What is Generative AI

Hello everyone. Welcome to today's post on generative AI. In today's post, we are going to talk about what is generative AI and look at some insights. 

Did you know that you can learn from vast amounts of data and generate new content and patterns characteristics of the original data? Generative AI are algorithms that can create seemingly real authentic material from the training data such as tech photos and audios.

What is Generative AI 

A generative AI is basically a type of AI system capable of generating text, images and other media in response to prompts. The beginning of generative AI begins with a prompt which could be a word image or a video etc. 

Then in response to the request several AI algorithms written fresh contents. We have three categories that can be used to group the skills of a generative AI. So here we have the skill set which is for generative AI which is used for creating ideas and contents. 

Also in terms of generating fresh original outputs using a variety of media like a video or commercial or even a novel protein with antibacterial capabilities and then moreover increasing effectiveness and accelerating manual or repetitive operations like email writing coding or document summaries.

How Generative AI Works

Over the past couple of months, large language models, or LLMs, such as ChatGPT, have taken the world by storm. Whether it's writing poetry or helping plan your upcoming vacation, we are seeing a step change in the performance of AI and its potential to drive enterprise value.

In this section of the post I am going to give a brief overview of this new field of AI that's emerging and how it can be used in a business setting to drive value. 

Large language models are actually a part of a different class of models called foundation models. The term foundation models was actually first coined by a team from Stanford when they saw that the field of AI was converging to a new paradigm, before AI applications were being built by training maybe a library of different AI models, where each AI model was trained on very task-specific data to perform a very specific task. 

They predicted that we were going to start moving to a new paradigm where we would have a foundational capability or a foundation model that would drive all of these same use cases and applications. So the same exact applications that we're envisioning before with conventional AI and the same model could drive any number of additional applications. The point is that this model could be transferred to any number of tasks.

This gives this model the superpower to be able to transfer to multiple different tasks and perform multiple different functions that it's been trained on a huge amount in an unsupervised manner on unstructured data.

It's this generative capability of the model, predicting and generating the next word based on the previous words that it's seen beforehand. That is why that foundation models are actually a part of the field of AI called generative AI, because they're generating something new. 

Types of Generative AI

Next, we will see what are the types of generative AI models, so firstly, we need to know that, most of the generative AI algorithms are constructed on top of foundation models that have also been self-supervisely trained on enormous amount of unsupervised data to find the underlying patterns for a variety of tasks. Here are some examples of generative AI; 


ChatGPT, also called generator pre-trained transformer. It is an autoregressive model that has been pre-trained on the corpus of text to produce excellent natural language writing. ChatGPT has been made adaptable to do a range of language activities including question answering, summarizing and language translation. 


DALL. E2 is also a deep learning model which was created by OpenAI to produce digital images from prompts or natural language description. Dall. E2 makes use of GPT variant that has been altered to produce images. 


Then we talk about StyleGAN for which the latest version is StyleGAN 3. StyleGAN has been used for a number of fields including fashion, design and the arts. Additionally, it has been used to create artificial data for the training of machine learning models in disciplines like categorization and object detection. 

Advantages of Generative AI

Then next up guys, let's talk about some of the advantages of generative AI. Every aspect of the organization can benefit greatly from the application of generative AI. So here are some of the advantages, which is in terms of automating the labor intensive process of content creation. 

It also helps in lowering the effort required to reply to emails, which increases the responsiveness to particular technique and creates accurate portraits of people, making a logical story out of difficult facts, also streamlining the production of material in specific style. 

Limitations of Generative AI

Going ahead, let's talk about some of the generative AI limitations. So the numerous drawbacks of generative AI are starkly illustrated by its early implementations. Some of the restrictions here are as follows:

It doesn't always tell where the content came from. That means the source is not defined, correct? It is more challenging to spot false information when it sounds very realistic. So various controversy in terms of ChatGPT have come into picture due to which it sounds realistic but they are false information. 

Next is in terms of understanding how to adjust for the novel circumstances. This could be challenging results can hide biases. So these were quite some limitations of generative AI.  

Applications of Generative AI

Now let's talk about some applications of generative AI. So these are the five most fundamental applications of generative AI, which includes your art and design, data augmentation, entertainment, in terms of drug discovery, and also in terms of personalization and recommendations.

Art and Design 

So talking about art and design; artists and designers leverage generative AI models to create unique visually appealing and innovative pieces of art ranging from paintings to fashion designs. 


When we talk about in terms of entertainment: Generative AI has been employed in creating music or writing screenplays and even designing video game levels, offering some avenues for human-machine collaboration in terms of creative process. 

Data Augmentation 

Then when we talk about in terms of data augmentation, in machine learning, generative AI can generate additional training data particularly useful for the available data in scars or which is imbalanced. 

Drug Discovery 

Next we see about drug discovery. The generative AI models can accelerate drug discovery by generating new molecular structures with desired properties significantly reducing the time and cost of traditional drug development process. 

Personalization and Recommendations 

Lastly, when we talk about in terms of personalization and recommendations, by learning user preferences and generating relevant content, generative AI can enhance user experience across various platforms from social media feeds to e-commerce recommendations.

So these were quite few applications about generative AI. I hope you enjoyed reading this post. Thank you for reading, and like always happy learning. Please be kind enough to like it and you can comment any of your doubts and queries and we will reply them at the earliest do look out for more videos in our playlist and subscribe to Edureka channel to learn more. Happy learning.

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