What Inference Means in Artificial Intelligence (And Why You Should Use It)

Inference is one of countless “new” words that have entered the mainstream as the popularity of artificial intelligence (AI) has exploded in recent years. “Inference,” unlike many other tech buzzwords, represents something concrete that brings real benefits to real-world applications every moment of every day. Or, put simply: inference is real, and it’s spectacular.

In this article, we will take a deep dive into inference. We will explain what it means, why it matters, and why you should be using it. And, we promise, we will not bore you with pointless buzzwords.

What does inference mean?

Inference is nothing new. According to Merriam-Webster, the first known use of the word was back in 1594 – long before people thought about implementing AI in their solutions.

So, what does it mean?

Oxford Languages defines it as follows: “a conclusion reached on the basis of evidence and reasoning.”

Inference simply how we do most things that we have already learned. For instance, you can recognize the following word: Hello as the word “Hello” without having to re-learn how to read or carefully dissecting each letter to recognize that the “H” indeed is an H and that the “e” is an e, and so on. Since you can do that already, you immediately recognize the word as “Hello.” That is inference at work.

Inference in AI refers to the same skill, but on behalf of artificial intelligence instead of human intelligence.

What is inference in AI?

Just like how you use inference when you do most things, so do most artificial intelligence applications. You do not need to learn how to read every time you want to read something, and neither does an AI application. Nor does it need to learn how to identify human faces, cats, cars, etc., each time a human, a cat, a car, or whatever the AI is trained for passes by its cameras and sensors.

Instead, the AI uses inference.

Just as you can instantly read a word or identify a car, inference allows AI applications to deliver instant results. As a result, the AI does not require uploading a large amount of data to the cloud, where a supercomputer or the like carefully figures out if the object captured is a car and then sends the result back to the AI device. Instead, the AI uses the skills it has already acquired to draw conclusions (infer) from the data it receives – no cloud necessary.

Okay, I understand. So, first you have to train your AI edge devices...?

Great question! The answer is yes and no. In the most important ways, the answer is no. Let us dig a bit deeper and better understand what that means.

You would be correct in assuming that edge AI devices must first be trained before they can successfully use inference in their applications.

However, what exactly that means is quite different (and can be much more efficient) with machines compared to with humans. While, for example, you will have to teach each of your children how to walk, with AI edge devices, you can simply deploy a pre-trained model and they will immediately acquire the ability to infer conclusions.

If only you could deploy a pre-trained kindergarten or even high-school level education (along with the experience that comes with it!) in your child!

As the name implies, a pre-trained AI model that you deploy in your edge AI device was trained at some point. This training could be based on data acquired from a vast number of sources and processed by computers or datacenters vastly more powerful than your humble AI edge device – equipping it with skills it most likely would never be able to acquire by itself.

I like what I am hearing. How do I get started?

I am glad that you asked! Getting started with inference is easier than ever and new technologies make it possible to deploy powerful AI applications in edge devices.

The recipe for doing inference at the edge is simple:


  • An edge device
  • Sensors (for data input, like cameras, scanners, lidar, and so on)
  • Hardware capable of inference (preferably fast, like Innodisk’s purpose-designed AI accelerator modules)
  • A trained AI model


  1. Equip your edge device with specialized hardware for faster AI, such as Innodisk’s AI accelerator modules (optional but recommended for higher performance)
  2. Connect sensors needed for your application to the AI device
  3. Download or build an AI model trained for your application
  4. Deploy and optimize the AI model for your application (for example using Innodisk’s iVINNO AI deployment tool)
  5. Sit back, relax, and enjoy the future of computing.


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