Contents of this E-Book

• AI systems and tools
• The AI layers
  1. Supervised learning
  2. Unsupervised learning
  3. Semi-supervised learning
• Deep learning
• Here's how ANNs work:
• Introduction to generative AI
• Generative AI: Some fascinating metrics
• How generative AI works
• ML model vs. gen AI model
• Journey from traditional programming to neural networks to generative AI
• Large Language Models: Powering generative AI
• How do LLMs work?
• Different Large Language Models
• Components of an LLM
• How do LLMs learn?
• Building an LLM application
• LLMs use cases
  • Content creation
  • Education
  • Customer service and support
  • Research and development.
  • Entertainment and media
• Limitations of LLMs
• Vector databases
• Vector Embeddings
• How vector databases work
• Traditional databases vs. vector databases
• The vector database landscape
• How vector databases search and retrieve data
• Some notable generative AI frameworks + tools
• LangChain
• How does LangChain work?
• LlamaIndex
• How LlamaIndex works
• Llama 2
  1. Model Hub:
  2. Datasets:
  3. Model Training & Fine-tuning Tools:
  4. Application Building:
  5. Community & Collaboration:
• The Rise of Small Language Models
  • Size:
  • Focus:
• Limitations:
• Prompt Engineering
• Generative AI Developer Stack
• Using Generative AI Responsibly
• Best Practices for Responsible Generative AI Use:
  • Data:
  • Development and Deployment:
  • Content and User Interaction:
  • Societal Impact and Governance:
• Deploying GenAI Applications on Kubernetes: A Step-By-Step Guide!
• Why Deploy GenAI Applications On Kubernetes?
• Let's Build a Full-Stack AI Application in React
• What is the Elegance SDK?
  • Key features
• SingleStore for Generative AI Applications
• Using SingleStoreDB as a full-context vector database

Welcome to the exciting world of generative Artificial Intelligence (AI), a frontier in technology that is not just transforming how we interact with machines, but also reshaping the very fabric of creativity, design and data synthesis. This eBook is your doorway to understanding the fundamentals, advancements and immense potential of this groundbreaking field.

"Generative AI: A Beginner's Guide" is designed to be accessible yet comprehensive, providing you with a clear understanding of the concepts, applications and implications of generative AI. Whether you're a student, professional or just a curious mind, this guide aims to equip you with the knowledge and insight to appreciate and engage with one of the most exciting technological advancements of our time.

AI systems and tools

Believe it or not, most of us have been using AI systems and tools for a very long time in our homes and day-to-day lives. Yes, we are talking about Alexa and Siri, two of the most popular AI assistants on the market. They are both capable of a wide range of tasks including setting alarms, playing music, making calls and controlling smart home devices.

Alexa is a virtual assistant developed by Amazon. It was first released in 2014, and is now available on a wide range of devices including the Amazon Echo, Echo Show and Echo Dot. Alexa can be used to control smart home devices like lights, thermostats and locks. It can also be prompted to play music, get news and weather updates, and set alarms.

It’s Apple counterpart, Siri,is a virtual assistant developed by Apple and first released in 2011. Siriis available on a wide range of Apple devices including the iPhone, iPad, Apple Watch and HomePod. It can be used to make calls, send texts, set alarms, get directions and more — and like Alexa, can also be used to control smart home devices.

Both Alexa and Siri are constantly updated with new features and capabilities., continuing to make them powerful tools that make our lives easier and more convenient.

While Alexa and Siri were great advancements in their time and today can answer questions and follow basic commands, generative AI excels at an entirely new pace — particularly in creating fresh content. Think writing poems, composing music or even generating code. Instead of relying on pre-programmed responses, generative AI leverages its understanding of language to produce truly original outputs, making it a powerful tool for creative exploration and innovation.

The AI layers

Machine learning (ML)
ML is a type of AI that allows software applications to become more accurate in predicting outcomes without being explicitly programmed to do so. ML algorithms use historical data as input to predict new output values.

Deep learning (DL)
DL is a type of ML that uses artificial neural networks to learn from data. Neural networks are inspired by the structure and function of the human brain, made up of layers of interconnected nodes — each of which performs a simple mathematical operation.

Generative AI
Generative AI is a type of AI that can create new content including text, code, images and music. Generative AI models are trained on large datasets of existing content, learning to identify patterns in data and using those patterns to generate new content.

Large Language Models (LLMs)
LLMs are a type of generative AI model trained on massive datasets of text and code. LLMs can generate text, translate languages, write different kinds of creative content and answer your questions in an informative way.

Generative Pre-trained Transformers (GPTs)
GPTs are a type of LLM that use a transformer architecture. Transformers are a neural network architecture well-suited for natural language processing tasks.

GPT-4 and ChatGPT
GPT-4 and ChatGPT are two examples of GPT models. GPT-4 is an LLM developed by OpenAI, while ChatGPT is an LLM (also developed by OpenAI) that is specifically designed for chatbot applications.

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