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Learn it your way
A short class with Sarah and her students. The other ways are under More.
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Today, we're diving deep into creating our own AI models. Can anyone tell me why it's important to learn how to build models?
Is it because we can teach machines to learn from data?
Exactly! By training models, we understand the training and testing process, learning patterns from datasets. Remember the acronym 'TTP' which stands for Training, Testing, and Patterns!
What tools can we use for this?
Great question! We can use tools like Teachable Machine or Machine Learning for Kids that are designed for our level. What do you think we can create with these tools?
How about making an image classifier for pets?
That’s an excellent idea! Remember to collect your sample data and document your findings. Let’s summarize: understand TTP—Training, Taking data, and Finding Patterns!
Now, let’s shift gears and discuss our next project which focuses on Sustainable Development Goals, or SDGs. Can anyone name some SDGs?
I know about reducing poverty and ensuring clean water!
And also climate action!
Perfect! These SDGs give us a framework for identifying problems. Let's think about how we can use AI to solve issues like pollution. Has anyone thought about using the 4Ws Problem Canvas?
What is the 4Ws canvas exactly?
The 4Ws canvas helps clarify our problem by answering Who, What, Where, and Why. For example, if we take air pollution, Who is affected? What is the problem? Where is it occurring? And Why does it concern us? Remember this framework as W^4!
That makes it easier to understand!
Exactly! Alright, let’s summarize: when approaching SDGs, use the 4Ws canvas—Who, What, Where, Why—our W^4 tool!
Next, let's discuss data collection and visualization. Why is this step crucial in our projects?
It’s important to gather accurate data to support our AI models!
Absolutely! We can use spreadsheets to record our data. What types of visualizations can help us understand our findings?
Bar charts and pie charts help to see the differences clearly!
Yes, and we can use line graphs to show trends over time! Remember the mnemonic 'DRA'—Data, Representation, Analysis —to keep track of the data management process.
What if we spot a correlation?
Great question! Finding patterns means we can better tailor our AI solution. To summarize, think of 'DRA'—Data, Representation, Analysis—when handling your project data!
When AI is smart and wise, It learns from data, no surprise!
Imagine a young inventor named Ava; she used a tool to teach her robot how to recognize plants by looking at pictures. This helped her save many trees. Ava learned the magic of AI!
Remember 'TTP' for Training, Testing, Patterns while creating AI models!
Use 'W^4' to remember Who, What, Where, Why in problem analysis.
Term
What does AI stand for?
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Term
What can the 4Ws canvas help us understand?
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A computational model trained to perform a specific task using machine learning techniques.
A collection of data used for training and testing AI models.
A collection of global goals set by the United Nations to address social, environmental, and economic challenges.
A problem-solving framework that focuses on who, what, where, and why related to a specific issue.
The objective section outlines the importance of applying AI knowledge through practical projects to address real-world challenges, particularly related to Sustainable Development Goals (SDGs).
This section emphasizes the necessity of hands-on learning in Artificial Intelligence, guiding students to create AI models and develop solutions for real-world issues linked to Sustainable Development Goals (SDGs). It outlines specific projects and methodologies to engage students actively in understanding AI applications.
The objective section of Chapter 22 focuses on the importance of experiential learning in the field of Artificial Intelligence (AI). It emphasizes that true understanding is achieved when students engage in practical projects rather than merely theoretical learning. The activities suggested within this chapter offer students opportunities to leverage AI in solving significant global challenges aligned with Sustainable Development Goals (SDGs).
AI Model Creation: Students will create a basic AI model using user-friendly tools to learn about datasets, the training/testing process, and how machines learn patterns.
Sustainable Development Problem Solving: The second project encourages students to identify real-world problems connected to SDGs and devise AI-supported solutions using design thinking and data handling techniques.
Field Visits: A suggested field visit aims at observing AI's real-world applications, with options for observing AI in various industries, thereby extending learning beyond the classroom.
Portfolio Creation: Throughout their learning journey, students are encouraged to maintain a portfolio of their activities, reflecting their understanding and growth in AI applications.
Benefits of Projects: The projects help build practical AI understanding, enhance data literacy and design thinking skills, and foster awareness of global challenges, integrating creativity, technology, and purpose.