The True Meaning of Algorithm
Algorithm, machine learning, artificial intelligence, use case, generative AI, Large Language Model, it’s almost impossible to have a conversation these days without these terms coming up. We all know what they mean, right? Maybe, but let’s take a few minutes to be sure.
It’s almost impossible to have a workplace conversation these days without terms like AI, machine learning, and algorithm coming up. But the general public and social media aren’t always painting an accurate picture of the risks and capabilities of these technologies.
Our brains do funny things sometimes. They look for shortcuts to help us process all the information coming at us. One of these shortcuts is believing that we understand something better than we do. If you hear something often enough, you may begin to believe that you understand it. But if someone challenged you to define some common AI terms that you’re probably using all the time, could you?
As talent development professionals, we have an unprecedented opportunity to use these new tools to accelerate learning, support performance, and improve our own personal productivity.
And we also have a responsibility to become aware, knowledgeable, and proficient in the effective and ethical use of these powerful tools.
You may already be familiar with these terms at some level, but let’s take a few minutes to be sure we’re in agreement on the definitions we’ll be using throughout my Master Series on AI For Talent Development Professionals.
Let’s take that word “algorithm”, for example. In relation to AI, almost everyone is using it incorrectly. In social media, for example, algorithms help maintain order and assist in ranking search results and advertisements. Facebook and other social media use algorithms to prioritize content based on individual user behavior and preferences. But many people use the term as a synonym for AI. And that’s just wrong. It’s what some people call a misnomer.
An algorithm is a mathematical set of rules specifying how a group of data behaves. In social media, algorithms help maintain order and assist in ranking search results and advertisements. On Facebook and other social media, for example, an algorithm directs pages and content to display in a certain order or priority, often based on user behavior and preferences. Algorithms were used in the early days of AI to create many simple applications, such as menu-driven chatbots and online calculators. But they bear little resemblance to today’s AI. For this reason, many experts argue that referring to AI programs as “algorithms” is a misnomer. See this example of an algorithm for basic addition.
Algorithms imply fixed, step-by-step instructions, while many AIs leverage machine learning to evolve beyond programmed rules. AIs like neural networks develop their own internal models based on patterns in the data, rather than just executing predefined algorithms.
The word “algorithm” suggests rigidity, whereas AIs can be fluid, capable of generating novel solutions in response to changing inputs. The choices made by game-playing AIs for example, reflect likelihoods rather than certainties.
So, while the core logic of AIs can still be described as algorithmic in a broad, mathematical sense, many believe that term carries connotations that fail to capture the current and evolving capabilities of modern systems.
They propose terms like “learning models”, “decision systems” or simply “artificial intelligence” as more precise descriptors that better reflect the adaptive, dynamic and intricate nature of AI. That’s why we’re not going to be using “algorithm” incorrectly in my Master Series.
Still, you will likely encounter it elsewhere, and you need to be ready to think clearly about difference between claiming something is “algorithmic” in structure, meaning that it bears some similarity to a mathematical construct, and simply calling everything related to AI an “algorithm,” which is incorrect.
Let’s help set the standard for understanding AI. Because if Learning and Development professionals don’t get it right, what chance do we have?
