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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 with Teachable Machine. This tool lets you train your computer to recognize images, sounds, or poses. Can anyone tell me what they think machine learning is?
Is it when computers learn from data?
Absolutely! In fact, think of it like teaching a dog to respond to commands. You show it what to do, and eventually, it learns how to respond. Now, can anyone remind me of the key steps in training a machine?
Collecting data, training the model, and then testing it!
Correct! Remember the acronym 'C-T-T' for Collect, Train, Test. That's how we'll approach our projects today. Let's move on to our first task!
Now that we understand the steps, let’s talk about data collection. Why do you think choosing the right data is crucial?
If we don't have good data, the model might not learn correctly!
Exactly! Quality data leads to better accuracy. When you select data for your model, consider its variety. Can anyone name types of data we can use?
Images, sounds, and text!
Great job! Let’s ensure we gather diverse samples for our projects. A tip—always label your data clearly! What strategies can we use to collect it?
We could take pictures ourselves or use online resources.
Good points! Using varied sources will enrich your dataset.
Next, let’s discuss the training and testing phase of your models. Why is testing important after training?
To see if the model actually learned what we wanted it to!
Exactly! Testing reveals how well our model performs. It's like taking a quiz after studying. After testing, we may need to refine our models based on their performance. What might that involve?
Adjusting the data or retraining with different samples?
Correct! Always analyze your results before sharing your work. Finally, how can we share our findings in an engaging way?
We can create a presentation or a video!
Absolutely! Communication is key to sharing our AI projects effectively.
Now let’s tie our projects to something critical—Sustainable Development Goals. Why do you think it’s important to relate our AI models to these goals?
To help solve real-world problems?
Exactly! This makes our work more meaningful. What are some local problems we could tackle?
Pollution or water wastage!
Great suggestions! Let's take a moment to create a 4Ws Problem Canvas for one of these issues. Who would like to start?
I can! For pollution, it's affecting local residents.
Perfect! You’re summarizing the 'who' part. These canvases will help us frame our AI projects more effectively.
To build AI, just don't shy, collect, train, test, and then apply!
A young student named Alex collected pictures of plants for a school project. By training their AI model, Alex learned how it can distinguish between healthy and unhealthy plants, helping their community garden thrive!
Remember 'C-T-T' to gather data, train your AI, then put it to the test!
C-T-T - Collect data, Train the model, Test the results.
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Teachable Machine
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Machine Learning
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Data Collection
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Sustainable Development Goals (SDGs)
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A web-based tool by Google that allows users to create machine learning models using images, sounds, and poses.
A subset of AI that enables machines to learn from data and improve their performance over time without explicit programming.
A collection of 17 global goals set by the United Nations to address pressing social, economic, and environmental challenges.
The process of gathering and measuring information on variables of interest to understand a particular phenomenon.
The process of evaluating a trained machine learning model's performance by applying it to unseen data and assessing its predictions.
The Teachable Machine project empowers students to create simple AI models using various data types while fostering skills in data collection and problem-solving related to Sustainable Development Goals.
In this section, students will utilize Teachable Machine and similar platforms to build basic AI models. They will learn the process of data training and testing, explore real-world problems linked to Sustainable Development Goals, and apply design thinking to devise creative AI solutions.
This section focuses on using Teachable Machine, a user-friendly tool developed by Google, to build AI models. Students engage hands-on with the concepts of machine learning by choosing data types—images, sounds, or text—and then go through the steps of collecting data, training their models, testing them, and presenting their outputs. Additionally, the section emphasizes problem-solving in the context of Sustainable Development Goals (SDGs), encouraging students to identify local issues such as pollution or energy consumption and to develop AI-based solutions. The overall aim is to integrate creativity and technology, providing students with practical AI experience while enhancing their understanding of global challenges.