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What’s The Difference Between AI, ML, and Algorithms?

All you need to know about AI and ML in Workforce Management.

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 technology 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.

Here's what Berend had to say:

What is an Algorithm?

An algorithm is a sequence of instructions for solving a problem, which can range from simple to highly complex. In workforce management software such as Quinyx, algorithms are essential for automating and optimizing tasks like scheduling and shift management.

What is Machine Learning?

Machine learning involves systems using structured data to perform tasks without explicit programming. In the realm of workforce management software, ML algorithms in Quinyx can predict scheduling needs and optimize shift management by analyzing historical data, helping businesses streamline operations.


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What is Artificial Intelligence?

Artificial intelligence systems can handle unstructured data and adapt to new information, making them versatile and powerful. For workforce management, AI in Quinyx’s software enables advanced features like demand forecasting and real-time labor optimization, ensuring businesses can meet dynamic workforce requirements efficiently.

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.

I 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!

Learn more about how Quinyx has optimized scheduling and forecasting with the help of AI.

Or download our guide "All you need to know about workforce management and AI".

Download our AI guide

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