Generative AI in a Nutshell and Tools(ChatBots, Video and Image Generators etc.)

3 min readJan 8, 2024

Generative AI is a cutting-edge technology that’s transforming the way we create and interact with content. Although we have long background with AI, it is more in our daily lives with generative AI. Now, we can easily find answers to our questions and instantly create works. Even if it is open to fraud, these tools are definitely our new assistants to save time and money. I wanted to give a short definition and show some apps in different categories to help anyone interested.

Generative AI can create new content, such as text, images, audio, and video. It works by learning the patterns and relationships in existing data and using that information to generate new outputs. There are many different methods for generating new content, but some of the most common techniques include:

Generative adversarial networks (GANs): GANs are a type of neural network that consists of two competing networks: a generator and a discriminator. The generator is responsible for generating new content, while the discriminator is responsible for distinguishing between real and fake content. By training the two networks against each other, GANs can learn to generate realistic and high-quality content.
Variational autoencoders (VAEs): VAEs are another type of neural network that is used for generative modeling. VAEs are trained to encode data into a latent representation and then decode the latent representation back into the original data. By learning the distribution of the latent representation, VAEs can generate new data that is similar to the training data.

What is key for Generative AI:

Data quality: The quality of the data used to train a generative AI model is crucial for its ability to generate realistic and accurate outputs. A generative AI model that is trained on high-quality data will be able to generate more creative and original content.
Model architecture: To better learn patterns and relationships in data. Different architectures are better suited for different tasks, and researchers are constantly developing new architectures to improve the performance of generative AI models.
Training methodology: Different training methodologies can lead to different types of outputs, and researchers are constantly exploring new training methods to optimize the performance of generative AI models.
Regularization: Regularization techniques are used to prevent generative AI models from overfitting the training data.
Bias mitigation: Generative AI models can exhibit biases that are reflected in the data they are trained on.

ChatBots:

ChatGPT: https://chat.openai.com/ (Everyone knows about it)

Bard: https://bard.google.com/chat (Google Tool)

Character AI: https://beta.character.ai/ (Speak with different characters and ask them-Fun)

Image Generator and Editors:

Dall-E: https://www.bing.com/images/create

Storia Lab: https://www.storia.ai/lab

Stylar: https://www.stylar.ai/

Voice Generators:

Mubert and Sun. A https://mubert.com/

Meta’s Audio Box https://audiobox.metademolab.com/

Vocal Remover https://vocalremover.org/