Mastering Generative AI: From GANs to Transformer Models

Welcome to this session on Generative AI!

Introduction

Hello everyone, good morning, and welcome to this J session. Myself, A Said, I am the host for this session. If you have any questions and queries, please put them in the chat box, and we'll be there to help you out. The event sponsor for this webinar is Synergetics, a distinguished learning company specializing in IT technology. We offer top-class learning solutions across various industries globally, including persona-based onboarding solutions, certification solutions, reskilling solutions, emerging technology training, and more.

Agenda

The agenda for today's session is:

Understanding Generative AI (Gen AI)
Importance of Gen AI
Exploring deep learning architectures like GAN, VAE, and Transformers
Discussion on OpenAI GPT models
Practical applications of Generative AI in different domains
Responsible AI concepts and ethical AI development

Generative AI and Its Importance

Artificial Intelligence (AI) has made significant strides across various industries, becoming integral to domains like healthcare, manufacturing, entertainment, education, and IT. Generative AI, a subset of AI, is distinguished by its ability to create fresh content from user instructions, be it text, images, code, or audio.

Types of Generative AI Deep Learning Architectures

Gen AI models are designed using specific deep learning architectures. Here are some key ones:

1. Generative Adversarial Networks (GAN)

GANs contain two components: a generator, which creates new data instances, and a discriminator, which evaluates the authenticity of the generated data. It learns the underlying data structure and is used in applications such as generating human faces, composing music, text-to-image translations, and face aging.

2. Variational Autoencoders (VAE)

VAEs capture the probability distribution of a dataset and generate new samples. They encode data into a latent space and can be used for generating realistic images, music compositions, text summarizations, and more.

3. Transformer Models

Transformers process sequential data like text and speech using attention mechanisms. They are widely used for tasks like machine translation, text summarization, language modeling, and are the backbone of GPT models.

OpenAI GPT Models

OpenAI’s GPT models are generative pre-trained transformers capable of generating human-like text. These models include GPT-4, GPT-3.5, and more. GPT-4 is a large multimodal model capable of handling both text and images.

Key Concepts of GPT Models:

API Key: Used for authenticating requests to AI models.
Prompt: The input text provided to the model.
Completion Endpoint: The API endpoint for generating responses.
Token: Basic units of text the model processes.
Temperature: Controls the randomness of the model’s output.
Model Selection: Different models are used based on requirements and capabilities.

Advanced Techniques: Fine-Tuning and RAG

Despite their capacity, GPT models must sometimes be fine-tuned to meet specific needs. Fine-tuning involves customizing the model using your data. Retrieval Augmented Generation (RAG), on the other hand, does not modify the models but uses custom data as grounding content.

Practical Applications

Gen AI applications are numerous, from code generation using GitHub Copilot to healthcare diagnostics. Real-world scenarios span across banking, insurance, manufacturing, education, and many other industries.

Ethical Development of AI

When developing AI applications, one must adhere to responsible AI principles ensuring fairness, reliability, safety, privacy, security, inclusiveness, transparency, and accountability.

Keywords

Generative AI
Deep Learning
GAN (Generative Adversarial Network)
VAE (Variational Auto Encoder)
Transformer Models
OpenAI GPT
Fine-Tuning
Retrieval Augmented Generation (RAG)
Responsible AI Principles

FAQ

Q1: What is Generative AI? A1: Generative AI is a subset of AI that can create new content such as text, images, code, or audio from user instructions.

Q2: What are the key architectures used in Generative AI? A2: The key architectures include GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and Transformer Models.

Q3: What are GPT models? A3: GPT models are generative pre-trained transformers developed by OpenAI, capable of generating human-like text and understanding context.

Q4: What is the difference between fine-tuning and RAG? A4: Fine-tuning involves retraining a model with custom data, while RAG (Retrieval Augmented Generation) uses external data sources to provide additional context for generating responses.

Q5: How can Generative AI be applied in real-world scenarios? A5: Generative AI can be used in code generation (e.g., GitHub Copilot), healthcare diagnostics, customer support automation, financial services, and more.

Q6: What are the principles of responsible AI development? A6: Responsible AI development principles include fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.