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Training Generative AI models are often trained Conventional AI models are typically
using techniques such as generative trained using supervised, unsupervised, or
adversarial networks (GANs), variational reinforcement learning techniques.
autoencoders (VAEs), or autoregressive
models.
Dataset Generative AI models typically require large Conventional AI models rely on smaller, more
amounts of diverse and representative curated datasets that are tailored to the task
data to learn effectively. These datasets at hand.
often contain thousands or even millions
of examples across various categories or
classes.
Output Generative AI output is fresh, innovative, Conventional AI produces more predictable
and often unexpected. output based on existing data.
Applications Generative AI is used in the fields of art, Conventional AI is used in banking, healthcare,
music, literature, gaming, and design. image recognition, and language processing.
Types of Generative AI
Generative AI comes in a variety of forms, each with unique advantages and uses. Let us learn about some of the
most typical varieties of Generative AI.
Generative Adversarial Networks (GANs)
GANs are neural networks that work to produce fresh data. It is made up of two neural networks, which work
together in a unique adversarial process to create realistic synthetic data. These two neural networks are as
follows:
• Generator Network: It produces data that is as close as possible to real data.
• Discriminator Network: It analyses data and provides feedback, i.e. it takes real data and the data generated
by the generator as input and attempts to distinguish between the two.
These two networks work together in a cycle where the generator tries to create realistic fake data, and the
discriminator tries to identify whether the data is real or fake. This back-and-forth process helps the generator
improve and produce more convincing data over time.
Some of the examples of GANs are as follows:
• It can create portraits of non-existing people.
• It can convert images from day to night.
• It can generate images based on textual description, for example, if we give a description of a bird, then it will
create an image that is similar to the description.
• It can generate realistic video, which can be used in film production, video games, and generating synthetic data
for training other AI models, etc.
Variational Autoencoders (VAEs)
A variational autoencoder (VAE) is a Generative AI algorithm that uses deep learning to generate new content,
detect anomalies, and remove noise. This is another class of generative models.
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