What are the differences between algorithms, automation and artificial intelligence?

These days, it’s almost impossible to talk about any technology-related topic without mentioning one of the following three terms: algorithms, automation and artificial intelligence. Whether the conversation is about industrial software development (where algorithms are key), DevOps (which is entirely about automation), or AIOps (the use of artificial intelligence to power IT operations), you’ll encounter these modern tech buzzwords.

In fact, the frequency with which these terms appear and the many overlapping use cases to which they are applied make it easy to conflate them. For example, we might think that every algorithm is a form of AI, or that the only way to automate is to apply AI to it.

The reality is much more complex. While algorithms, automation, and AI are all related, they are distinctly different concepts, and it would be a mistake to conflate them. Today, we’re going to break down what these terms mean, how they differ, and where they intersect in the modern technology landscape.


What is an algorithm:

Let’s start with a term that has been bandied about in technical circles for decades: algorithm.

An algorithm is a set of procedures. In software development, an algorithm usually takes the form of a series of commands or operations that a programme performs to accomplish a given task.


That said, not all algorithms are software. For example, you could say that a recipe is an algorithm because it is also a set of programmes. In fact, the word algorithm has a long history, dating back centuries before anyone ta


What is automation:

Automation means performing tasks with limited human input or supervision. Humans may set up the tools and processes to perform automated tasks, but once initiated, automated workflows will run largely or entirely on their own.
Like algorithms, the concept of automation has been around for centuries. In the early days of the computer age, automation was not a central focus of tasks such as software development. But over the past decade or so, the idea that programmers and IT operations teams should automate as much of their work as possible has become widespread.
Today, automation goes hand-in-hand with practices like DevOps and continuous delivery.



What is Artificial Intelligence:

Artificial intelligence (AI) is the simulation of human intelligence by computers or other non-human tools.

Generative AI, which generates written or visual content that mimics the work of real people, has been at the centre of AI discussions for the past year or so. However, generative AI is only one of many types of AI in existence, and most other forms of AI (e.g., predictive analytics)

existed long before the launch of ChatGPT sparked the current AI boom.

Teach the difference between algorithms, automation, and AI:

Algorithms vs. automation and AI:

We can write an algorithm that is completely unrelated to automation or AI. For example, an algorithm in a software application that authenticates a user based on a username and password uses a specific set of procedures to complete the task (which makes it an algorithm), but it is not a form of automation, and it is certainly not AI.

Automation vs. AI:

Similarly, many of the processes that software developers and ITOps teams automate are not a form of AI. For example, CI/CD pipelines often contain many automated workflows, but they do not rely on AI to automate processes. They use simple rule-based processes.

AI with automation and algorithms:

Meanwhile, AI often relies on algorithms to help mimic human intelligence, and in many cases, AI aims to automate tasks or make decisions. But again, not all algorithms or automation are related to AI.



How the three come together:

That said, the reason why algorithms, automation, and AI are so important to modern technology is that using them together is key to some of today’s hottest technology trends.

The best example of this is generative AI tools, which rely on algorithms trained to mimic human content production. When deployed, generative AI software can generate content automatically.

Algorithms, automation and AI can converge in other contexts as well. For example, NoOps (fully automated IT operations workflows that no longer require human labour) may require not only algorithmic automation, but also sophisticated AI tools to enable complex, context-based decision-making that cannot be achieved by algorithms alone.

Algorithms, automation and AI are at the heart of today’s technology world. But not all modern technologies rely on these three concepts. To accurately understand how a technology works, we need to know the role that algorithms, automation and AI play (or don’t play) in it.


Post time: May-16-2024