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Kickstart Your Journey with SAP Artificial Intelligence: Part 4 Fundamentals of Generative AI

Kickstart Your Journey with SAP Artificial Intelligence: Part 4 Fundamentals of Generative AI

Introduction

Now that we’ve covered Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning, it’s time to explore an even more fascinating area—Generative AI. 

This 7 part blog series is designed for anyone who wants to start learning about SAP AI. No matter what your job is or what you do, understanding the basics of Generative AI how it connects to SAP is a smart choice.

In this blog series, we’ll break down SAP AI into easy-to-understand parts, covering one step at a time.

SAP AI step 4 of 7

Part 1 — Introduction to AI 

Part 2 — What is Machine Learning?

Part 3 — Basics of Deep Learning

Part 4 — Getting Started with Generative AI [Current Blog]

Part 5 — What Are Large Language Models (LLMs)?

Part 6 — Prompt Engineering: How to Communicate with AI

Part 7 — Introduction to SAP AI

What is Generative AI?

Generative AI is a form of artificial intelligence designed to create new content, such as artwork, music, or realistic images, without explicit instructions on what to produce.

Unlike traditional AI, which is task-oriented and problem-solving, Generative AI stands out for its ability to mimic human-like creativity.

It can generate original and unique content, ideas, or solutions, much like humans do.

Let’s Break it Down with a New Example

Imagine I asked you to design a vehicle that doesn’t exist. You’d need to use your imagination to invent something entirely new, something no one has ever seen before.

Thanks to our natural creativity, you might come up with a vehicle that has the body of a spaceship, the wheels of a monster truck, and the wings of a bird. It’s completely original, something you dreamed up from scratch.

Now, imagine a computer program doing something similar. Instead of just following a set of instructions, it can generate new things—like artwork, music, or even realistic images—without needing exact directions. It’s as if the program has its own creative spark, just like you.

Imagine a computer program has been trained by looking at countless aerial photos of eagles flying over mountains. With this knowledge, it can now generate a completely new image—not by copying any of the pictures it has seen, but by combining its understanding of what makes an eagle and a mountain unique. The result could be an entirely new perspective, like an eagle soaring high above rugged peaks, blending elements from what it learned to create something fresh and original.

Eagle Flying Over Mountains Generate by AI
Eagle Flying Over Mountains Generate by AI

This is Generative AI — a machine that can imagine and create on its own. It can draw pictures, tell stories, or even invent new games, all without needing anyone to show it how. It’s like giving a computer the ability to dream up new ideas, just like we do.

Where Does Generative AI Fit into the AI Hierarchy?

Generative AI is a branch of Deep Learning. It sits within a larger framework of artificial intelligence. Imagine AI as the broadest category, with Machine Learning being a more specific part of it. Within Machine Learning, we have Deep Learning, and finally, Generative AI is a subset of Deep Learning. The diagram below helps visualize how AI, Machine Learning, Deep Learning, and Generative AI are related.

Artificial-Intelligence-Hierarchy

Generative AI builds on machine learning techniques, particularly deep learning and neural networks.

What makes Generative AI stand out is its ability to create brand-new content.

Traditional AI, machine learning, and even deep learning have largely focused on predictive models—used to recognize, classify, or forecast patterns in data. For example, a typical machine learning task might be identifying a car in a set of images or grouping different vehicles based on their features.

Generative AI goes further. Instead of just identifying a car, it can create an entirely new design of a futuristic vehicle or imagine and generate something that’s never been seen before. This creative capability was once thought to be unique to humans.

The image below showcases AI’s evolution over time. The leap from rule-based systems to Generative AI has been powered by advances in learning algorithms, greater computational power, and access to vast amounts of data.

Evolution of AI with Time

What are Generative Models?

Generative Models are the heart of Generative AI. These machine learning models learn the patterns and structures of existing data, then use that knowledge to generate new, original content.

Applications:

  • Visual Arts: Creating stunning images and art pieces.
  • Music Composition: Generating unique musical compositions.
  • Text Generation: Writing articles, stories, and scripts.

Common Generative Models:

  • Variational Autoencoders (VAEs): Known for their versatility.
  • Generative Adversarial Networks (GANs): Creating highly realistic content.
  • Restricted Boltzmann Machines (RBMs): Often used for collaborative filtering.
  • Transformer-based Language Models: Excelling in natural language tasks.

We’ll explore these models in detail and see how they’re revolutionizing various industries.

Usages of Generative AI in Real-life

Here are some examples of how Generative AI is being used to create real-life applications:

Generative AI Use Cases

Software:

  • Predictive Maintenance: Using AI to predict when equipment will fail, reducing downtime and costs.
  • Virtual Assistants: AI-powered assistants that can handle customer inquiries and tasks, improving efficiency.

Content Creation:

  • Creative Writing: AI can generate creative text, such as poems, scripts, or marketing copy.
  • Art Generation: AI can create original artwork in various styles, from abstract to realistic.

NLP(Natural Language Processing):

  • Language Translation: AI can translate text and speech between different languages, facilitating global communication.
  • Sentiment Analysis: AI can analyze text to determine the sentiment expressed (e.g., positive, negative, neutral), providing valuable insights for businesses.

Here are Some Popular Generative AI Tools

There are plenty of tools available today in the market, here are the popular ones you can try.

  1. ChatGPT: Developed by OpenAI, ChatGPT engages in natural language conversations, generating contextually relevant and coherent responses using deep learning techniques.

  2. AlphaCode: Created by DeepMind, AlphaCode generates optimized code solutions across various programming languages, leveraging deep learning to analyze and learn from code patterns.

  3. GitHub Copilot: An AI-powered code completion tool by GitHub and OpenAI, integrated into code editors to provide real-time suggestions and completions.

  4. Gemini: Google’s conversational AI chatbot, initially based on LaMDA and later upgraded to models like PaLM and Gemini.

  5. Microsoft Copilot: An AI-powered assistant by Microsoft for tasks like summarizing articles, reformatting text, and updating images.

  6. DALL-E: Developed by OpenAI, DALL-E generates images from text descriptions, with versions including DALL-E2 and DALL-E3.

  7. StyleGAN: Created by NVIDIA, StyleGAN generates high-quality synthetic images, especially realistic human faces, with control over visual features.

An image generated using StyleGAN that looks like a portrait of a young woman. Generated by an artificial neural network based on an analysis of a large number of photographs.
An image generated using StyleGAN that looks like a portrait of a young woman.

Summary

In this blog, we explored what generative AI is and how it differs from other types of AI. We also discussed several real-life applications of generative AI and highlighted some popular tools in this field.

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