Machine learning is the acquisition, processing and sharing of information using data sets and basic algorithms. But how does it really work?
Machine learning is transforming the way companies like yours do business around the world, especially in the realm of e-commerce.
At the highest level, machine learning is the use and development of computer systems that learn and adapt without following explicit instructions. They do this by using algorithms and statistical models to analyze and draw inferences from patterns in data.
The data sets used to train machines for the purpose of artificial intelligence (AI) are often referred to as experiences. These experiences aren’t unlike how we learn, beginning the day we’re born. Our past experiences help us learn and better process future experiences.
A basic set of algorithms gives the machine the ability to compare different objects or phrases (speech or text), much like we use our five senses to learn.
There are three main types of machine learning: supervised, unsupervised, and reinforcement learning.
We’re going to talk about them in relation to TurtleID, a hypothetical program that identifies turtles in images.
Supervised Machine Learning
Let’s think of a children’s book with animal photos. As you turn the pages over, you see a picture of an animal and a caption that lists the name of the animal.
Seeing this picture for the first time and identifying the caption “turtle,” a child will form the initial connection between what a turtle looks like and what it is called.
A few books later, and perhaps supported with other real-life interactions where a child is exposed to turtles of all shapes and sizes, the connection between the form of the animal and the name will solidify in the brain.
Supervised machine learning mimics this approach – we give the machine both the input and the resulting output.
Think of this way: as a child you see a picture of the turtle (the input) with a caption that says “turtle” (the output).
For the TurtleID dataset, we would have a large set of turtle pictures and the output we give it for each picture is “this is a turtle.” Now TurtleID has learned what a turtle looks like. For a computer, the turtles appearance my be calculated by using preset parameters such as length, width, color, etc.
TurtleID therefore uses “past experiences” (a.k.a. the dataset) to try to predict whether a picture is or isn’t a turtle.
Again, this works much like a child learning what a turtle is and using the five senses to identify turtles based on past experiences.
Unsupervised Machine Learning
Though unsupervised machine learning is underutilized, it provides a more exciting path for the future of machine learning.
Unsupervised machine learning is when you give the input and allow the machine to identify its own output. This means it creates different clusters based on similarities within the data.
When you were too young to understand full words, you did the same thing. You could identify a turtle, place it in a group of “turtle like things,” but you never really knew what it was until you could learn the word “turtle.”
So, if we give TurtleID 20 pictures of turtles, it will notice similarities between those pictures and classify the dataset in its own language, since it doesn’t know the word “turtle.” Let’s call that dataset A1.
Now we give it 20 more pictures consisting of turtles, cars and the ocean. It classifies the cars as A2, the ocean as A3, and puts the turtles under the original A1.
The interesting part is that the machine just created new words in its own dictionary, and – just like you as a child – it won’t call A1 “turtle” unless it is told to do so.
The exciting part about unsupervised machine learning is its ability to think differently. Let’s imagine we gave this machine a set of data with animals ranging from different eras, and each animal is classified by the 26 letters of the alphabet.
Unsupervised machine learning can use its clustering approach to connect things that humans may not think of. It can identify animal X as the cousin to animal B due to similar size of snout, even though it was never thought of by humans.
The possibilities for unsupervised machine learning are endless. It makes discoveries and connections in places that we would never see due to our restricted knowledge of input with a definite output.
Unsupervised learning is given the space for curiosity to discover the output on its own.
Reinforcement machine learning is a mix of supervised and unsupervised.
For example, let’s say we gave TurtleID three pictures: one of a turtle, one of a dog, and one of a horse. We tell it that the first picture is “a turtle” while the next two are “not a turtle.” Since we gave it these labels, it can use its algorithms to place the unlabeled data it is given into the specific dataset by comparing characteristics in the image.
Now we give it a bunch of unlabeled data, meaning it doesn’t know if it is ‘a turtle’ or is ‘not a turtle’ – it just sees inputs. The algorithm used for both the supervised and unsupervised sides will be like the machine’s eyes, allowing it to discern shapes and colors.
It will use the algorithm to say, “Oh, this unlabeled image’s shapes and colors are like this group of labeled image’s shapes and colors. Therefore, I will place it in the similar category.”
This method is desirable for large datasets. You don’t want to spend all your time labeling data to get insights, but you also want to ensure that the machine can assign appropriate labels to each element of the dataset.
Start Incorporating These Tools
Now that you have a better idea of how it works, it’s time to reap the benefits of machine learning.
Companies of all sizes are creating and investing in AI and machine learning solutions.
We can tailor language solutions to meet your specific objectives and help your business grow in all your target markets. We can also collect the data to train your machine learning model.
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