articles) and I will continue to post daily content for as long as I can. Thank you all so much for reading this (leave a clap, comment and follow if you enjoyed)!
See you in the next one!
