"Generative AI" Becomes a Must Know
By Agustina Alvarez ♦ 4 min read
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As ChatGPT goes viral,
"Generative AI" becomes a must know
In today’s world, ChatGPT has become one of the most popular topics, not only in the tech world but in every industry. It has made its way all over social media, so much that everyone has heard of this concept at least more than once.
What's all the hype about?
To understand the tech behind this intelligence first we need to start by differentiating between Generative and traditional AI.
Traditional AI systems are designed to learn patterns and be able to classify data in those patterns.
Meanwhile, Generative AI refers to a group of Artificial Intelligence algorithms which, unlike traditional AI, are able to generate new outputs that could have been drawn from the original dataset.
How do they do that?
Generative AI uses different types of deep Machine Learning called Generative Models that implement unsupervised learning.
In unsupervised learning, the machine is not provided with labeled data, it has to define the categories itself.
This type of algorithm excels on anomaly detection and clustering applications, which involve discovering and learning the similarities or patterns on the input data.
Imagine the possibilities!
There are several types of generative models such as VAEs (Variational Autoencoders), Transformer-based models, and the newest GANs (Generative Adversarial Networks).
GANs, the “new” trend
GANs consist of two main neural networks, a generator that creates new data and a discriminator that evaluates the data.
These two train together to generate new data that is like the training data.
How does this learning work?
It starts with the generator producing fake data for the discriminator to analize. It quickly realizes it's fake:
The more iterations of fake date are made, the closer the generator gets to produce outputs that can dupe the real data and fool the discriminator:
Last, if generator training goes well, the data classification gets harder so the discriminator can barely tell the difference between real and fake. When it starts to classify fake data as real, it means the generator job has finished.
In Conclusion
Although it's been a long time since Generative AI exists, it has taken more relevance these past months because of models such as ChatGPT, DALL-E 2, DeepMind’s Alpha Code (GoogleLabs), MidJourney, Jasper, Stable Diffusion and many others.
While Generative AI is not a new concept, it has become a powerful tool that can have many applications in manufacturing, supply chain, service, and governmental organizations.
The fact that it became so publicly known helped the widespread usage of AI on an everyday basis.
AI may change the way we create and consume content in the future, that's why it became a must know between developers, AI enthusiasts, and tech leads that want to make the most of it in the coming years.
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