Artificial Intelligence – What Is Generative AI?
Generative AI is a subfield of artificial intelligence that focuses on creating new and original content, such as images, music, text, and even videos. Unlike traditional AI systems that rely on predefined rules and patterns, generative AI tools have the ability to produce novel and creative outputs based on patterns they’ve learned from vast amounts […] Generative AI is a subfield of artificial intelligence that focuses on creating new and original content, such as images, music, text, and even videos. Unlike traditional AI systems that rely on predefined rules and patterns, generative AI tools have the ability to produce novel and creative outputs based on patterns they’ve learned from vast amounts of data.
At the core of generative AI lies a class of algorithms known as Generative Models. These models are designed to understand the underlying structure and distribution of the data they were trained on and use that understanding to generate new data that resembles the training examples. Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE) are two popular models of generative models.
Generative Adversarial Networks (GANs):
The discriminator and the generator are two neural networks of GANs. The generator’s task is to create fake data samples, such as images or text, while the discriminator’s role is to distinguish between real and fake data. The two networks are trained together in a competitive process.
During training, the generator starts with random noise and attempts to create realistic data samples. On the other hand, the discriminator is fed with real and fake data and tries to correctly identify which data is real or fake. As the training progresses, the generator becomes more adept at generating convincing data, while the discriminator improves at telling real from fake.
The ultimate goal of GAN training is to reach a point where the generator produces data that is indistinguishable from real data, fooling the discriminator. Once this equilibrium is achieved, the generator can be used independently to produce new, original data that shares characteristics with the training data. GANs are commonly used in generating images, videos, and audio samples.
Variational Autoencoders (VAEs):
VAEs are another type of generative model that takes a different approach compared to GANs. VAEs are based on the idea of encoding data into a lower-dimensional space and then decoding it back into the original data space. This process encourages the model to learn the underlying distribution of the data, making it capable of generating new samples.
The VAE architecture consists of an encoder network that maps the input data into a lower-dimensional space called the “latent space,” and a decoder network that converts points in the latent space back into the original data space. During training, the model learns to reconstruct the input data as accurately as possible, while also regularizing the latent space to follow a specific distribution (usually a Gaussian distribution).
Once the VAE is trained, generating new data involves sampling random points from the latent space and using the decoder network to transform them into the data space. Since the latent space has been learned to represent the underlying structure of the data, samples taken from this space result in new data points that are similar to the training data.
GANs and VAEs have their strengths and weaknesses, and the choice of which to use depends on the specific application and the data generated.
