Unit 4: Introduction to Generative AI – Simplified Notes

1. Real Images vs. AI-Generated Images

  • What it is: This is the difference between a picture taken with a camera and a picture created by a computer’s imagination.
  • Detailed Explanation:
    • A Real Image captures a moment in the real world. When you take a photo with your phone, you are capturing light from actual objects. It’s a record of something that actually existed in front of the camera.
    • An AI-Generated Image is created from scratch by an AI program. The AI has learned what things look like by studying millions of real images. It then uses this knowledge to create a brand new image based on instructions (a prompt). It’s like a digital artist that has never seen the world but has read descriptions of everything in it.
  • Real-Life Example:
    • Real Image: A photo you took of your cat sleeping on a sofa.
    • AI-Generated Image: You type “a cat wearing a superhero cape, flying over a city” into an AI tool, and it creates that picture for you. That cat and that scene never existed in reality.

2. Supervised Learning and Discriminative Modelling

  • What it is: Think of this as an AI learning with a “teacher” to tell the difference between things.
  • Detailed Explanation:
    • Supervised Learning: This is a way to train AI where you give it a lot of data that is already labelled. It’s like showing a child flashcards. You show a picture of an apple and say “This is an apple.” You show a picture of a banana and say “This is a banana.” The “labels” (apple, banana) act as the teacher.
    • Discriminative Modelling: The goal of this type of model is to discriminate or classify. After being trained, its job is to look at something new and decide which category it belongs to. It answers the question, “What is this?“
  • Real-Life Example:
    • A Spam Filter in your email. It has been trained on thousands of emails that were labelled “Spam” or “Not Spam” (Supervised Learning). Now, when a new email arrives, its job is to look at it and decide if it’s spam or not (Discriminative Modelling).

3. What is Generative AI?

  • What it is: A type of AI that can create new and original content, like text, images, music, or videos.
  • Detailed Explanation:
    • While the Discriminative AI (from the point above) is good at classifying things, Generative AI is like a creator or an artist. It doesn’t just identify what something is; it generates something new. It has learned the underlying patterns and structures of data and can produce new examples that follow those same patterns.
  • Real-Life Example:
    • Instead of just identifying a cat in a photo, you can ask a Generative AI to write a poem about a cat or create a picture of a cat that doesn’t exist. ChatGPT writing an essay or Midjourney creating an image are perfect examples.

4. Examples of Generative AI

  • Text Generation: Creating essays, emails, stories, poems, and even computer code. (e.g., ChatGPT, Google Gemini)
  • Image Generation: Creating realistic or artistic images from text descriptions. (e.g., Midjourney, DALL-E)
  • Music Generation: Composing new melodies, beats, and songs in various styles.
  • Video Generation: Creating short video clips from text prompts.
  • Code Generation: Helping programmers by writing lines of code automatically.

5. Generative AI Tools

  • What they are: These are the actual apps and websites you can use to access Generative AI.
  • Examples:
    • For Text: ChatGPT, Google Gemini, Microsoft Copilot. You use them for homework help, writing stories, or summarizing articles.
    • For Images: Midjourney, DALL-E 3, Stable Diffusion. You give them a text prompt, and they create an image.
    • For Presentations: Tome, Canva AI. They can help design slides for your school project.

6. The Potential Negative Impact on Society

  • What it is: The harmful ways Generative AI could be used.
  • Detailed Explanation:
    • Misinformation & Fake News: It’s very easy to create fake images or news articles that look real, which can be used to trick people.
    • Job Displacement: Some jobs, especially those involving repetitive writing or design tasks, might be replaced by AI.
    • Academic Dishonesty: Students might use AI to write their essays, which is a form of cheating and prevents them from learning.
    • Bias: If the AI is trained on biased information from the internet, it can create content that is unfair or stereotypical towards certain groups of people.
  • Real-Life Example:
    • Someone could create a fake picture of a school principal announcing a holiday to cause confusion. This is misinformation.

7. AI or Real Image… How to Identify?

  • What it is: Tips and tricks to spot a fake, AI-generated image.
  • How to check:
    • Look at Hands and Fingers: AI often struggles with hands, creating images with extra or missing fingers.
    • Check the Background: Look for strange, blurry, or nonsensical objects in the background. Things might merge into each other.
    • Unnatural Perfection: Sometimes things look too perfect, like skin without any pores or perfectly symmetrical faces.
    • Weird Text: If there is any text in the image (like on a sign), it’s often misspelled or looks like gibberish.
    • Shadows and Reflections: Check if shadows and reflections make sense. Sometimes they point in the wrong direction or don’t exist at all.

8. Unsupervised Learning and Generative Modelling

  • What it is: Think of this as an AI learning without a teacher to create new things.
  • Detailed Explanation:
    • Unsupervised Learning: In this method, the AI is given a huge amount of data with no labels. It’s like being given a big box of mixed Lego bricks and having to sort them into groups yourself by finding patterns (e.g., grouping by color, size, shape). The AI finds hidden structures and patterns on its own.
    • Generative Modelling: This type of model uses what it learned from those patterns to generate new, similar data. It answers the question, “What does a typical example of this look like?” and then creates one.
  • Real-Life Example:
    • An AI is shown thousands of pictures of different flowers (without being told they are flowers). It starts to learn the common patterns—petals, a center, a stem, etc. (Unsupervised Learning). Then, it can use this understanding to generate a picture of a completely new, unique flower that has never existed but still looks like a flower (Generative Modelling).

9. Types of Generative AI

This refers to the different ways Generative AI models work or what they produce.

  • Text-to-Text: You give it text, and it gives you text back. (Example: Asking ChatGPT a question).
  • Text-to-Image: You give it a text description, and it creates an image. (Example: Midjourney).
  • Text-to-Video: You give it text, and it creates a short video clip.
  • Text-to-Audio: You give it text, and it can generate speech (text-to-speech) or even music.

10. Generative AI: Boon or Bane?

  • What it means: Is Generative AI a boon (a gift/blessing) or a bane (a curse/problem) for humanity? The answer is that it’s both; it depends on how we use it.
  • As a BOON (The Good Side):
    • Creativity: Helps artists, writers, and musicians create new things faster.
    • Learning: Can explain complex topics in simple ways, acting as a personal tutor.
    • Efficiency: Automates boring tasks, saving time for more important work.
    • Problem-Solving: Helps scientists discover new medicines or materials.
  • As a BANE (The Bad Side):
    • Misinformation: Spreads fake news and propaganda easily.
    • Cheating: Can be used to cheat in exams and assignments.
    • Job Loss: May replace human jobs.
    • Deepfakes: Can be used to create fake videos to bully or defame people.
  • Conclusion: Like fire, Generative AI is a powerful tool. You can use fire to cook food and stay warm (boon), or you can use it to burn down a house (bane). The responsibility lies with the user.

11. Ethical Considerations of using Generative AI

  • What it is: The “moral questions” or the debate about what is right and wrong when using this technology.
  • Key Questions to Consider:
    • Ownership & Copyright: If an AI creates a piece of art, who owns it? The person who wrote the prompt, the company that made the AI, or no one?
    • Consent: Is it fair to train an AI on an artist’s work without their permission, allowing it to copy their style? Is it right to use someone’s voice or face to create a deepfake without their consent?
    • Bias and Fairness: Is the AI producing content that is fair to all people, regardless of their race, gender, or background?
    • Truth and Deception: When should we be required to tell people that a video or image is AI-generated? Is it okay to trick people with AI?

12. Responsible Use of Generative AI

  • What it is: A set of rules or guidelines on how to use AI in a good, fair, and honest way.
  • How to be a Responsible User:
    • Be Honest: Never claim AI-generated work is your own for school assignments. Use it as a tool for brainstorming or research, but write the final work yourself.
    • Fact-Check Everything: AI models can make mistakes (called “hallucinations”). Always verify important information from reliable sources.
    • Don’t Create Harmful Content: Never use AI to create fake images, videos, or text to bully, embarrass, or spread lies about others.
    • Give Credit: If you use an AI tool to help you create something, it’s good practice to mention it.
    • Think Critically: Don’t just accept the AI’s first answer. Ask follow-up questions, and always think for yourself.

Discover more from EduGrown School

Subscribe to get the latest posts sent to your email.