The words artificial intelligence (AI), machine learning (ML), and algorithm are too often misused and misunderstood. They’re used interchangeably when they shouldn’t be. It adds unnecessary confusion in an already complex environment.
This is understandable to a degree. The definitions of any word or phrase linked to a new trend is bound to be somewhat fluid in its interpretation. However, AI, ML and algorithm are three terms that have been around for long enough to have a fixed meaning assigned to them.
As such, in an attempt to clear up all the misunderstanding and confusion, we sat down with Quinyx’s Berend Berendsen to once and for all explain the differences between AI, ML and algorithm.
What is an Algorithm?
An algorithm is any form of automated instruction. The majority of algorithms are simpler than most people think. Sometimes, they can be a single if → then statement. If this button is pressed, execute that action.
An algorithm can either be a sequence of simple if → then statements or a sequence of more complex mathematical equations. The complexity of an algorithm will depend on the complexity of each individual step it needs to execute, and on the sheer number of the steps the algorithm needs to execute.
What is Machine Learning?
Whereas algorithms are the building blocks that make up machine learning and artificial intelligence, there is a distinct difference between ML and AI, and it has to do with the data that serves as the input.
Machine learning is a set of algorithms that is fed with structured data in order to complete a task without being programmed how to do so. A credit card fraud detection algorithm is a good example of machine learning. Ever received a message asking if your credit card was used in a certain country for a certain amount? Thank machine learning for that.
The data that this particular algorithm receives is structured. Banks store data in a fixed format, where each transaction has a date, location, amount, etc. If the value for the location variable suddenly deviates from what the algorithm usually receives, it will alert you and stop the transaction from happening. It’s this type of structured data that we define as machine learning.
What is Artificial Intelligence?
Before we jump into what AI is, we have to mark that there is no clear separation between AI and ML. Machine learning is, in fact, a part of AI. However, we define Artificial intelligence as a set of algorithms that is able to cope with unforeseen circumstances. It differs from machine learning in that it can be fed unstructured data and still function.
One of the reasons why AI is often used interchangeably with ML is because it’s not always straightforward to know whether the underlying data is structured or unstructured. This is not so much about supervised and unsupervised learning (which is another article on its own), but about the way it’s formatted and presented to the AI algorithm.
A good example of extremely capable AI would be Boston Dynamic’s Atlas robot, which can physically navigate through the world while avoiding obstacles. It doesn’t know what it can encounter, but it still functions admirably well without structured data. The data here is much more complex than in the fraud detection example, because the variables are unknown. Still, each time the algorithm is activated and encounters an entirely new situation, it does what it should do without any human interference.
To summarize: algorithms are automated instructions and can be simple or complex, depending on how many layers deep the initial algorithm goes. Machine learning and artificial intelligence are both sets of algorithms, but differ depending on whether the data they receive is structured or unstructured.
We hope this adds some clarity to terms that are all too often used interchangeably. Understanding the difference between these definitions has certainly been of value to us, and we hope it can be valuable for you too.
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