Generative AI is a subset of artificial intelligence that allows a machine to create. From text, music, paintings, to realistic images, it broadens the capacity of machines to generate new content or data mimicking known patterns from a given dataset.


Imagine you’re drawing pictures with your crayons and you show all your drawings to a robot. Now, this robot likes your drawings so much, it starts to draw more pictures similar to yours. This is sort of what Generative AI does. It learns the patterns of things it sees, hears, or reads, and then it tries to create more of those things on its own!

In-depth explanation

Generative Artificial Intelligence, often termed as Generative AI, takes AI competencies to another level. Generative models are a class of statistical models primarily used in unsupervised learning to automatically learn and produce the accurate representations of the type of data they are trained on.

Generative AI is powered by an ensemble of techniques including (but not limited to), Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Transformer-based language models like GPT-3. These techniques are responsible for a wide array of impressive results across different fields from image generation to text production to music synthesis.

Take GANs, for instance. They consist of two parts: a generator and a discriminator. The generator tries to come up with new data similar to the known data, while the discriminator’s job is to identify whether the given data is real or generated. Over time, the generator improves its ability to produce data that becomes more and more similar to the real ones, tricking the discriminator in the process.

VAEs, another technique in Generative AI, work a bit differently. Given some output data, they infer the statistical properties of the input that could have generated such outputs. This is done by learning a low-dimensional latent space representation of the data. The learned representation is not deterministic but probabilistic in nature which can then be sampled from, to generate new data.

In the context of language modeling like GPT-3, Generative AI uses large corpus of text data to create coherent and contextually relevant sentences. This is achieved by predicting the next word in a sequence, ultimately being capable of generating whole paragraphs of novel creative text.

Despite their relatively recent introduction, Generative AI techniques have made significant strides, opening novel avenues across diverse areas such as deepfake video generation, text-to-image translation, music composition, customer interaction management, and many more.

Generative Adversarial Networks (GANs), Variational Autoencoder (VAE),s (VAEs), Transformer-based language models, GPT-3, Unsupervised Learning, Latent Space, Autoencoders, Deepfake, Data Generation