ML and Generative AI

---

---

After the initial release of ChatGPT on November 30, 2022, the interest in artificial intelligence has become widespread. ChatGPT (GPT stands for Generative Pre-Trained Transformer) is a conversational AI system developed by OpenAI that anyone can experiment with and use as it facilitates natural conversations between humans and the bot.

Within a very short period of time, ChatGPT has made us think about how AI affects our society and economy. But one thing is sure, AI is becoming a crucial part of our lives and will shape our future in the coming years.

Just like Generative AI is the brain behind tools like ChatGPT and Dall-E3, one can regard "Machine Learning" and "Deep Learning" as the primary components that shape Generative AI. Read this chapter to get an overview of ML and DL and how these two concepts play a critical role in shaping Generative AI in its present form.

AI is not an isolated discipline; it is an umbrella of every technology that helps transcend human capabilities. With the help of below given diagram, let’s understand the relationships of various disciplines to each other and to AI.

Generative AI is the latest subtype of AI that is reshaping the landscape of creativity and innovation. Other subtypes of AI namely Machine Learning and Deep Learning lay the foundations of generative AI. In this chapter, we will briefly overview the foundations of generative AI, including machine learning, its subtypes, and deep learning.

Machine Learning - A Brief Overview

Machine Learning is a subset of Artificial Intelligence that enables computer systems or machines to extract patterns from raw data by using an algorithm or method. It builds a model themselves by learning from experience and available data, without being explicitly programmed.

Based on training methods as well as data availability, machine learning has following three basic learning categories −

Supervised Learning

In this category of machine learning, the algorithm is trained using a labeled dataset. Basically, the algorithm or model in supervised learning is provided with input-output pairs where every input is matched with a corresponding output or label. The main goal is to make a model to learn the relationship between input and outputs, enable it to accurately predict or classify new, unseen data.

Unsupervised Learning

In this category, as opposed to supervised learning, the model is trained without labeled dataset. It learns to analyze and derive insights from data autonomously. The main goal is to make a model to learn the relationship within unlabeled data.

Reinforcement Learning

In this paradigm of machine learning, rather than using labeled or unlabeled data, the model is trained with the help of agent and environment. The agent learns to make decisions by interacting with an environment.

First it takes actions in the environment and then receives feedback in the form of rewards or punishments. Finally, the agent uses the feedback to improve its decision-making.

Contribution of ML in Generative AI

Let’s understand how machine learning contributes to the foundation of generative AI −

Learning From Data

In the early stages of development, generative AI models use supervised learning to train models so that they can generate content based on the learned relationship between input and outputs.

Understanding Patterns and Relationships

Generative AI utilizes unsupervised learning to uncover patterns and relationships. It helps generative AI models to generate new content from unlabeled data.

Adaptability and Improvements

In generative AI, adaptability is very important, especially for the tasks that need continuous improvements. Generative AI models use reinforcement learning to refine their output based on feedback and rewards. ChatGPT, in fact, uses Reinforcement Learning with Human Feedback (RLHF) that involves a small increment of human feedback to refine the agent’s learning process.

Optimizing Model Parameters

Generative AI models use ML optimization techniques to fine-tune parameters. It enhances their performance, and they can generate more accurate content.

Transfer Learning

Generative AI uses another ML paradigm called transfer learning, to pre-train their model. It helps the model to accelerate the learning for specific content generation process.

Deep Learning - A Brief Overview

Deep Learning is a subset of ML inspired by the structure and function of the human brain. It uses a multi-layered structure of algorithms called artificial neural network (ANN) to extract complex features from input data.

Deep Learning algorithms, in contrast to algorithms, once set up requires less human intervention. It also requires less time for testing and hence can generate results instantaneously.

Let’s understand how deep learning contribute to the foundation of generative AI −

Hierarchical Representations

To generate diverse content, generative AI needs to learn hierarchical representations of data. Deep neural networks (neural networks with multiple layers), a kind of deep learning model, helps generative AI models to do so.

Convolutional Neural Network (CNNs)

It is a type of ANN (artificial neural network) used for analyzing images. They automatically learn spatial hierarchies of features from input images using convolutional layers. Generative AI models use CNN to extract features from visual data and to facilitate cross-modal tasks like text-to-image generation. This makes CNN a powerful tool in advancing generative AI capabilities.

Recurrent Neural Network (RNN)

They are feedforward neural networks with closed loops i.e., all the nodes are connected to all the other nodes. Each node in RNN works as both input and output. Generative AI models use RNNs to learn from examples and create new sequences of data that follow patterns they have learned.

Large-Scale Data Handling

To train generative AI models we need to access large-scale datasets. Deep learning models help to handle such datasets.

Generative Adversarial Networks (GANs)

Generative Adversarial Networks (GANs) are a type of deep neural network architecture used for generative modelling. GANs have proven highly effective for their innovative approach to generate realistic images, videos, and other kinds of content generation.

Conclusion

In this chapter, we explained how the various disciplines of AI are related to each other. We also seen an overview of machine learning and deep learning and how they play an important role in laying the foundation of generative AI’s remarkable capabilities.

We also highlighted various ML paradigms including Supervised, Unsupervised, and Reinforcement learning. It's clear that machine learning and deep learning will play crucial roles in unlocking the full potential of generative AI.