Ultimate Guide On Generative AI; What It Is And How Does It Work?

What is Generative AI?

Generative AI’s abilities go far beyond mobile apps and avatars, as now they are being utilized to produce codes, write articles, craft images, and much more. You must be wondering what’s so special about this, as this can easily be done with other AI tools like ChatGPT and Bard.

However, what you need to know is that General AIs like ChatGPT and Google Bard are trained on an existing dataset - so whenever a prompt is given to them, they fetch information from the given data set and present it in front of you. In simpler terms, traditional AIs are not strong enough to generate an output of a query that is not present in their data set.

On the other hand, Generative AI is an AI-powered technology that leverages a large corpus of data to generate new content on its own. For example, a generative AI can develop unique images - that too by only your few words prompt, sounds interesting, right?

How Does Generative AI Work?

Generative AI harnesses the power of machine learning algorithms to analyze large data sets. This means that you can provide AI with a bunch of existing information on a topic so that it can train on finding patterns and structure of the provided information.

Based on what it learns from the inputted data, generative AI would be able to create new and original content. For example, you ask Chatgpt to write a comparison in table form about which movie has the saddest ending - Notebook Or Titanic. ChatGpt will quickly gather all the relevant information available over the web about Notebook and Titanic, and will instantly produce the content you requested.

And yes, we did ask ChatGPT this question. See what it has to say about the prompt.

Who Uses The Generative AI?

Businesses of all sizes - let it be a startup, or an enterprise-level mobile app development company harness the power of generative AI in different manners.

Businesses may use this tech-savvy technology to streamline and improve customer support while leading app development companies like Appingine use this technology to produce robust and scalable apps.

However, some of the popular kinds of Generative AI use cases include:

• Empowering existing abilities of customer support agents with AI
• Analyzing data accurately for the sales team to get more qualified leads
• Customizing marketing communications
• Optimizing data for IT departments
• Helping software engineers generate code
• Streamlining the HR industry for onboarding premium talent

Generative AI vs Traditional AI - What Is The Difference?

If you still have mixed and unclear feelings about traditional AI that we use normally and how Generative AI differs from that, consider paying attention to the chart below.

Appingine, the leading mobile app development company’s expert conducted comprehensive research on Generative AI and has concluded that traditional AI uses machine learning rules and programmed logic to carry out specific tasks, however, Generative AI learns from large data sets to produce human-like content.

Consider the following example:

• Traditional AI makes ticketing systems efficient by identifying customer sentiment, service requests, and intent to automatically route the query towards the right agent
• Generative AI improves agent productivity by empowering them with intelligent writing tools, allowing them to address requests more efficiently.

Have you got the difference now?

Types Of Generative AI models?

Generative AI comes with many use cases, which means there are various types of Generative AI models suited to perform dedicated tasks. Here are some of the most common types of AI models.

Each of these is the prime Generative AI that is being used in the world right now. However, the best three of them are described below:

Generative Adversarial Network

In this type of Generative AI, two networks known as the “generator” and “discriminator” compete against each other to continuously create better content. Once the GAN receives the data sample, it creates the output as per the requested prompt.

Transformers

This type of Generative AI is the neural networks that harness the power of deep learning architecture to predict new text based on sequential data.

The main idea of the transformer is to learn the context of the prompt and “transform” the type of input to generate human-like text.

Variational autoencoders

These autoencoders are the generative models that encode the coming inputted data, simplify and optimize the crucial prompt points, and store them in a hidden storage that is known to be called latent space. When a user enters the prompt, the encoder pulls the data from the latent space and assembles it again to its original form.

This type is best known for delivering human-like images and texts.