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Interactive Audio Lesson
Listen to a student-teacher conversation explaining the topic in a relatable way.
Creating an AI Model
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Today, we're going to explore how to create an AI model. Who can explain what an AI model is?
An AI model is like a computer program that learns from data to make predictions.
Exactly! We can use tools like Teachable Machine. What types of data can we train models with?
Images, sounds, and text!
Great job! Let’s remember 'IST' — Images, Sounds, Text. Now, how do we start creating a model?
We need to collect sample data first, right?
Yes! Collecting sample data is the first step. Can anyone suggest an example project we could do?
We could create an image classifier to recognize happy and sad faces!
That's a wonderful idea! Remember to train and test your model after collecting data. Let’s recap: we’ve learnt what an AI model is, types of data to use, the acronym 'IST', and an example project.
Sustainable Development Goals (SDGs)
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Now, let's discuss how we can identify problems related to the Sustainable Development Goals. What are some issues you think we could address?
Maybe pollution in our area could be one?
Absolutely! Pollution is a major global concern. How could we detail this problem using the 4Ws canvas?
For 'Who', we could say local residents are affected. 'What' is air pollution. 'Where' is the industrial area. And 'Why' is it a concern because it affects health.
Perfect! Now, if we want to explore features relating to air pollution, what data points could we use?
We could look at vehicle counts and air quality index levels!
Great suggestions! Lastly, how do we visualize the data we collect?
We can use graphs like bar or line charts to display our findings.
Exactly! Remember our project structure: identify a problem, use 4Ws for deep analysis, identify features, and visualize data. That’s a lot of knowledge packed into one discussion!
Creating AI-Enabled Solutions
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Now that we’ve identified problems, how do we create AI-enabled solutions? Any suggestions?
What about a mobile app that alerts us about pollution levels?
Excellent idea! How might this app work?
It could use sensors to measure air quality and notify users when pollution levels are too high.
Exactly! And what about another solution related to water? Any thoughts?
We could develop a smart watering system that only waters plants based on the soil moisture level!
Fantastic! We connect technology with sustainability this way. Remember, our focus is creativity along with practicality. Let's summarize: we discussed possible AI-enabled solutions such as a pollution alert app and smart watering systems!
Introduction & Overview
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Quick Overview
Standard
Students learn to apply AI concepts through two major projects; creating an AI model and solving real-world problems aligned with Sustainable Development Goals. By utilizing user-friendly tools, they develop critical thinking, teamwork, and data handling skills in the context of AI and sustainability.
Detailed
In this section, we introduce a series of suggested projects designed for ninth-grade students to apply their knowledge of Artificial Intelligence (AI) through hands-on experiences. Emphasizing the importance of learning by doing, students are encouraged to select projects that stimulate their creativity and address real-world challenges. The projects are organized into two main focal points: creating an AI model using accessible tools such as Teachable Machine and Machine Learning for Kids, and identifying and developing AI solutions for problems related to the Sustainable Development Goals (SDGs). Through these activities, students not only learn the basics of AI, including datasets and the training and testing processes but also engage in data visualization and system mapping to better understand and address global issues like pollution and resource consumption. The chapter concludes by highlighting the benefits of such projects, including practical understanding of AI, enhanced data literacy, teamwork development, and an increased awareness of global challenges.
Audio Book
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Objective of the AI Model Project
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Students will build a basic AI model using user-friendly tools. This project helps students understand the training and testing process, concept of datasets, and how machines learn patterns.
Detailed Explanation
In this project, students are tasked with creating a basic AI model. This is a hands-on activity that allows them to engage with the concepts they have learned in a practical way. The goal is to help them grasp key ideas including how to train an AI model, the significance of testing it, and understanding datasets which are crucial for teaching machines to recognize patterns and make predictions.
Examples & Analogies
Think of it like teaching a child to recognize animals. You show them pictures of different animals (training data) and ask them to identify these animals later (testing). The more examples they learn, like a dog, a cat, or a bird, the better they get at recognizing them on their own.
Tools to Use for AI Creation
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- Teachable Machine https://teachablemachine.withgoogle.com/ A web-based tool by Google that allows anyone to train a model using images, sounds, or poses.
- Machine Learning for Kids https://machinelearningforkids.co.uk/ Designed especially for students to create and train models using text, images, or numbers and use them in Scratch or Python.
Detailed Explanation
To build their AI models, students are provided with two specific tools: Teachable Machine and Machine Learning for Kids. Teachable Machine is a simple, web-based application that enables users to train models based on different types of data, such as images and sounds, making it accessible for everyone. On the other hand, Machine Learning for Kids is tailored for students, allowing them to dive deeper into training models using text, images, or numbers, while also integrating their work with popular programming environments like Scratch or Python.
Examples & Analogies
Imagine you have two different art supplies for creating a masterpiece. Teachable Machine is like using a paintbrush where you can blend colors and shapes easily. Machine Learning for Kids is like a complete art set, providing not just brushes but also colors, markers, and mediums that let you create stylish art on a canvas of your choice.
Steps to Perform in the AI Model Project
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- Choose a type of data (Images/Text/Sound).
- Collect sample data for different classes.
- Train the model using the tool.
- Test and refine the model.
- Present the output and share your learning.
Detailed Explanation
The project consists of several clearly defined steps. First, students need to decide what kind of data they want to work with — this could be images, text, or sounds. Next, they should gather samples related to different categories or 'classes' relevant to their selected data type. Once they have their data, they will proceed to train the AI model using the chosen tools. After training, it’s crucial to test the model's accuracy and make any necessary improvements based on the results. Finally, students will present their project outcomes, sharing what they've created and learned throughout the process.
Examples & Analogies
Think of this process like preparing for a school science fair. First, you decide on a project (choosing the type of data). Then, you gather materials (collecting sample data), build your experiment (training), test it to see if it works (testing and refining), and finally, you present your findings to the class (sharing your results).
Example Ideas for AI Model Projects
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• Image Classifier: Recognize happy and sad faces.
• Sound Classifier: Differentiate claps, whistles, and snaps.
• Text Classifier: Identify positive or negative feedback.
Detailed Explanation
To inspire creativity, several example projects are suggested. An image classifier can be created to identify emotions based on facial expressions, while a sound classifier can distinguish between different types of sounds, like claps versus whistles. Another option is a text classifier, which can analyze text inputs to determine whether they express positive or negative sentiments. These examples guide students on practical applications of AI and illustrate the potential of machine learning.
Examples & Analogies
Consider how social media platforms recognize emotions in photos or comments. The image classifier works like a friend who can tell when you're happy or sad just by looking at your face in a picture, while the text classifier is like having a knowledgeable friend who can sense the mood of your message—whether it’s cheerful or negative—just by reading the words.
Definitions & Key Concepts
Learn essential terms and foundational ideas that form the basis of the topic.
Key Concepts
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Hands-on Learning: Engaging students through practical projects in AI.
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AI Model Creation: Building models using user-friendly tools to understand AI processes.
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Sustainable Development: Aligning projects with global goals to address real-world problems.
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Data Handling: Collecting and visualizing data effectively.
Examples & Real-Life Applications
See how the concepts apply in real-world scenarios to understand their practical implications.
Examples
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Building an image classifier for emotional recognition.
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Creating a mobile app for pollution alerts based on air quality data.
Memory Aids
Use mnemonics, acronyms, or visual cues to help remember key information more easily.
🎵 Rhymes Time
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To learn with AI's might, we train it right, data and tools in sight, solutions take flight!
📖 Fascinating Stories
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Once upon a time, in a town clouded with pollution, kids created an AI model that saved the day by alerting everyone about dirty air. Unity and technology brought hope to their community!
🧠 Other Memory Gems
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Remember 'IPDS' — Identify the problem, Plan the solution, Develop the model, Share the result.
🎯 Super Acronyms
IST
- Images
- Sounds
- Text — the data we can use to build our AI models!
Flash Cards
Review key concepts with flashcards.
Glossary of Terms
Review the Definitions for terms.
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Term: AI Model
Definition:
A program that learns from data to make predictions or decisions.
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Term: Sustainable Development Goals (SDGs)
Definition:
Global goals aimed at addressing various social, environmental, and economic challenges.
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Term: Data Visualization
Definition:
The graphical representation of information and data.
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Term: 4W Canvas
Definition:
A tool used to analyze problems by answering Who, What, Where, and Why.