Generative AI: Its Role in the AI Field

Generative AI is a sophisticated form of artificial intelligence technology designed to produce diverse types of content. Its capabilities extend to:

• Text: Crafting written material such as articles, stories, and poetry.
• Imagery: Creating visuals, including graphics, illustrations, and artwork.
• Audio: Generating sound, including music, voiceovers, and sound effects.
• Synthetic Data: Producing artificially generated data that mimics real-world information, useful for various applications like training models and simulations.

This technology uses advanced algorithms to create unique and engaging content across various mediums.

But before proceeding with GenAI, we need to understand

What is Artificial Intelligence? | What is Machine Learning? | What is Deep Learning?

Artificial Intelligence:

Artificial intelligence (AI) is the overarching discipline that covers anything related to making machines smart.

For Example, AI is a branch of computer science that deals with the creation of intelligent agents and our systems that can listen, learn, and act autonomously.

Essentially, AI has to do with the theory and methods to build machines that think and act like humans.

Machine Learning:

In the discipline of AI, machine learning is a subfield. It is a program or system that trains a model from input data.

The trained model can make valid predictions from new or never-before-seen data drawn from the same source used to train it.

Machine learning gives the computer the ability to learn without explicit programming.

Two of the most common models of machine learning are

• Unsupervised ML Model
• Supervised ML Models.

The critical difference between the above two models is that supervised models have labels. Labeled data has a tag, such as a name, a type, or a number. Unlabelled data has no tag.

Deep Learning:

Deep learning fits as a subset of machine learning. While machine learning is a broad field that encompasses many different techniques, Deep learning is a type of machine learning that uses artificial neural networks, allowing them to process more complex patterns than machine learning.

Artificial neural networks are inspired by the human brain. They are made up of many interconnected nodes, or neurons, that can learn to perform tasks by processing data and making predictions.

Deep learning models typically have many layers of neurons which allows them to learn more complex patterns than traditional machine learning models. And neural networks can use both labeled and unlabeled data. This is called semi-supervised learning.

In semi-supervised learning, a neural network is trained on a small amount of labeled data and a large amount of unlabeled data.

The labeled data helps the neural network to learn the basic concepts of the task. At the same time, the unlabeled data helps the neural network to generalise to new examples.

Generative AI:

Now, we finally get to where generative AI fits into this AI discipline.

GenAI is a subset of deep learning, which means it uses artificial neural networks and can process both labeled and unlabeled data using supervised, unsupervised, and semi-supervised methods.

GenAI is a type of artificial intelligence that creates new content
based on what it has learned from existing content. The process of learning from existing content is called training and results in the creation of a statistical model.

When given a prompt, GenAI uses the model to predict what an expected response might be. And this generates new content. Essentially, it learns the underlying structure of the data and can then generate new samples that are similar to the data it was trained on.

The rise of transformer-based deep neural networks, particularly large language models (LLMs), has fueled the growth of generative AI systems.

Basics of Large Language Model

Large language models are also a subset of deep learning. Deep learning models or machine learning models, in general, can be divided into two types:

• Generative — A generative model is designed to produce new data instances by learning the underlying probability distribution from existing data. This means that, rather than merely analyzing or classifying data, generative models can create original content that resembles the characteristics of the training set. Through this process, they can generate entirely new samples, whether it's images, text, or other forms of data, effectively expanding the possibilities of creativity and innovation within various applications.
• Discriminative — A discriminative model is a type of model used to classify or predict labels for data points. These models are typically trained on a dataset containing labeled data, allowing them to learn the relationship between the features of the data points and their corresponding labels. Once trained, a discriminative model can predict the label for new, unseen data points.

In this post, we will gain a basic understanding of AI, ML, DL, Generative AI (GenAI) and LLM. It's important to note that GenAI is a type of artificial intelligence that generates new content based on what it has learned from existing content.

In the next post, we will get more details on Large Language Models.