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A short class with Sarah and her students. The other ways are under More.
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Welcome, everyone! Today, we’re diving into the world of AI models. Can anyone tell me what an AI model is?
Isn’t it like a computer program that learns from data?
Exactly right! AI models learn patterns from datasets. We will use tools like Teachable Machine. Remember, 'T-M' stands for Training Model.
How do we start building one?
First, you'll choose data. We can use images, sounds, or texts. Let’s list potential examples of each!
We could use images of animals for classification!
Great idea! Let's remember: 'I-S-C,' Images-Sounds-Classification. Now, let’s recap: what’s our first step?
Choosing the type of data!
Very good! Keep that in mind as we move on.
In our next part, let’s discuss SDGs. Who can name a few?
Ending poverty and protecting the planet!
Correct! SDGs are crucial as they guide our AI projects. For instance, how can AI help with pollution?
We could create an app for pollution alerts!
That's a fantastic idea! Always remember, addressing SDGs is a big part of our learning. 'P-A,' Pollution-Application. Let’s aim to create solutions that matter.
What steps do we take to identify a problem?
We’ll use the 4Ws canvas. 'W-W-W-W,' Who, What, Where, Why. Can you identify those for a problem related to SDGs?
Yes! For pollution, the 'Who' is local communities.
Excellent! Let’s keep practicing this method.
Now let’s talk about data collection. How do we collect and visualize data?
Using spreadsheets like Google Sheets!
Exactly! Remember the saying, 'D-V,' Data-Visualization. Can someone tell me how we can visualize collected data?
Graphs and charts could help!
Yes! We can use bar, pie, or line graphs. Let’s summarize: Why is visualizing data important?
It helps us understand patterns and make decisions!
Perfect! Always keep your charts clear and informative.
Finally, let's brainstorm solutions. What AI-enabled solution can we think of for the pollution problem?
How about an app that tracks pollution levels in real-time?
Great suggestion! We’re merging technology with a real need. Keep in mind: 'T-P,' Technology-Problem. How can we prototype this solution?
We could use scenarios to demonstrate how users interact with it.
Exactly! This creative thinking drives effective AI applications. Remember, your prototypes should address real needs.
To build an AI model, choose the data with care, collect and train it, see what’s there!
Once there was a data detective who solved pollution puzzles. With AI, he learned to collect and organize data to create solutions.
Remember the acronym 'W-W-W-W' for 4Ws - Who, What, Where, Why.
T-P for Technology-Problem helps remember to combine AI with real-world challenges.
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What is an AI Model?
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What are the Sustainable Development Goals?
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A computer program designed to recognize patterns in data.
A collection of data points used for training AI models.
Global objectives aimed at addressing social, economic, and environmental issues.
A tool used to analyze problems by answering Who, What, Where, and Why.
This section outlines the objectives of engaging students in practical AI projects that relate to real-world problems, particularly focusing on Sustainable Development Goals.
This section highlights the significance of hands-on AI projects for students by emphasizing the learning objectives such as constructing AI models, identifying real-world issues related to Sustainable Development Goals, and encouraging creativity and problem-solving through practical application of AI concepts.
The objective section focuses on providing students with practical experiences to apply their AI knowledge. The projects suggested apply concepts learned in previous lessons and engage students in creating AI models to solve real-world issues. The projects are designed to develop creativity, critical thinking, and problem-solving skills, especially in the context of Sustainable Development Goals (SDGs). Students are encouraged to explore various AI tools such as Teachable Machine and Machine Learning for Kids while understanding the processes of training and testing AI models. The projects not only aim to explain how AI can help address global challenges but also to facilitate a deeper understanding of data handling and design thinking in developing solutions.