Generative AI : What is it, How does it work and its Applications

What is Generative A.I. and How does it Differ from Normal A.I. ?

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Generative AI, short for Generative Artificial Intelligence, refers to a category of artificial intelligence that focuses on creating new and original content. Unlike classic AI models that are designed for specific tasks or classifications, generative AI has the capability to generate content autonomously.

It uses machine learning algorithms, particularly generative models, to produce new data that is similar to, but not exactly the same as, the training data it has been exposed to. One example of generative AI that comes to my mind is the Generative Adversarial Network (GAN), where two neural networks, a generator and a discriminator, engage in a competitive process to create increasingly realistic and indistinguishable data. ChatGPT and DALL-E are best examples of generative A.I. .Generative AI finds applications in various fields, including image and text generation, creative content creation, data augmentation, and more. It has the capacity to generate novel and diverse outputs, making it a powerful tool in areas where creativity, diversity, and originality are essential.

Explaining different types of Generative A.I.

Image by Ray Shrewsberry • from Pixabay

Generative A.I. require some model that are based on complex mathematics. It is based on different types of AI models like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Neural Networks, etc. I have explained Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) as follows (Well, as I wrote them and explained them as suited to me. You are free to explain the thing in comments and I will update and give you the credits)

Generative Adversarial Networks (GANs) :

Generative Adversarial Network, or GAN is a model where two neural networks engage in a competitive process, leveraging deep learning techniques to enhance their precision in making predictions. The duo of neural networks comprising a GAN includes the generator and the discriminator. The generator is a convolutional neural network, while the discriminator is a deconvolutional neural network. The primary objective of the generator is to craft outputs that closely resemble authentic data, aiming to deceive observers. On the flip side, the discriminator's task is to discern and flag which outputs it encounters are synthetically generated. It is like a creative duo of computer programs that love playing games with each other. Like you see imagine two friends, a Generator and a Discriminator, who challenge each other in a game where one creates pictures, and the other tries to figure out if they are real or fake.

In this game, the Generator's job is to make images that look as real as possible, almost like photos. It starts with some random ideas, like playing with different colors or shapes. These ideas are like magic ingredients that the Generator uses to create a picture. Now, the Discriminator is the detective. Its mission is to tell whether the pictures are real or just made up by the Generator. It studies the images and tries to catch any tricks the Generator might be playing.

They play this game over and over again. The Generator keeps getting better at making images, and the Discriminator keeps getting better at telling if they're real or not. It's like a friendly competition, but they're both learning and improving together. Behind the scenes, there's some math involved. The Generator and Discriminator use a special formula to guide their game. The formula helps the Generator make images that fool the Discriminator, and the Discriminator tries not to be fooled.

In the end, they find a balance where the Generator makes images that look so real that even the Discriminator has a hard time telling if they're fake or not. It's like magic because the computer learns to create amazing pictures by playing this game! The game is a bit like a seesaw – when one friend goes up, the other goes down. This seesaw helps them both get better, and they keep playing until the computer creates really cool and realistic pictures. And that's how GANs work, with the Generator and Discriminator as creative friends, playing and learning together to make awesome things!

Variational Autoencoders (VAEs) :

In machine learning, Diederik P. Kingma and Max Welling introduced the variational autoencoder (VAE), an artificial neural network architecture. This model falls under the categories of probabilistic graphical models and variational Bayesian methods. Well, you see, Variational Autoencoders, or VAEs, are like human artists inside a computer that can learn to draw and create new things all by themselves! Imagine you have a friend, let's call them the "Encoder," who takes a picture and turns it into a secret code like woah - magic. This secret code is like a special recipe that describes everything about the picture. Now, you have another friend, the "Decoder," who can take that secret code and use it to draw the picture again. It's like having a magical recipe that can create the same picture over and over!

But here's the cool part: the Encoder and Decoder aren't just copying the picture. They are also learning how to be creative. The Encoder figures out the important parts of the picture and turns them into the secret code, and the Decoder learns how to use that code to make the picture come to life. So, when you want to create something new, you can ask the Encoder to turn it into a secret code, and then the Decoder can use that code to draw the new thing. It's like having a magical translator that can turn your ideas into a secret language only the computer understands! Like making a cat look more cuter (Well, cats are already cute though)

But wait, there's more magic! The secret codes aren't just for one picture. They can represent many different pictures. It's like having a secret language that can describe lots of cool things. So, the Encoder and Decoder aren't just drawing one picture – they're learning to draw a whole bunch of amazing things and in the end, VAEs are like magicians that learn to turn pictures into secret codes and use those codes to create new and exciting things. It's like having your own team of creative friends inside the computer, ready to bring your ideas to life in a magical and artistic way!

Limitations of Generative A.I. :

Here are the limitations of generative AI that we all know but still I put :

1. Incorrect Output :

• Generative AI models may produce output with errors
• Due to the probabilistic nature, they generate the most probable response, not necessarily the correct one.
• Outputs may be indistinguishable from authentic content, leading to misinformation and potential deception.

2. Dependence on Training Data Quality:

• The correctness of generative AI models is highly dependent on the quality of training data.
• Correctness checks can be implemented, but the black-box nature of AI models requires user trust.
• Closed-source commercial systems limit tuning and re-training possibilities.

3.Bias and Fairness Concerns:

• Societal biases in training data can amplify biases in generative AI outputs.
• Biases may perpetuate stereotypes, toxic language, or societal prejudices.
• Efforts to address bias through coding guidelines and quality checks are ongoing, but true fairness is a research challenge.

4.Copyright Violation:

• Generative AI may violate copyright laws by producing outputs resembling or copying existing works.
• Risks include illegal copying or creating derivative works without permission.
• Legal questions arise about originality, creativity, and intellectual property in generative AI.

5.Environmental Concerns:

• Large-scale neural networks used in generative AI contribute to significant electricity consumption.
• Development and operation have a substantial negative carbon footprint.
• Ongoing efforts in AI research aim to make algorithms more carbon-friendly through efficiency improvements and compression of neural network architectures.

Some popular applications of Generative A.I. :

Image by Amanda Wilson from Pixabay

Well, we all have used ChatGPT, DALL-E sometimes in your life. If not, what are doing bro, use the technology in your favour and win things for yourself. These both are popular examples of generative ai. Other than that, are the popular example of it are deepfake technology and Image improving technologies like Upscale AI. Deepfake make super good image except that thing that they suck at drawing fingers and toes. The reason behind this is the Complexity of Human Anatomy and Limited Training Data provided to them. Other than that, they work just fine.

References: Here is the list of websites, I recommend you to view as they have tons of useful information that I haven’t included, will be provided here:

1. Medium

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