Traditional AI vs Generative AI

In the 3rd edition of my "Generative AI for Business Innovation" video series, I briefly discuss the crucial differences between traditional AI and generative AI. I think by now, most of us are aware of these differences.

What is traditional machine learning?

Traditional machine learning is adept at the following, like we discussed in the previous video.

Making Predictions: Traditional machine learning is like a smart learner who learns from what's already there. It is very good at making predictions, just like a weather forecaster who can predict if it’s going to rain based on past data.

Classifying Objects: It is also a super fast sorter that can be used to classify objects, such as apples from oranges, super quickly once it has been trained well.

Finding Anomalies: Traditional AI can also find patterns in data that can be used to identify anomalies, such as in fraud detection or in face recognition.

What is Generative AI?

But generative AI goes one step beyond that by not just learning but also creating new things, such as writing its own stories, creating music, videos, images, and so on. You can think of it as an artist in the AI world. In other words, it is not just crunching numbers but also making brand new stuff from bits of data that look very real.

Why does it matter to know the difference?

Knowing this difference helps you apply it to your business based on different needs. For example, you can use Generative AI to guess what's going to be popular next in your industry, what the market trend is, and to shape up your business strategy.

Understanding this kind of AI can really change the game in your work or business by solving problems or tapping into new opportunities that were not possible before.

And as we continue to embrace these new innovations, it is undoubtedly going to change the world!

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Disclaimer: All opinions are my own and not those of my employer.