Course Structure: This course we have divided into 4 modules
Module 1: Understanding Generative AI
In this module, we will cover the following topics:
- Introduction to Generative AI
- Understanding the fundamentals of Generative AI
- How it differs from traditional AI models
- Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning
- Overview of AI, ML, and Deep Learning
- Key differences and their real-world applications
- Exploring ChatGPT: Features and Capabilities
- Understanding ChatGPT’s functionality
- Key features, limitations, and practical use cases
1- Generative AI = Generative + AI
Generative AI combines Generative (creating new content) and AI (Artificial Intelligence, enabling machines to learn and make decisions). It uses machine learning models to generate text, images, music, and more, mimicking human creativity.
- AI is not a new concept; it has been around for a long time.
- AI is widely used in various fields, including:
- 📍 Google: Distance estimation & navigation.
- 🚗 Tesla: Self-driving car technology.
- 📱 Advertising: Personalized mobile ads.
Generative AI Model
- Trained on vast amounts of data
- Capable of generating new content based on learned patterns.
Examples of AI Models
- 📝 ChatGPT – Generates text-based responses in real-time without retrieving data from a fixed source.
- 🎨 DALL·E – Creates unique images on demand using generative AI.
- 💻 GitHub Copilot – An AI-powered coding assistant that helps developers write code faster and more efficiently.
- 🎙️ Whisper – OpenAI’s automatic speech recognition (ASR) model for transcribing audio into text.
Before We Dive Deeper, Let’s Understand Some Core Concepts:
1️⃣ Artificial Intelligence (AI)
2️⃣ Machine Learning (ML)
3️⃣ Deep Learning (DL)
Let’s explore each one step by step! 🚀
1 – Artificial Intelligence (AI) 🤖
- Humans are the smartest species on Earth.
- AI replicates human intelligence in machines, making them capable of reasoning and creativity.
- A broad field of computer science, AI can:
- 🏥 Predict diseases based on symptoms.
- 💳 Detect fraud in financial transactions.
- But how does AI achieve these capabilities?
👉 This is where Machine Learning (ML) comes into play!
2 – Machine Learning (ML) 🤖📚
- As the name suggests, Machine Learning is about teaching machines to learn on their own.
- The goal is to make machines smart enough to:
- 🔍 Predict outcomes based on patterns.
- 🧠 Make decisions without explicit programming.
Prerequisites for Machine Learning
1️⃣ High-quality data – Machines must be trained with large volumes of meaningful data, not random or inaccurate information.
2️⃣ Significant computational power – ML requires powerful hardware to process vast datasets.
3️⃣ Strong algorithms – Well-designed models help machines learn and improve over time.
🚀 With these elements, machines can learn and evolve just like humans!
3 – Deep Learning (DL) 🧠💡
- Deep Learning is a specialized branch of Machine Learning (ML) that uses artificial neural networks with multiple layers.
- It can analyze patterns in data and generate new content such as:
- 🖼️ Realistic images
- 📝 Text generation
- 🎵 Music composition
- 🎥 Video creation
How Deep Learning Works?
- Input Layer – Receives raw data.
- Hidden Layers – Perform complex computations to extract meaningful patterns.
- Output Layer – Produces the final result or prediction.
🔧 Neural networks require high computational power to process large datasets, recognize patterns, and optimize model parameters through iterative learning.
📈 This enhances accuracy and helps the model generalize to unseen data.
Relation Between AI, ML & Deep Learning
🤖 Artificial Intelligence (AI) (Broadest Field)
- AI focuses on creating machines that mimic human intelligence for tasks like problem-solving, decision-making, and language understanding.
- Example: Siri, self-driving cars 🚗
📊 Machine Learning (ML) (Subset of AI)
- ML allows computers to learn from data and improve their performance without direct programming.
- Example: Spam filters in emails 📩, Netflix recommendations 🎬
🧠 Deep Learning (DL) (Subset of ML)
- DL uses multi-layered neural networks to analyze large datasets and recognize complex patterns.
- Example: Facebook’s image recognition 🏷️, Google Assistant’s speech recognition 🎙️
🚀 Deep Learning is the driving force behind Generative AI!
Due to deep research in AI, Machine Learning (ML), and Deep Learning, advanced models like ChatGPT have been invented.
- In the current era, ChatGPT is one of the hottest topics in the technology field.
Formal ChatGPT Definition-
ChatGPT is a language model developed by OpenAI. It is based on Generative Pre-trained Transformer (GPT) technology, which allows it to understand and generate human-like natural text responses.
How Does ChatGPT Work?
- Pre-training:
- The model is trained on a vast dataset containing text from books, articles, and websites.
- It learns grammar, facts, and general world knowledge.
- Fine-tuning:
- The model is refined using human feedback (RLHF – Reinforcement Learning from Human Feedback) to improve accuracy and reduce biases.
- Generating Responses:
- When a user inputs a question or prompt, ChatGPT processes it and generates a response based on its training data.
- It does not think or have emotions, but it predicts the most relevant text based on patterns.
Key Features of ChatGPT
- Conversational Abilities: Can hold natural and meaningful conversations.
- Context Understanding: Remembers previous messages in a conversation to give relevant responses.
- Multifunctional: Can be used for answering questions, writing content, coding, and more.
- Customizable: Businesses can fine-tune ChatGPT for specific applications like customer support.
Applications of ChatGPT
- Customer Support: Automating responses for businesses.
- Content Generation: Writing blogs, emails, and reports.
- Education: Assisting students with learning and tutoring.
- Programming Help: Debugging code and writing scripts.
Limitations of ChatGPT
- Not Always Accurate: This can generate incorrect or outdated information.
- Lack of Real Understanding: It does not “think” but predicts text patterns.
- Bias in Responses: This may reflect biases present in its training data.
Module 2: Overview of Key Terminology
In this module, we will cover essential concepts related to AI and ChatGPT:
LLM (Large Language Model) – Definition & Explanation
LLM refers to an advanced artificial intelligence (AI) model trained on massive amounts of text data to understand, generate, and process human-like language.
It is common to assume that Generative AI and LLM (Large Language Model) are the same, but they have key differences. Below is a summarized comparison:
Generative AI vs. LLM
| Aspect | Generative AI | LLM (Large Language Model) |
|---|---|---|
| Definition | A broad AI category that generates various types of content (text, images, videos, music). | A specialized AI model focused on understanding and generating human-like text. |
| Function | Can create content in multiple formats beyond text. | Processes and generates text-based content only. |
| Technology | Uses GANs, VAEs, Diffusion Models, and Transformers. | Based primarily on Transformer architecture. |
| Examples | DALL·E (images), Jukebox (music), ChatGPT (text). | GPT-4, BERT, LLaMA, PaLM. |
| Output Type | Text, images, audio, video, 3D models, etc. | Only text-based outputs. |
| Scope | Broader – includes multiple AI subfields. | A subset of Generative AI, focused on NLP (Natural Language Processing). |
| Applications | AI art, music generation, video synthesis, and chatbots. | Chatbots, content writing, summarization, and translation. |
In short, LLMs are a subset of Generative AI—all LLMs are Generative AI, but not all Generative AI models are LLMs.
Here is a simplified version of the visual representation showing how a large language model (LLM)
In the simplified version of the LLM workflow, here’s how the process is broken down:
- Input Text:
The process starts with the input text. For example, the sentence “The dog runs” is fed into the system. This is the starting point where the model begins processing language. - Tokenization:
The input text is then tokenized. This means the model breaks down the sentence into smaller chunks (tokens), often at the word or subword level. For example, “The dog runs” could be broken into [“The”, “dog”, “runs”]. Tokenization is important because LLMs don’t work directly with raw text; they process tokens (numbers representing pieces of words or words themselves). - Passing Through Transformer Layers:
These tokens are passed through transformer layers, which are the core architecture of LLMs. The transformer consists of multiple layers of attention mechanisms.- Self-Attention: This is where the model looks at each token in the context of the other tokens in the sentence. For instance, it considers how “dog” and “runs” are related and adjusts its focus accordingly.
- The transformer helps the model understand the relationships and context between tokens.
- Output Generation:
Once the model has processed the tokens through the transformer layers, it generates the output word or token. In this case, the output might be a word like “fast”, indicating the next logical word that fits with the input sentence. The model uses its learned language knowledge to predict the most likely word in this context. - Iteration for Further Prediction:
This process is repeated for every token until the full sentence or desired output is generated. Each token output becomes part of the input for the next prediction step, helping the model generate coherent and contextually appropriate text.
LLMs have a wide range of applications, including:
- Conversational AI (e.g., chatbots, virtual assistants)
- Content generation (e.g., writing articles, code, poetry)
- Text summarization and translation
- Sentiment analysis
- Question answering
- Code generation (e.g., for programming)
Here is a list of some of the most notable Large Language Models (LLMs) that have been developed over the years:
- GPT Series (OpenAI)
- BERT (Google)
- T5 (Text-to-Text Transfer Transformer) – Google
- XLNet (Google/CMU)
- ALBERT (Google)
and many more.