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Generative AI leverages neural networks to create data that resembles real examples, from images to text and music. It's a breakthrough technology driving art, content creation, and more by learning patterns and producing novel, often astonishing, outputs.

Let’s Understand Generative AI in Easy Words

Right now, everyone is talking about generative AI. Generative AI is capable of doing so many different things. Let’s understand what generative AI is in simple words.

To comprehend generative AI, we must first define artificial intelligence. Let me begin by posing a query.

Are digital devices intelligent?

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Many people will say “yes” in response to this query; however, this is incorrect. Computers are not intelligent. They need to become intelligent, so whatever we do to achieve that falls under the category of artificial intelligence. Artificial intelligence is a vast field with no limit and new developments occurring daily.

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We will now discuss a section on machine learning. How does machine learning work?

Machine learning entails learning from the past, comprehending the insights that can be gained from that, identifying the patterns, educating ourselves, and being able to predict the future based on the learning that has been done so far. That prediction should be accurate as well.

You’ll have feelings that this happened somewhere with us after reading this. Yes, since we were children, this has been occurring to us. We are just basing our assumptions on what we have already learned. And we give this capacity to an uneducated system. We need an algorithm, or a particular collection of instructions, to accomplish this. A machine learning algorithm is the collection of instructions used in this process.

What is deep learning?

Deep learning is a subset of machine learning. In deep learning, a unique technique is combined with standard machine learning to predict the future.

Imagine if we had seen so many cats since birth that we could instantly tell whether we saw a tail or even a small portion of one. Our brain accomplishes all of this by thinking more deeply about challenging issues. Another example is when we need to make a move in a game of chess, but before we do, our brain deeply considers things like what might happen after this move, what move our opponent might make next, what if he doesn’t make that move, etc. A decision is taken after careful consideration. It’s the same as making future predictions but with a challenging assignment.

Numerous millions of neurons make up the brain. All of these neurons contribute, and deep learning also makes use of the idea of these neurons.

Deep learning is a subtype of machine learning; however, they are not distinct from one another. Furthermore, we can observe that generative AI, a subtype of deep learning, isn’t fantastic.

A system known as generative AI allows us to provide a little amount of pertinent information, and it responds by providing new information that, although not exactly replicating the original, has many similarities.

For example, you can generate any image, sound, song, or piece of music based on your requirements; in general, generative AI allows us to generate anything. And a lot of mathematical computation is being done in the background to start the task.

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The main distinction between these three is that machine learning requires less data than deep learning does, and vice versa. And generative AI needs a lot of data because the amount needed for deep learning is insufficient. Because of this, generative AI-based products are being produced even now by major corporations.

Let’s take a technical look at it.

Artificial Intelligence Definition:

The replication of human intelligence functions by machines, particularly computer systems, is known as artificial intelligence.

Machine Learning Definition:

Machine learning is a branch of artificial intelligence that focuses primarily on creating algorithms that enable a computer to independently learn from data and previous experiences. Or we could say that machine learning allows a system to predict outcomes without being explicitly programmed, automatically learn from data, and enhance performance from experiences.

Deep Learning Definition:

A family of machine learning techniques known as “deep learning” uses numerous layers to gradually extract higher-level features from the input’s raw data. Artificial neural networks with numerous layers (deep architectures) are used in deep learning to automatically recognize and represent complex patterns in data.

If I define machine learning and deep learning in one sentence, it is the process of “prediction, classification, and clustering.”

Generative AI:

  • There will be no estimations made by generative AI.
  • Generative AI won’t make any predictions.
  • Generative AI won’t be able to categorize anything.
  • Nothing will be grouped by generative AI.

Generative AI only generates things. There are many different generations, including text, audio, video, images, music, songs, and so on.

Still, because generative AI is a subset of deep learning, it makes use of:

  • Every deep-learning idea
  • Every deep-learning technique
  • Each deep learning method
  • And generate the things by using the above

Generative AI Definition:

Any artificial intelligence (AI) system that can generate fresh text, photos, videos, audio, code, or synthetic data is referred to as generative AI.

The formula is well known:



y= output

x= input


a function that is utilized for clustering, classification, and regression in ML and DL. However, generative AI uses this feature to create original content.

The three most popular categories in generative AI:

  • Generative Adversarial Networks (GANs)
  • Variational Autoencoders (VAEs)
  • Autoregressive Models

Generative Adversarial Networks (GAN):

A generator and a discriminator are the two neural networks that makeup GANs. The generator’s responsibility is to produce data samples, while the discriminator’s responsibility is to determine which samples are real and which are not.

The two networks are trained in a competition where the generator tries to trick the discriminator by producing more realistic data, and the discriminator gets better at telling the difference between real and fake data. The generator learns to produce high-quality data that closely mimics the training data thanks to this adversarial process.

Suppose I want to be a very talented artist, and my goal is to be able to completely replicate any artist’s paintings. I enlisted a friend’s assistance in this and instructed him to study the works of all the artists to identify any errors in my drawing. He was to then point out the errors to me so that I could try to correct them and make my drawing more similar to the original. In this way, my friend began analyzing numerous images and began pointing out flaws in my drawings. He used to point out the flaws in my pictures, and I eventually improved them by realizing what I had done wrong. Although my friend mostly succeeded and I frequently failed, this is when the real fun began. As I learned more, he began pointing out every little mistake, but now that I’ve had so many failures (I’ve mastered failure and learned every little thing that causes me to fail, so I fix those faults myself), my drawings are so accurate that it’s hard for him to find errors.

In this game, I play the role of the “Generator,” and my friend plays the role of the “Discriminator.” The discriminator must determine whether the images are real or phony, whereas the generator’s task is to produce images (or data).

Let’s take some real-time examples of GAN:

In the real world, GANs do fascinating things like produce lifelike images, create video game characters, and even assist researchers in the development of new products like pharmaceuticals.

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  • ANs are frequently employed in image synthesis to produce realistic, high-resolution images. Creating pictures of people, animals, or landscapes, for instance, Applications include enriching training data for computer vision tasks, producing AI-generated art, and producing faces for video games.
  • In the entertainment business, GANs are used to produce computer-generated characters and creatures for movies and video games. They aid in creating believable characters for imaginary settings.
  • Video Generation and Prediction: GANs have applications in video editing and compression because they can create fresh video frames or forecast upcoming frames in a video sequence.


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  • GANs can be used for style transfer, which is the process of translating the aesthetic of one image into another. For example, turning an image into a creative painting in the manner of a well-known artist Image-to-image translation is transforming images from one domain to another, such as converting black-and-white photos into color or satellite images into maps.
  • GANs can be used to boost low-quality photos’ resolution, making them crisper and more detailed. Applications like boosting the quality of old photographs or raising the resolution of medical pictures make use of this.


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Drug Discovery: GANs are employed in pharmaceutical research to produce novel molecular structures with certain features that can help with drug discovery and design.


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Realistic 3D model generation: GANs can produce 3D models of objects or settings that are helpful in applications like virtual reality (VR) and augmented reality (AR).


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Text-to-Image Generation: GANs are capable of creating images from descriptions in text. For instance, using a textual description of a bird as a starting point, GANs can produce an image of the bird.


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Voice Creation and Speech Synthesis: GANs may produce voices and speech that are eerily reminiscent of actual people, giving text-to-speech systems a more expressive and natural quality.


Variational Autoencoders (VAEs):

What should you do if you’re asked to write a poem, a song, or a picture but just know the first line or the first couple of verses, or if you know it but it’s not quite right?

Whatever you come up with, even if it’s not ideal, Variational Autoencoders (VAEs) will take care of the rest. Yes, you can use this model to create any image, music, poem, or other type of content; simply start with what you want and let the model handle the rest.

VAEs are a particular class of autoencoder, a form of neural network design utilized for data reduction and reconstruction. Input data can be encoded into a latent space using VAEs, which can subsequently be used to generate fresh data samples.

The model becomes more adept at producing varied and insightful data as it learns to represent data in a continuous and structured fashion. In the actual world, VAEs are used in a variety of situations, including:

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They can develop variations of a painting or drawing style in art by building new artistic pictures off of pre-existing ones.


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They can create new songs in a certain musical genre by writing new tunes that are similar to existing melodies.


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They can be used in data analysis to generate fresh samples of data that mimic current data, which can enhance the performance of machine learning models.


Models of Autoregression:

Like language models (such as GPT-3), autoregressive models sequentially produce new data one element at a time. By studying the probability distribution of each element in light of the preceding ones, they can create new sequences.

For example:

  • They may create new sentences or paragraphs of text using natural language processing and the words in an existing document.
  • They can generate new musical notes from an existing piece of music.
  • When making new images, they can create new pixels based on the pixels around them, imitating the look of already existing images.
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Currently, Chat GPT, a large language model (LLM), is the most well-known instance of this. A very vast infrastructure and a very dense dataset are needed to develop an LLM model. A super-smart computer system that can comprehend and produce human language is known as a big language model. It absorbs knowledge from a lot of content, such as books and webpages, and then applies that understanding to inquiries, dialogues, and even the creation of stories or articles.

Large language models are employed for a variety of purposes in the actual world.

  • By responding to their inquiries, we assist people in finding information on the internet.
  • To aid in language translation, making it simpler for speakers of various languages to comprehend one another.
  • To develop virtual assistants and chatbots that can communicate with and engage with people.
  • To produce content that resembles human speech, such as articles, poems, or even screenplays.

It’s like having your private genius friend assist you with knowledge and language.


An intriguing and cutting-edge area of artificial intelligence called “generative AI” is dedicated to building tools that can produce original and creative data. By recognizing patterns in data that already exist and applying that knowledge to generate new outputs, it enables computers to display creativity. This technique has numerous uses in a variety of fields, including the creation of text, music, and images.

Consultant (Digital) in StatusNeo. Master of Engineering in Data Science. Love to work on Machine Learning, NLP, Deep Learning, Transfer Learning, Computer Vision, Yolo, MlOps.