What is Generative AI? How is it different from traditional AI? 

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You must have heard of Generative AI. It’s quite popular these days.


  • What exactly is it?
  • How does it differ from regular AI? and
  • How does it work?

Let us understand with a simple no-jargon explanation:

Traditional AI was primarily used for tasks like classification, prediction, or detection. For instance, an AI model could determine whether an image contains a cat or a dog.

It could also identify faces in photos or detect various objects or people in your Facebook posts.

In essence, it could handle basic tasks but lacked the ability to create an image from scratch.

However, with the latest advancements, we now have AI models that can generate images based on a given description.

For example, the cover image of this article was generated using one such Generative AI tool.

By simply providing the prompt “a featured image for an article on Generative AI,” the tool produced the image within a minute.

And Generative AI goes beyond just generating images; it can also create music, text, code, videos, and more.

Essentially, this branch of AI that generates new content based on user prompts is known as Generative AI. ChatGPT is an example of Generative AI.

Read, how I learned AI as a non-tech person in 4 months for free

Cool, how does Generative AI work?

Now, let’s dive deeper into Generative AI.

Unlike traditional AI, which typically relies on a single model to perform a task, Generative AI involves two models working together: the Discriminator and the Generator.

To help you understand this concept, let’s use an analogy:

Imagine a robber being trained by a policeman.

In this scenario, the robber attempts a robbery, and the policeman’s job is not only to catch the robber but also to provide feedback on why the robber was caught.

The aim is for the robber to improve his techniques with each attempt until the policeman can no longer catch him.

Similarly, in Generative AI, the Generator and Discriminator work in tandem.

The Generator generates the content (like art, music, etc.), while the Discriminator’s role is to classify it as either fake or real.

The Generator’s objective is to create an image that the Discriminator cannot classify as fake. Once this happens, we have a “fake” image that actually appears real.

In a way, you can think of Generative AI as a movie plot where the villain defeats the hero in the end, with the hero’s assistance.

Thanks for choosing to read it completely. I can try to simplify any AI concept for you.

In case you want me to simplify any AI topic, please let me know on LinkedIn – https://www.linkedin.com/in/nikhileshtayal/

Further Reading

In case, you want to learn more about it, you can take this Generative AI free course. You need to opt for “Audit this course” in order to get it free.

If you want to quickly understand ML then read – Let’s learn to build a basic AI/ML model in 4 minutes (Part 1)

Read Can AI do everything? If not, what are the things AI can do today?

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Nikhilesh Tayal
Nikhilesh Tayal
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